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Qi R, Fu R, Lei X, He J, Jiang Y, Zhang L, Wu Y, Wang S, Guo X, Chen F, Nie M, Yang M, Chen Y, Zeng J, Xu J, Xiong H, Fang M, Que Y, Yao Y, Wang Y, Cao J, Ye H, Zhang Y, Zheng Z, Cheng T, Zhang J, Lin X, Yuan Q, Zhang T, Xia N. Therapeutic vaccine-induced plasma cell differentiation is defective in the presence of persistently high HBsAg levels. J Hepatol 2024; 80:714-729. [PMID: 38336348 DOI: 10.1016/j.jhep.2023.12.032] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/04/2023] [Revised: 12/15/2023] [Accepted: 12/29/2023] [Indexed: 02/12/2024]
Abstract
BACKGROUND & AIMS Mechanisms behind the impaired response of antigen-specific B cells to therapeutic vaccination in chronic hepatitis B virus (HBV) infection remain unclear. The development of vaccines or strategies to overcome this obstacle is vital for advancing the management of chronic hepatitis B. METHODS A mouse model, denominated as E6F6-B, was engineered to feature a knock-in of a B-cell receptor (BCR) that specifically recognizes HBsAg. This model served as a valuable tool for investigating the temporal and spatial dynamics of humoral responses following therapeutic vaccination under continuous antigen exposure. Using a suite of immunological techniques, we elucidated the differentiation trajectory of HBsAg-specific B cells post-therapeutic vaccination in HBV carrier mice. RESULTS Utilizing the E6F6-B transfer model, we observed a marked decline in antibody-secreting cells 2 weeks after vaccination. A dysfunctional and atypical pre-plasma cell population (BLIMP-1+ IRF4+ CD40- CD138- BCMA-) emerged, manifested by sustained BCR signaling. By deploying an antibody to purge persistent HBsAg, we effectively prompted the therapeutic vaccine to provoke conventional plasma cell differentiation. This resulted in an enhanced anti-HBs antibody response and facilitated HBsAg clearance. CONCLUSIONS Sustained high levels of HBsAg limit the ability of therapeutic hepatitis B vaccines to induce the canonical plasma cell differentiation necessary for anti-HBs antibody production. Employing a strategy combining antibodies with vaccines can surmount this altered humoral response associated with atypical pre-plasma cells, leading to improved therapeutic efficacy in HBV carrier mice. IMPACT AND IMPLICATIONS Therapeutic vaccines aimed at combatting HBV encounter suboptimal humoral responses in clinical settings, and the mechanisms impeding their effectiveness have remained obscure. Our research, utilizing the innovative E6F6-B mouse transfer model, reveals that the persistence of HBsAg can lead to the emergence of an atypical pre-plasma cell population, which proves to be relevant to the potency of therapeutic HBV vaccines. Targeting the aberrant differentiation process of these atypical pre-plasma cells stands out as a critical strategy to amplify the humoral response elicited by HBV therapeutic vaccines in carrier mouse models. This discovery suggests a compelling avenue for further study in the context of human chronic hepatitis B. Encouragingly, our findings indicate that synergistic therapy combining HBV-specific antibodies with vaccines offers a promising approach that could significantly advance the pursuit of a functional cure for HBV.
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Affiliation(s)
- Ruoyao Qi
- State Key Laboratory of Vaccines for Infectious Diseases, Xiang An Biomedicine Laboratory, School of Public Health and School of Life Sciences, Xiamen University, Xiamen 361102, Fujian, China; National Institute of Diagnostics and Vaccine Development in Infectious Diseases, State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, NMPA Key Laboratory for Research and Evaluation of Infectious Disease Diagnostic Technology, Xiamen University, Xiamen 361102, Fujian, China
| | - Rao Fu
- State Key Laboratory of Vaccines for Infectious Diseases, Xiang An Biomedicine Laboratory, School of Public Health and School of Life Sciences, Xiamen University, Xiamen 361102, Fujian, China; National Institute of Diagnostics and Vaccine Development in Infectious Diseases, State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, NMPA Key Laboratory for Research and Evaluation of Infectious Disease Diagnostic Technology, Xiamen University, Xiamen 361102, Fujian, China
| | - Xing Lei
- State Key Laboratory of Vaccines for Infectious Diseases, Xiang An Biomedicine Laboratory, School of Public Health and School of Life Sciences, Xiamen University, Xiamen 361102, Fujian, China; National Institute of Diagnostics and Vaccine Development in Infectious Diseases, State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, NMPA Key Laboratory for Research and Evaluation of Infectious Disease Diagnostic Technology, Xiamen University, Xiamen 361102, Fujian, China
| | - Jinhang He
- State Key Laboratory of Vaccines for Infectious Diseases, Xiang An Biomedicine Laboratory, School of Public Health and School of Life Sciences, Xiamen University, Xiamen 361102, Fujian, China; National Institute of Diagnostics and Vaccine Development in Infectious Diseases, State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, NMPA Key Laboratory for Research and Evaluation of Infectious Disease Diagnostic Technology, Xiamen University, Xiamen 361102, Fujian, China
| | - Yao Jiang
- State Key Laboratory of Vaccines for Infectious Diseases, Xiang An Biomedicine Laboratory, School of Public Health and School of Life Sciences, Xiamen University, Xiamen 361102, Fujian, China; National Institute of Diagnostics and Vaccine Development in Infectious Diseases, State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, NMPA Key Laboratory for Research and Evaluation of Infectious Disease Diagnostic Technology, Xiamen University, Xiamen 361102, Fujian, China
| | - Liang Zhang
- State Key Laboratory of Vaccines for Infectious Diseases, Xiang An Biomedicine Laboratory, School of Public Health and School of Life Sciences, Xiamen University, Xiamen 361102, Fujian, China; National Institute of Diagnostics and Vaccine Development in Infectious Diseases, State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, NMPA Key Laboratory for Research and Evaluation of Infectious Disease Diagnostic Technology, Xiamen University, Xiamen 361102, Fujian, China
| | - Yangtao Wu
- State Key Laboratory of Vaccines for Infectious Diseases, Xiang An Biomedicine Laboratory, School of Public Health and School of Life Sciences, Xiamen University, Xiamen 361102, Fujian, China; National Institute of Diagnostics and Vaccine Development in Infectious Diseases, State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, NMPA Key Laboratory for Research and Evaluation of Infectious Disease Diagnostic Technology, Xiamen University, Xiamen 361102, Fujian, China
| | - Siling Wang
- State Key Laboratory of Vaccines for Infectious Diseases, Xiang An Biomedicine Laboratory, School of Public Health and School of Life Sciences, Xiamen University, Xiamen 361102, Fujian, China; National Institute of Diagnostics and Vaccine Development in Infectious Diseases, State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, NMPA Key Laboratory for Research and Evaluation of Infectious Disease Diagnostic Technology, Xiamen University, Xiamen 361102, Fujian, China
| | - Xueran Guo
- State Key Laboratory of Vaccines for Infectious Diseases, Xiang An Biomedicine Laboratory, School of Public Health and School of Life Sciences, Xiamen University, Xiamen 361102, Fujian, China; National Institute of Diagnostics and Vaccine Development in Infectious Diseases, State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, NMPA Key Laboratory for Research and Evaluation of Infectious Disease Diagnostic Technology, Xiamen University, Xiamen 361102, Fujian, China
| | - Feng Chen
- State Key Laboratory of Vaccines for Infectious Diseases, Xiang An Biomedicine Laboratory, School of Public Health and School of Life Sciences, Xiamen University, Xiamen 361102, Fujian, China; National Institute of Diagnostics and Vaccine Development in Infectious Diseases, State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, NMPA Key Laboratory for Research and Evaluation of Infectious Disease Diagnostic Technology, Xiamen University, Xiamen 361102, Fujian, China
| | - Meifeng Nie
- State Key Laboratory of Vaccines for Infectious Diseases, Xiang An Biomedicine Laboratory, School of Public Health and School of Life Sciences, Xiamen University, Xiamen 361102, Fujian, China; National Institute of Diagnostics and Vaccine Development in Infectious Diseases, State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, NMPA Key Laboratory for Research and Evaluation of Infectious Disease Diagnostic Technology, Xiamen University, Xiamen 361102, Fujian, China
| | - Man Yang
- State Key Laboratory of Vaccines for Infectious Diseases, Xiang An Biomedicine Laboratory, School of Public Health and School of Life Sciences, Xiamen University, Xiamen 361102, Fujian, China; National Institute of Diagnostics and Vaccine Development in Infectious Diseases, State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, NMPA Key Laboratory for Research and Evaluation of Infectious Disease Diagnostic Technology, Xiamen University, Xiamen 361102, Fujian, China
| | - Yiyi Chen
- State Key Laboratory of Vaccines for Infectious Diseases, Xiang An Biomedicine Laboratory, School of Public Health and School of Life Sciences, Xiamen University, Xiamen 361102, Fujian, China; National Institute of Diagnostics and Vaccine Development in Infectious Diseases, State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, NMPA Key Laboratory for Research and Evaluation of Infectious Disease Diagnostic Technology, Xiamen University, Xiamen 361102, Fujian, China
| | - Jing Zeng
- State Key Laboratory of Vaccines for Infectious Diseases, Xiang An Biomedicine Laboratory, School of Public Health and School of Life Sciences, Xiamen University, Xiamen 361102, Fujian, China; National Institute of Diagnostics and Vaccine Development in Infectious Diseases, State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, NMPA Key Laboratory for Research and Evaluation of Infectious Disease Diagnostic Technology, Xiamen University, Xiamen 361102, Fujian, China; Department of clinical laboratory, Women and Children's Hospital, School of Medicine, Xiamen University, Xiamen 361102, Fujian, China
| | - Jingjing Xu
- Key Laboratory of Gastrointestinal Cancer (Fujian Medical University), Ministry of Education, Fuzhou, China
| | - Hualong Xiong
- State Key Laboratory of Vaccines for Infectious Diseases, Xiang An Biomedicine Laboratory, School of Public Health and School of Life Sciences, Xiamen University, Xiamen 361102, Fujian, China; National Institute of Diagnostics and Vaccine Development in Infectious Diseases, State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, NMPA Key Laboratory for Research and Evaluation of Infectious Disease Diagnostic Technology, Xiamen University, Xiamen 361102, Fujian, China
| | - Mujin Fang
- State Key Laboratory of Vaccines for Infectious Diseases, Xiang An Biomedicine Laboratory, School of Public Health and School of Life Sciences, Xiamen University, Xiamen 361102, Fujian, China; National Institute of Diagnostics and Vaccine Development in Infectious Diseases, State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, NMPA Key Laboratory for Research and Evaluation of Infectious Disease Diagnostic Technology, Xiamen University, Xiamen 361102, Fujian, China
| | - Yuqiong Que
- State Key Laboratory of Vaccines for Infectious Diseases, Xiang An Biomedicine Laboratory, School of Public Health and School of Life Sciences, Xiamen University, Xiamen 361102, Fujian, China; National Institute of Diagnostics and Vaccine Development in Infectious Diseases, State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, NMPA Key Laboratory for Research and Evaluation of Infectious Disease Diagnostic Technology, Xiamen University, Xiamen 361102, Fujian, China
| | - Youliang Yao
- State Key Laboratory of Vaccines for Infectious Diseases, Xiang An Biomedicine Laboratory, School of Public Health and School of Life Sciences, Xiamen University, Xiamen 361102, Fujian, China; National Institute of Diagnostics and Vaccine Development in Infectious Diseases, State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, NMPA Key Laboratory for Research and Evaluation of Infectious Disease Diagnostic Technology, Xiamen University, Xiamen 361102, Fujian, China
| | - Yingbin Wang
- State Key Laboratory of Vaccines for Infectious Diseases, Xiang An Biomedicine Laboratory, School of Public Health and School of Life Sciences, Xiamen University, Xiamen 361102, Fujian, China; National Institute of Diagnostics and Vaccine Development in Infectious Diseases, State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, NMPA Key Laboratory for Research and Evaluation of Infectious Disease Diagnostic Technology, Xiamen University, Xiamen 361102, Fujian, China
| | - Jiali Cao
- State Key Laboratory of Vaccines for Infectious Diseases, Xiang An Biomedicine Laboratory, School of Public Health and School of Life Sciences, Xiamen University, Xiamen 361102, Fujian, China; National Institute of Diagnostics and Vaccine Development in Infectious Diseases, State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, NMPA Key Laboratory for Research and Evaluation of Infectious Disease Diagnostic Technology, Xiamen University, Xiamen 361102, Fujian, China; Department of clinical laboratory, Women and Children's Hospital, School of Medicine, Xiamen University, Xiamen 361102, Fujian, China
| | - Huiming Ye
- Department of clinical laboratory, Women and Children's Hospital, School of Medicine, Xiamen University, Xiamen 361102, Fujian, China
| | - Yali Zhang
- State Key Laboratory of Vaccines for Infectious Diseases, Xiang An Biomedicine Laboratory, School of Public Health and School of Life Sciences, Xiamen University, Xiamen 361102, Fujian, China; National Institute of Diagnostics and Vaccine Development in Infectious Diseases, State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, NMPA Key Laboratory for Research and Evaluation of Infectious Disease Diagnostic Technology, Xiamen University, Xiamen 361102, Fujian, China
| | - Zizheng Zheng
- State Key Laboratory of Vaccines for Infectious Diseases, Xiang An Biomedicine Laboratory, School of Public Health and School of Life Sciences, Xiamen University, Xiamen 361102, Fujian, China; National Institute of Diagnostics and Vaccine Development in Infectious Diseases, State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, NMPA Key Laboratory for Research and Evaluation of Infectious Disease Diagnostic Technology, Xiamen University, Xiamen 361102, Fujian, China
| | - Tong Cheng
- State Key Laboratory of Vaccines for Infectious Diseases, Xiang An Biomedicine Laboratory, School of Public Health and School of Life Sciences, Xiamen University, Xiamen 361102, Fujian, China; National Institute of Diagnostics and Vaccine Development in Infectious Diseases, State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, NMPA Key Laboratory for Research and Evaluation of Infectious Disease Diagnostic Technology, Xiamen University, Xiamen 361102, Fujian, China
| | - Jun Zhang
- State Key Laboratory of Vaccines for Infectious Diseases, Xiang An Biomedicine Laboratory, School of Public Health and School of Life Sciences, Xiamen University, Xiamen 361102, Fujian, China; National Institute of Diagnostics and Vaccine Development in Infectious Diseases, State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, NMPA Key Laboratory for Research and Evaluation of Infectious Disease Diagnostic Technology, Xiamen University, Xiamen 361102, Fujian, China
| | - Xu Lin
- Key Laboratory of Gastrointestinal Cancer (Fujian Medical University), Ministry of Education, Fuzhou, China.
| | - Quan Yuan
- State Key Laboratory of Vaccines for Infectious Diseases, Xiang An Biomedicine Laboratory, School of Public Health and School of Life Sciences, Xiamen University, Xiamen 361102, Fujian, China; National Institute of Diagnostics and Vaccine Development in Infectious Diseases, State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, NMPA Key Laboratory for Research and Evaluation of Infectious Disease Diagnostic Technology, Xiamen University, Xiamen 361102, Fujian, China.
| | - Tianying Zhang
- State Key Laboratory of Vaccines for Infectious Diseases, Xiang An Biomedicine Laboratory, School of Public Health and School of Life Sciences, Xiamen University, Xiamen 361102, Fujian, China; National Institute of Diagnostics and Vaccine Development in Infectious Diseases, State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, NMPA Key Laboratory for Research and Evaluation of Infectious Disease Diagnostic Technology, Xiamen University, Xiamen 361102, Fujian, China.
| | - Ningshao Xia
- State Key Laboratory of Vaccines for Infectious Diseases, Xiang An Biomedicine Laboratory, School of Public Health and School of Life Sciences, Xiamen University, Xiamen 361102, Fujian, China; National Institute of Diagnostics and Vaccine Development in Infectious Diseases, State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, NMPA Key Laboratory for Research and Evaluation of Infectious Disease Diagnostic Technology, Xiamen University, Xiamen 361102, Fujian, China.
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Meng Z, Guo Y, Deng S, Xiang Q, Cao J, Zhang Y, Zhang K, Ma K, Xie S, Kang Z. Improving image quality of triple-low-protocol renal artery CT angiography with deep-learning image reconstruction: a comparative study with standard-dose single-energy and dual-energy CT with adaptive statistical iterative reconstruction. Clin Radiol 2024; 79:e651-e658. [PMID: 38433041 DOI: 10.1016/j.crad.2024.01.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2023] [Revised: 01/03/2024] [Accepted: 01/09/2024] [Indexed: 03/05/2024]
Abstract
AIM To investigate the improvement in image quality of triple-low-protocol (low radiation, low contrast medium dose, low injection speed) renal artery computed tomography (CT) angiography (RACTA) using deep-learning image reconstruction (DLIR), in comparison with standard-dose single- and dual-energy CT (DECT) using adaptive statistical iterative reconstruction-Veo (ASIR-V) algorithm. MATERIALS AND METHODS Ninety patients for RACTA were divided into different groups: standard-dose single-energy CT (S group) using ASIR-V at 60% strength (60%ASIR-V), DECT (DE group) with 60%ASIR-V including virtual monochromatic images at 40 keV (DE40 group) and 70 keV (DE70 group), and the triple-low protocol single-energy CT (L group) with DLIR at high level (DLIR-H). The effective dose (ED), contrast medium dose, injection speed, standard deviation (SD), signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) of abdominal aorta (AA), and left/right renal artery (LRA, RRA), and subjective scores were compared among the different groups. RESULTS The L group significantly reduced ED by 37.6% and 31.2%, contrast medium dose by 33.9% and 30.5%, and injection speed by 30% and 30%, respectively, compared to the S and DE groups. The L group had the lowest SD values for all arteries compared to the other groups (p<0.001). The SNR of RRA and LRA in the L group, and the CNR of all arteries in the DE40 group had highest value compared to others (p<0.05). The L group had the best comprehensive score with good consistency (p<0.05). CONCLUSIONS The triple-low protocol RACTA with DLIR-H significantly reduces the ED, contrast medium doses, and injection speed, while providing good comprehensive image quality.
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Affiliation(s)
- Z Meng
- Department of Radiology, The Third Affiliated Hospital of Sun Yat-sen University, Tianhe District, Tianhe Road, 600, Guangzhou, 510620, China
| | - Y Guo
- Department of Radiology, The Third Affiliated Hospital of Sun Yat-sen University, Tianhe District, Tianhe Road, 600, Guangzhou, 510620, China
| | - S Deng
- Department of Radiology, The Third Affiliated Hospital of Sun Yat-sen University, Tianhe District, Tianhe Road, 600, Guangzhou, 510620, China
| | - Q Xiang
- Department of Radiology, The Third Affiliated Hospital of Sun Yat-sen University, Tianhe District, Tianhe Road, 600, Guangzhou, 510620, China
| | - J Cao
- Department of Radiology, The Third Affiliated Hospital of Sun Yat-sen University, Tianhe District, Tianhe Road, 600, Guangzhou, 510620, China
| | - Y Zhang
- Department of Radiology, The Third Affiliated Hospital of Sun Yat-sen University, Tianhe District, Tianhe Road, 600, Guangzhou, 510620, China
| | - K Zhang
- Department of Radiology, The Third Affiliated Hospital of Sun Yat-sen University, Tianhe District, Tianhe Road, 600, Guangzhou, 510620, China
| | - K Ma
- CT Imaging Research Center, GE HealthCare China, Tianhe District, Huacheng Road 87, Guangzhou, 510623, China
| | - S Xie
- Department of Radiology, The Third Affiliated Hospital of Sun Yat-sen University, Tianhe District, Tianhe Road, 600, Guangzhou, 510620, China.
| | - Z Kang
- Department of Radiology, The Third Affiliated Hospital of Sun Yat-sen University, Tianhe District, Tianhe Road, 600, Guangzhou, 510620, China.
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Kong H, Cao J, Tian J, Yong J, An J, Zhang L, Song X, He Y. Coronary microvascular dysfunction: prevalence and aetiology in patients with suspected myocardial ischaemia. Clin Radiol 2024; 79:386-392. [PMID: 38433042 DOI: 10.1016/j.crad.2024.01.010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2023] [Revised: 09/19/2023] [Accepted: 01/09/2024] [Indexed: 03/05/2024]
Abstract
AIM To evaluate the prevalence, aetiology, and corresponding morbidity of coronary microvascular dysfunction (CMD) in patients with suspected myocardial ischaemia. MATERIALS AND METHODS The present study included 115 patients with suspected myocardial ischaemia who underwent stress perfusion cardiac magnetic resonance imaging. CMD was assessed visually based on the myocardial perfusion results. The CMR-derived myocardial perfusion reserve index (MPRI) and left ventricular (LV) strain parameters obtained using the post-processing software CVI42 were employed to evaluate LV myocardial perfusion and deformation. LV strain parameters included global longitudinal, circumferential, and radial strain (GLS, GCS, and GRS), global systolic/diastolic longitudinal, circumferential, and radial strain rates (SLSR, SCSR, SRSR, DLSR, DCSR, and DRSR). RESULTS Of the 115 patients, 12 patients were excluded and 103 patients were finally included in the study. CMD was observed in 79 % (81 patients, aged 53 ± 12 years) of patients. Regarding aetiology, 91 (88 %) patients had non-obstructive coronary artery disease (CAD), eight (8 %) had obstructive CAD, and four (4 %) had hypertrophic cardiomyopathy (HCM). The incidence of CMD was highest (100 %) in patients with HCM, followed by those with non-obstructive CAD (up to 79 %). There were no statistical differences between CMD and non-CMD groups in GCS, GRS, GLS, SRSR, SCSR, SLSR, DCSR, DRSR and DLSR. CONCLUSION The incidence of CMD was higher in patients with signs and symptoms of ischaemia. CMD occurred with non-obstructive CAD, obstructive CAD, and HCM, with the highest prevalence of CMD in HCM.
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Affiliation(s)
- H Kong
- Department of Radiology, Beijing Friendship Hospital, Capital Medical University, Beijing, 100050, China
| | - J Cao
- Department of Radiology, Beijing Friendship Hospital, Capital Medical University, Beijing, 100050, China
| | - J Tian
- Department of Cardiology, Beijing Anzhen Hospital, Capital Medical University, Beijing, China
| | - J Yong
- Department of Cardiology, Beijing Anzhen Hospital, Capital Medical University, Beijing, China
| | - J An
- Siemens Shenzhen Magnetic Resonance, MR Collaboration NE Asia, Shenzhen, China
| | - L Zhang
- Department of Radiology, Beijing Anzhen Hospital, Capital Medical University, Beijing, China
| | - X Song
- Department of Cardiology, Beijing Anzhen Hospital, Capital Medical University, Beijing, China.
| | - Y He
- Department of Radiology, Beijing Friendship Hospital, Capital Medical University, Beijing, 100050, China.
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Huang Y, Cao J, Zhu M, Wang Z, Jin Z, Xiong Z. Bacteroides fragilis aggravates high-fat diet-induced non-alcoholic fatty liver disease by regulating lipid metabolism and remodeling gut microbiota. Microbiol Spectr 2024; 12:e0339323. [PMID: 38411057 DOI: 10.1128/spectrum.03393-23] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2023] [Accepted: 02/05/2024] [Indexed: 02/28/2024] Open
Abstract
Gut microbiota dysbiosis is a prominent determinant that significantly contributes to the disruption of lipid metabolism. Consequently, it is essential to the occurrence and development of non-alcoholic fatty liver disease (NAFLD). Nevertheless, the connection between diet and symbiotic gut microbiota in the progression of NAFLD remains uncertain. The purpose of this study was to explore the role of supplementing commensal Bacteroides fragilis (B. fragilis) on lipid metabolism, gut microbiota, and metabolites in high-fat diet (HFD)-fed mice, elucidating the impact of gut microbiota and metabolites on the development of NAFLD. Our study revealed that supplementation with B. fragilis exacerbated both weight gain and obesity in mice. B. fragilis exacerbated blood glucose levels and liver dysfunction in mice. Furthermore, an increase in liver lipid accumulation and the upregulation of genes correlated with lipid metabolism were observed in mice. Under an HFD, supplementation of commensal B. fragilis resulted in alterations in the gut microbiota, notably a significant increase in Desulfovibrionaceae, which led to elevated endotoxin levels and thereby influenced the progression of NAFLD. It was interesting that the simultaneous examination of gut microbiota metabolites revealed a more pronounced impact of diet on short-chain fatty acids. This study represented the pioneering investigation into the impact of B. fragilis on NAFLD. Our findings demonstrated that B. fragilis induced dysregulation in the intestinal microbiota, leading to elevated levels of lipopolysaccharide and dysfunction in glucose and lipid metabolism, thereby exacerbating NAFLD.IMPORTANCESome intestinal symbiotic microbes are involved in the occurrence of the metabolic disorders. Our study investigated the impact of supplementing commensal Bacteroides fragilis on host metabolism in high-fat diet-fed mice. Research results indicated that adding a specific bacterial strain to the complex intestinal microecology can worsen metabolic conditions. This effect mainly affects the structural diversity of intestinal microorganisms, the increase in harmful bacteria in the gut, and the elevation of endotoxin levels, blood glucose, and lipid metabolism, thereby impacting the progression of non-alcoholic fatty liver disease (NAFLD). Understanding the principles that govern the establishment of microbial communities comprising multiple species is crucial for preventing or repairing dysfunctions in these communities, thereby enhancing host health and facilitating disease treatment. This study demonstrated that gut microbiota dysbiosis could contribute to metabolic dysfunction and provides new insights into how to promote gut microbiota in the prevention and therapy of NAFLD.
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Affiliation(s)
- Yumei Huang
- Department of Gastroenterology, Liyuan Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Jiali Cao
- Department of Gastroenterology, Liyuan Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Mengpei Zhu
- Department of Gastroenterology, Liyuan Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Ziwen Wang
- Department of Gastroenterology, Liyuan Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Ze Jin
- Department of Gastroenterology, Liyuan Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Zhifan Xiong
- Department of Gastroenterology, Liyuan Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
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Gui M, Wu C, Qi R, Zeng Y, Huang P, Cao J, Chen T, Chen K, Lin L, Han Q, He P, Fu R, Wu Q, Yuan Q, Zhang T, Xia N, Wang G, Chen Y. Swine pseudorabies virus attenuated vaccine reprograms the kidney cancer tumor microenvironment and synergizes with PD-1 blockade. J Med Virol 2024; 96:e29568. [PMID: 38549430 DOI: 10.1002/jmv.29568] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2023] [Revised: 01/27/2024] [Accepted: 03/02/2024] [Indexed: 04/02/2024]
Abstract
The global incidence rate of kidney cancer (KC) has been steadily increasing over the past 30 years. With the aging global population, kidney cancer has become an escalating concern that necessitates vigilant surveillance. Nowadays, surgical intervention remains the optimal therapeutic approach for kidney cancer, while the availability of efficacious treatments for advanced tumors remains limited. Oncolytic viruses, an emerging form of immunotherapy, have demonstrated encouraging anti-neoplastic properties and are progressively garnering public acceptance. However, research on oncolytic viruses in kidney cancer is relatively limited. Furthermore, given the high complexity and heterogeneity of kidney cancer, it is crucial to identify an optimal oncolytic virus agent that is better suited for its treatment. The present study investigates the oncolytic activity of the Pseudorabies virus live attenuated vaccine (PRV-LAV) against KC. The findings clearly demonstrate that PRV-LAV exhibits robust oncolytic activity targeting KC cell lines. Furthermore, the therapeutic efficacy of PRV-LAV was confirmed in both a subcutaneous tumor-bearing nude mouse model and a syngeneic mouse model of KC. Combined RNA-seq analysis and flow cytometry revealed that PRV-LAV treatment substantially enhances the infiltration of a diverse range of lymphocytes, including T cells, B cells, macrophages, and NK cells. Additionally, PRV-LAV treatment enhances T cell activation and exerts antitumor effects. Importantly, the combination of PRV-LAV with anti-PD-1 antibodies, an approved drug for KC treatment, synergistically enhances the efficacy against KC. Overall, the discovery of PRV-LAV as an effective oncolytic virus holds significant importance for improving the treatment efficacy and survival rates of KC patients.
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Affiliation(s)
- Mengxuan Gui
- State Key Laboratory of Vaccines for Infectious Diseases, National Institute of Diagnostics and Vaccine Development in Infectious Diseases, State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Life Sciences, School of Public Health, Xiamen University, Xiamen, People's Republic of China
| | - Chongxin Wu
- State Key Laboratory of Vaccines for Infectious Diseases, National Institute of Diagnostics and Vaccine Development in Infectious Diseases, State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Life Sciences, School of Public Health, Xiamen University, Xiamen, People's Republic of China
| | - Ruoyao Qi
- State Key Laboratory of Vaccines for Infectious Diseases, National Institute of Diagnostics and Vaccine Development in Infectious Diseases, State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Life Sciences, School of Public Health, Xiamen University, Xiamen, People's Republic of China
| | - Yue Zeng
- State Key Laboratory of Vaccines for Infectious Diseases, National Institute of Diagnostics and Vaccine Development in Infectious Diseases, State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Life Sciences, School of Public Health, Xiamen University, Xiamen, People's Republic of China
| | - Pengfei Huang
- State Key Laboratory of Vaccines for Infectious Diseases, National Institute of Diagnostics and Vaccine Development in Infectious Diseases, State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Life Sciences, School of Public Health, Xiamen University, Xiamen, People's Republic of China
| | - Jiali Cao
- Department of Laboratory Medicine, Fujian Key Clinical Specialty of Laboratory Medicine, Women and Children's Hospital, School of Medicine, Xiamen University, Xiamen
| | - Tian Chen
- State Key Laboratory of Vaccines for Infectious Diseases, National Institute of Diagnostics and Vaccine Development in Infectious Diseases, State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Life Sciences, School of Public Health, Xiamen University, Xiamen, People's Republic of China
| | - Kaiyun Chen
- State Key Laboratory of Vaccines for Infectious Diseases, National Institute of Diagnostics and Vaccine Development in Infectious Diseases, State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Life Sciences, School of Public Health, Xiamen University, Xiamen, People's Republic of China
| | - Lina Lin
- State Key Laboratory of Vaccines for Infectious Diseases, National Institute of Diagnostics and Vaccine Development in Infectious Diseases, State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Life Sciences, School of Public Health, Xiamen University, Xiamen, People's Republic of China
| | - Qiangyuan Han
- State Key Laboratory of Vaccines for Infectious Diseases, National Institute of Diagnostics and Vaccine Development in Infectious Diseases, State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Life Sciences, School of Public Health, Xiamen University, Xiamen, People's Republic of China
| | - Peiqing He
- State Key Laboratory of Vaccines for Infectious Diseases, National Institute of Diagnostics and Vaccine Development in Infectious Diseases, State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Life Sciences, School of Public Health, Xiamen University, Xiamen, People's Republic of China
| | - Rao Fu
- State Key Laboratory of Vaccines for Infectious Diseases, National Institute of Diagnostics and Vaccine Development in Infectious Diseases, State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Life Sciences, School of Public Health, Xiamen University, Xiamen, People's Republic of China
| | - Qian Wu
- State Key Laboratory of Vaccines for Infectious Diseases, National Institute of Diagnostics and Vaccine Development in Infectious Diseases, State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Life Sciences, School of Public Health, Xiamen University, Xiamen, People's Republic of China
| | - Quan Yuan
- State Key Laboratory of Vaccines for Infectious Diseases, National Institute of Diagnostics and Vaccine Development in Infectious Diseases, State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Life Sciences, School of Public Health, Xiamen University, Xiamen, People's Republic of China
| | - Tianying Zhang
- State Key Laboratory of Vaccines for Infectious Diseases, National Institute of Diagnostics and Vaccine Development in Infectious Diseases, State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Life Sciences, School of Public Health, Xiamen University, Xiamen, People's Republic of China
| | - Ningshao Xia
- State Key Laboratory of Vaccines for Infectious Diseases, National Institute of Diagnostics and Vaccine Development in Infectious Diseases, State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Life Sciences, School of Public Health, Xiamen University, Xiamen, People's Republic of China
| | - Guosong Wang
- State Key Laboratory of Vaccines for Infectious Diseases, National Institute of Diagnostics and Vaccine Development in Infectious Diseases, State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Life Sciences, School of Public Health, Xiamen University, Xiamen, People's Republic of China
| | - Yixin Chen
- State Key Laboratory of Vaccines for Infectious Diseases, National Institute of Diagnostics and Vaccine Development in Infectious Diseases, State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Life Sciences, School of Public Health, Xiamen University, Xiamen, People's Republic of China
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Zhu M, Liu J, Cao J, Ni Y, Chang M, Chen R, Su Z, Yu W, Ye H. Validation of a new kit for preeclampsia screening: A comprehensive analysis. Heliyon 2024; 10:e28080. [PMID: 38533029 PMCID: PMC10963371 DOI: 10.1016/j.heliyon.2024.e28080] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2023] [Revised: 03/11/2024] [Accepted: 03/12/2024] [Indexed: 03/28/2024] Open
Abstract
Objectives Preeclampsia is a common pregnancy complication that significantly contributes to maternal mortality, perinatal mortality, and preterm delivery. The sFlt-1/PlGF (fms-like tyrosine kinase-1/placental growth factor) ratio has demonstrated robust diagnostic value for preeclampsia. This study assessed the analytical performance and diagnostic accuracy of a novel quantitative determination kit for sFlt-1 and PlGF for the diagnosis of preeclampsia. Methods The detection performance of the test kit was validated using the Center for Medical Device Evaluation (CMDE) and Clinical and Laboratory Standards Institute (CLSI) documents. The test results were compared to those of the Elecsys immunoassay (Roche Diagnostics). Independent discovery and validation sets were used to analyze the diagnostic efficacy of the preeclampsia kit. The area under the curve (AUC) for preeclampsia at different gestational ages was calculated. Results Correlation analysis between the test and Roche kits revealed a strong concordance (sFlt-1: r = 0.9966, P < 0.0001; PlGF: r = 0.9935, P < 0.0001). The AUCs for sFlt-1, PlGF, and the sFlt-1/PlGF ratio in diagnosing preeclampsia were 0.749, 0.795, and 0.834, respectively, in the discovery set and 0.729, 0.811, and 0.831, respectively, in the validation set. The corresponding results from the Roche kit were 0.741, 0.795, and 0.829, respectively, and 0.761, 0.864, and 0.844, respectively. Conclusions Quantitative sFlt-1 and PlGF kits exhibited high levels of consistency with the Roche kits in terms of quantitative outcomes and diagnostic performance for preeclampsia.
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Affiliation(s)
- Min Zhu
- Department of Laboratory Medicine, Fujian Key Clinical Specialty of Laboratory Medicine, Women and Children's Hospital, School of Medicine, Xiamen University, Xiamen, China
| | - Jumei Liu
- Department of Laboratory Medicine, Fujian Key Clinical Specialty of Laboratory Medicine, Women and Children's Hospital, School of Medicine, Xiamen University, Xiamen, China
| | - Jiali Cao
- Department of Laboratory Medicine, Fujian Key Clinical Specialty of Laboratory Medicine, Women and Children's Hospital, School of Medicine, Xiamen University, Xiamen, China
| | - Yan Ni
- Department of Obstetrics, Women and Children's Hospital, School of Medicine, Xiamen University, Xiamen, China
| | - Mengqi Chang
- School of Public Health, Xiamen University, Xiamen, China
| | - Ruitong Chen
- School of Public Health, Xiamen University, Xiamen, China
| | - Zhiying Su
- Department of Obstetrics, Women and Children's Hospital, School of Medicine, Xiamen University, Xiamen, China
| | - Weiwei Yu
- Department of Obstetrics, Women and Children's Hospital, School of Medicine, Xiamen University, Xiamen, China
| | - Huiming Ye
- Department of Laboratory Medicine, Fujian Key Clinical Specialty of Laboratory Medicine, Women and Children's Hospital, School of Medicine, Xiamen University, Xiamen, China
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Kong H, Cao J, Tian J, Yong J, An J, Song X, He Y. Relationship between coronary microvascular dysfunction (CMD) and left ventricular diastolic function in patients with symptoms of myocardial ischemia with non-obstructive coronary artery disease (INOCA) by cardiovascular magnetic resonance feature-tracking. Clin Radiol 2024:S0009-9260(24)00129-6. [PMID: 38679491 DOI: 10.1016/j.crad.2024.02.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2023] [Revised: 02/18/2024] [Accepted: 02/20/2024] [Indexed: 05/01/2024]
Abstract
AIM To investigate whether there was an association between coronary microvascular dysfunction (CMD) and left ventricular (LV) diastolic function in patients with myocardial ischemia with non-obstructive coronary artery disease (INOCA). MATERIALS AND METHODS Our study included 115 subjects with suspected myocardial ischemia that underwent stress perfusion cardiac magnetic resonance (CMR). They were divided into non-CMD and CMD two groups. CMR-derived volume-time curves and CMR-FT parameters were used to assess LV diastolic function using CVI42 software. The latter included global/regional LV peak longitudinal, circumferential, radial diastolic strain rate (LDSR, CDSR, RDSR). Logistic regression analysis was performed with CMR-FT strain parameters as independent variables and CMD as dependent variables, and the effect value was expressed as an odds ratio (OR). RESULTS Of the 115 patients, we excluded data from 23 patients and 92 patients (56.5% male;52 ± 12 years) were finally included in the study. Of these, 19 patients were included in the non-CMD group (49 ± 11 years) and CMD group included 73patient (52 ± 12 years). The regional CDSR (P=0.019), and regional RDSR (P=0.006) were significantly lower in the CMD group than in non-CMD group. But, regional LDSR in CMD group was higher than non-CMD (P=0.003). In logistic regression analysis, regional LDSR (adjusted β= 0.1, 95%CI 0.077, 0.349, p=0.002) and RDSR (adjusted β= 0.1, 95 % CI 0.066, 0.356, p=0.004) were related to CMD. CONCLUSIONS LV myocardial perfusion parameter MPRI was negatively correlated with LV diastolic function (CDSR) which needs to take into account the degree of diastolic dysfunction.
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Affiliation(s)
- H Kong
- Department of Radiology, Beijing Friendship Hospital, Capital Medical University, Beijing, 100050, China
| | - J Cao
- Department of Radiology, Beijing Friendship Hospital, Capital Medical University, Beijing, 100050, China
| | - J Tian
- Department of Cardiology, Beijing Anzhen Hospital, Capital Medical University, Beijing, China
| | - J Yong
- Department of Cardiology, Beijing Anzhen Hospital, Capital Medical University, Beijing, China
| | - J An
- Siemens Shenzhen Magnetic Resonance, MR Collaboration NE Asia, Shenzhen, China
| | - X Song
- Department of Cardiology, Beijing Anzhen Hospital, Capital Medical University, Beijing, China.
| | - Y He
- Department of Radiology, Beijing Friendship Hospital, Capital Medical University, Beijing, 100050, China.
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Cao J, Pan J, Yang X, Liu J, Zhu M, Zhao Z, Chen L, Chen T, Ye H. Trends in Respiratory Infectious Pathogens in Children Under the Age of 14 - Xiamen City, Fujian Province, China, 2017-2023. China CDC Wkly 2024; 6:143-147. [PMID: 38476820 PMCID: PMC10926045 DOI: 10.46234/ccdcw2024.028] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2024] [Accepted: 02/16/2024] [Indexed: 03/14/2024] Open
Abstract
What is already known about this topic? Respiratory infections pose a significant burden on public health. Despite recent outbreaks occurring in various locations, there is limited information available on the prevalence trends of multiple common respiratory pathogens in China beyond 2022. What is added by this report? A retrospective analysis was conducted on respiratory pathogen infections in a Xiamen hospital over a seven-year period. The analysis revealed fluctuating trends, with the number of infections for certain viruses initially decreasing after 2019, only to rebound to previous or higher levels. Recently, there has been an observed collective increase in positive cases for certain pathogens. What are the implications for public health practice? The study improves understanding of respiratory pathogens, primarily in Xiamen, with potential implications for the improvement of strategies for the prevention and management of respiratory infectious diseases.
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Affiliation(s)
- Jiali Cao
- Department of Laboratory Medicine, Fujian Key Clinical Specialty of Laboratory Medicine, Women and Children’s Hospital, School of Medicine, Xiamen University, Xiamen City, Fujian Province, China
| | - Jie Pan
- State Key Laboratory of Vaccines for Infectious Diseases, Xiang An Biomedicine Laboratory, School of Public Health, Xiamen University, Xiamen City, Fujian Province, China
| | - Xiaoqing Yang
- Department of Pediatrics, Women and Children's Hospital, School of Medicine, Xiamen University, Xiamen City, Fujian Province, China
| | - Jumei Liu
- Department of Laboratory Medicine, Fujian Key Clinical Specialty of Laboratory Medicine, Women and Children’s Hospital, School of Medicine, Xiamen University, Xiamen City, Fujian Province, China
| | - Min Zhu
- Department of Laboratory Medicine, Fujian Key Clinical Specialty of Laboratory Medicine, Women and Children’s Hospital, School of Medicine, Xiamen University, Xiamen City, Fujian Province, China
| | - Zeyu Zhao
- State Key Laboratory of Vaccines for Infectious Diseases, Xiang An Biomedicine Laboratory, School of Public Health, Xiamen University, Xiamen City, Fujian Province, China
| | - Ling Chen
- Department of Laboratory Medicine, Fujian Key Clinical Specialty of Laboratory Medicine, Women and Children’s Hospital, School of Medicine, Xiamen University, Xiamen City, Fujian Province, China
| | - Tianmu Chen
- State Key Laboratory of Vaccines for Infectious Diseases, Xiang An Biomedicine Laboratory, School of Public Health, Xiamen University, Xiamen City, Fujian Province, China
| | - Huiming Ye
- Department of Laboratory Medicine, Fujian Key Clinical Specialty of Laboratory Medicine, Women and Children’s Hospital, School of Medicine, Xiamen University, Xiamen City, Fujian Province, China
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9
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Cao J, Shen C, He W. Editorial: Molecular pathogenesis and control of viral infectious diseases in children. Front Cell Infect Microbiol 2024; 14:1368324. [PMID: 38371302 PMCID: PMC10869591 DOI: 10.3389/fcimb.2024.1368324] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2024] [Accepted: 01/25/2024] [Indexed: 02/20/2024] Open
Affiliation(s)
- Jiali Cao
- Department of Laboratory Medicine, Fujian Key Clinical Specialty of Laboratory Medicine, Women and Children’s Hospital, School of Medicine, Xiamen University, Xiamen, China
| | - Chenguang Shen
- BSL-3 Laboratory (Guangdong), Guangdong Provincial Key Laboratory of Tropical Disease Research, School of Public Health, Guangzhou, China
- Department of Laboratory Medicine, Zhujiang Hospital, Southern Medical University, Guangzhou, China
| | - Wanting He
- Department of Immunology and Microbiology, The Scripps Research Institute, La Jolla, CA, United States
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Chai L, Cao Y, Zhao L, Liu K, Chong Z, Lu Y, Zhu G, Cao J, Lu G. [Quantitative analysis of risk assessment indicators for re-introduction of imported malaria in China]. Zhongguo Xue Xi Chong Bing Fang Zhi Za Zhi 2024; 35:604-613. [PMID: 38413021 DOI: 10.16250/j.32.1374.2023177] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Subscribe] [Scholar Register] [Indexed: 02/29/2024]
Abstract
OBJECTIVE To quantitatively analyze the risk indicators of re-introduction of imported malaria in China and their weighting coefficients, so as to investigate the difference in the contribution of risk indicators included in the current risk assessment framework for re-introduction of imported malaria in China to the risk assessment of re-introduction of imported malaria. METHODS Publications pertaining to the risk assessment framework for re-introduction of imported malaria in China that reported the risk indicators and their weighting coefficients were retrieved in PubMed, Web of Science, CNKI, Wanfang Data, and VIP with terms of "malaria", "re-introduction/re-transmission/re-establishment", "risk assessment/risk evaluation/risk prediction" from the inception of the database through 3 August 2023, and literature search was performed in Google Scholar to ensure the comprehensiveness of the retrieval. Basic characteristics of included studies were extracted using pre-designed information extraction forms by two investigators, and data pertaining to risk indicators of re-introduction of imported malaria were cross-checked by these two investigators. The risk indicators included in the risk assessment framework for re-introduction of imported malaria in China and their weighting coefficients were visualized with the Nightingale's rose diagrams using the software R 4.2.1, and the importance of risk indictors was evaluated with the frequency of risk indicators included in the risk assessment framework and the ranking of weighting coefficients of risk indicators. In addition, the capability of risk indicators screened by different weighting methods was compared by calculating the ratio of the maximum to the minimum of the weighting coefficients of the risk indicators screened by different weighting methods. RESULTS A total of 2 138 publications were retrieved, and following removal of duplications and screening, a total of 8 publications were included in the final analysis. In these 8 studies, 8 risk assessment frameworks for re-introduction of imported malaria in China and 52 risk indicators of re-introduction of imported malaria were reported, in which number of imported malaria cases (n = 8) and species of malaria vectors were more frequently included in the risk assessment frameworks (n = 8), followed by species of imported malaria parasites (n = 6) and population density of local malaria vectors (n = 6), and species of local malaria vectors (n = 6), number of imported malaria cases (n = 5) and species of imported malaria parasites had the three highest weighting coefficients (n = 4). The weighting methods included expert scoring method, combination of expert scoring method and analytic hierarchy process, and combination of expert scoring method and entropy weight method in these 8 studies, and the ratios of the maximum to the minimum of the weighting coefficients of the risk indicators screened by the expert scoring method were 1.143 to 2.241, while the ratios of the maximum to the minimum of the weighting coefficients of the risk indicators screened by combination of the expert scoring method and analytic hierarchy process were 34.970 to 162.000. CONCLUSIONS Number of imported malaria cases, species of imported malaria parasites, species of local malaria vectors and population density of local malaria vectors are core indicators in the current risk assessment framework for re-introduction of imported malaria in China. Combination of the expert scoring method and analytic hierarchy process is superior to the expert scoring method alone for weighting the risk indicators.
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Affiliation(s)
- L Chai
- School of Public Health, Medical College of Yangzhou University, Yangzhou, Jiangsu 225007, China
| | - Y Cao
- National Health Commission of Key Laboratory for Parasitic Disease Prevention and Control, Jiangsu Provincial Key Laboratory on Parasite and Vector Control Technology, Jiangsu Institute of Parasitic Diseases, China
| | - L Zhao
- School of Public Health, Medical College of Yangzhou University, Yangzhou, Jiangsu 225007, China
| | - K Liu
- School of Public Health, Medical College of Yangzhou University, Yangzhou, Jiangsu 225007, China
| | - Z Chong
- School of Public Health, Medical College of Yangzhou University, Yangzhou, Jiangsu 225007, China
| | - Y Lu
- Health and Quarantine Office, Nanjing Customs, China
| | - G Zhu
- National Health Commission of Key Laboratory for Parasitic Disease Prevention and Control, Jiangsu Provincial Key Laboratory on Parasite and Vector Control Technology, Jiangsu Institute of Parasitic Diseases, China
| | - J Cao
- National Health Commission of Key Laboratory for Parasitic Disease Prevention and Control, Jiangsu Provincial Key Laboratory on Parasite and Vector Control Technology, Jiangsu Institute of Parasitic Diseases, China
| | - G Lu
- School of Public Health, Medical College of Yangzhou University, Yangzhou, Jiangsu 225007, China
- Jiangsu Key Laboratory of Zoonoses, Yangzhou University, Yangzhou, Jiangsu 225007, China
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Selcuk MA, Celik F, Simsek S, Ahmed H, Kesik HK, Kilinc SG, Cao J. Genetic, haplotype and phylogenetic analysis of Ligula intestinalis by using mt-CO1 gene marker: ecological implications, climate change and eco-genetic diversity. BRAZ J BIOL 2024; 84:e258626. [DOI: 10.1590/1519-6984.258626] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2021] [Accepted: 03/01/2022] [Indexed: 11/22/2022] Open
Abstract
Abstract Ligula intestinalis is a cestode parasite that affects freshwater fish in different countries of the world. The current study aims to reveal the phylogenetic, genetic and haplotype diversity of mt-CO1 gene sequences sent to the NCBI database from different countries by using in-silico analysis. The 105 mt-CO1 (371 bp) gene sequences of L. intestinalis obtained from NCBI were used for bioinformatics analyses. Sequences were subjected to phylogenetic and haplotype analysis. As a result of the haplotype analysis of L. intestinalis, 38 haplotypes were obtained from 13 different countries. Hap24 constituted 44.76% of the obtained haplotype network. Changes in nucleotides between haplotypes occurred at 1-84 different points. China and Turkey have highest fixation index (Fst) values of 0.59761, while the lowest (-0.10526) was found between Russia and Turkey. This study provides a baseline for future studies on extensive scale on the epidemiology, ecological aspects, distribution pattern, transmission dynamics and population dispersion of L. intestinalis worldwide.
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Affiliation(s)
- M. A. Selcuk
- Siirt University, Turkey; University of Firat, Turkey
| | | | | | - H. Ahmed
- COMSATS University Islamabad, Pakistan
| | | | | | - J. Cao
- National Health Commission of People’s Republic of China, China; National Institute of Parasitic Diseases, China; Shanghai Jiao Tong University School of Medicine, China; World Health Organization Collaborating Centre for Tropical Diseases, China
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12
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Jin ML, Mamute M, Shapaermaimaiti H, Li JX, Cao J, Li HY, Meng FH, Zhao Q, Ji HY, Abuzhalihan J, Aigaixi A, Lu XF, Fu ZY. [Analysis of the prevalence of dyslipidemia and correlative factors in Tajik population in Pamir Plateau of Xinjiang]. Zhonghua Xin Xue Guan Bing Za Zhi 2023; 51:1240-1246. [PMID: 38123206 DOI: 10.3760/cma.j.cn112148-20231007-00231] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Subscribe] [Scholar Register] [Indexed: 12/23/2023]
Abstract
Objective: To investigate the prevalence of dyslipidemia and the level of blood lipids among Tajik people in Pamir Plateau, Xinjiang, and explore the related factors of dyslipidemia. Methods: It is a retrospective cross-sectional study. A multi-stage cluster random sampling survey was conducted among 5 635 Tajiks over 18 years old in Tashkorgan Tajik Autonomous County, Xinjiang Province from May to October 2021. Data were collected through questionnaire survey (general information, medical history, and personal history), physical examination (height, weight, waist, and blood pressure) and blood test (total cholesterol (TC), triglycerides (TG), low-density lipoprotein cholesterol (LDL-C), and high-density cholesterol (HDL-C)) to analyze the dyslipidemia and its risk factors among Tajiks. Results: The age of Tajik participants was (41.9±15.0) years, including 2 726 males (48.4%). The prevalence of borderline high TC, high LDL-C and high TG levels were 17.2%, 14.7% and 8.9%, respectively. The prevalence of high TC, high LDL-C, high TG and low HDL-C were 4.1%, 4.9%, 9.4% and 32.4%, respectively, and the prevalence of dyslipidemia was 37.0%. There is a positive correlation between male,higher education level, higher body mass index (BMI) value,waist circumference, living in town, smoking and dyslipidemia. Conclusions: The low prevalence of high TC, high LDL-C, high TG and high prevalence of low HDL-C was a major characteristic of Tajik people in Pamir Plateau of Xinjiang. The lower rates of overweight and obesity may be one of the reasons for the lower prevalence of dyslipidemia among Tajik.
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Affiliation(s)
- M L Jin
- Department of Cardiology and State Key Laboratory of Pathogenesis, Prevention and Treatment of High Incidence Diseases in Central Asia, First Affiliated Hospital of Xinjiang Medical University, Urumqi 830011, China
| | - Mawusumu Mamute
- Department of Urology, First People's Hospital of Kashgar District, Kashgar 844099, China
| | - Hebali Shapaermaimaiti
- Disease Control and Prevention Center of Tashkurgan Tajik Autonomous County, Kashgar 845250, China
| | - J X Li
- Department of Epidemiology, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100037, China
| | - J Cao
- Department of Epidemiology, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100037, China
| | - H Y Li
- Department of Cardiology, First Affiliated Hospital of Xinjiang Medical University, Urumqi 830011, China
| | - F H Meng
- Department of Cardiology of Affiliated Hospital of Jining Medical University, Jining 272007, China
| | - Q Zhao
- Department of Cardiology, First Affiliated Hospital of Xinjiang Medical University, Urumqi 830011, China
| | - H Y Ji
- Department of Cardiology, First Affiliated Hospital of Xinjiang Medical University, Urumqi 830011, China
| | - Jialin Abuzhalihan
- Department of Cardiology, First Affiliated Hospital of Xinjiang Medical University, Urumqi 830011, China
| | - Abuduhalike Aigaixi
- Health Commission of Tashkurgan Tajik Autonomous County, Kashgar 845250, China
| | - X F Lu
- Department of Epidemiology, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100037, China
| | - Z Y Fu
- Department of Cardiology and State Key Laboratory of Pathogenesis, Prevention and Treatment of High Incidence Diseases in Central Asia, First Affiliated Hospital of Xinjiang Medical University, Urumqi 830011, China
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Cao J, Zhong Q, Huang Y, Zhu M, Wang Z, Xiong Z. Identification and validation of INHBE and P4HA1 as hub genes in non-alcoholic fatty liver disease. Biochem Biophys Res Commun 2023; 686:149180. [PMID: 37922570 DOI: 10.1016/j.bbrc.2023.149180] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2023] [Revised: 10/27/2023] [Accepted: 10/27/2023] [Indexed: 11/07/2023]
Abstract
PURPOSE Non-alcoholic fatty liver disease (NAFLD) is currently the most prevalent type of liver disease and a worldwide disease threatening human health. This study aims to identify the novel diagnostic biomarkers of NAFLD by comprehensive bioinformatics and machine learning, and to validate our results in hepatocyte and animal models. METHODS We used Gene Expression Omnibus (GEO) databases on NAFLD patients for differential gene expression analyses. Intersections were taken with genes from the key modules of WGCNA and differentially expressed genes (DEGs). Machine learning algorithms like LASSO regression analysis, SVM-RFE, and RandomForest were used to screen hub genes. In addition, a nomogram model and calibration curves were built in order to forecast the probability of NAFLD occurrence. Then, the relationship between hub genes and immune cells was verified using Spearman analysis. Finally, we further verified the expression of key genes by constructing a steatosis hepatocyte model and animal model. RESULTS Key genes (INHBE and P4HA1) were identified by comprehensive bioinformatics analysis and machine learning. INHBE and P4HA1 were up-regulated and down-regulated in the steatosis hepatocyte model, respectively. Animal experiments also showed that INHBE was up-regulated in the liver of mice fed with high fat diet (HFD). CONCLUSION INHBE and P4HA1 are the hub genes of NAFLD. Our findings may contribute to a greater understanding of the occurrence and development of NAFLD and provide potential biomarkers and possible therapeutic targets for future clinical diagnosis and treatment.
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Affiliation(s)
- Jiali Cao
- Department of Gastroenterology, Liyuan Hospital, Tongji Medical College of Huazhong University of Science and Technology, Wuhan, 430077, China
| | - Qiangqiang Zhong
- Department of Gastroenterology, Liyuan Hospital, Tongji Medical College of Huazhong University of Science and Technology, Wuhan, 430077, China
| | - Yumei Huang
- Department of Gastroenterology, Liyuan Hospital, Tongji Medical College of Huazhong University of Science and Technology, Wuhan, 430077, China
| | - Mengpei Zhu
- Department of Gastroenterology, Liyuan Hospital, Tongji Medical College of Huazhong University of Science and Technology, Wuhan, 430077, China
| | - Ziwen Wang
- Department of Gastroenterology, Liyuan Hospital, Tongji Medical College of Huazhong University of Science and Technology, Wuhan, 430077, China
| | - Zhifan Xiong
- Department of Gastroenterology, Liyuan Hospital, Tongji Medical College of Huazhong University of Science and Technology, Wuhan, 430077, China.
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14
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Yang Y, Cao J, Zhu Y, Yang QC. [Tubular adenocarcinoma and signet ring cell carcinoma arising in gastric inverted hyperplastic polyp: report of a case]. Zhonghua Bing Li Xue Za Zhi 2023; 52:1284-1286. [PMID: 38058051 DOI: 10.3760/cma.j.cn112151-20230802-00045] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Subscribe] [Scholar Register] [Indexed: 12/08/2023]
Affiliation(s)
- Y Yang
- Department of Pathology, the Second Affiliated Hospital of Nantong University, Nantong 226001, China
| | - J Cao
- Department of Pathology, the Second Affiliated Hospital of Nantong University, Nantong 226001, China
| | - Y Zhu
- Department of Pathology, the Second Affiliated Hospital of Nantong University, Nantong 226001, China
| | - Q C Yang
- Department of Pathology, the Second Affiliated Hospital of Nantong University, Nantong 226001, China
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Wang G, Cao J, Gui M, Huang P, Zhang L, Qi R, Chen R, Lin L, Han Q, Lin Y, Chen T, He P, Ma J, Fu R, Hong J, Wu Q, Yu H, Chen J, Huang C, Zhang T, Yuan Q, Zhang J, Chen Y, Xia N. Correction: The potential of swine pseudorabies virus attenuated vaccine for oncolytic therapy against malignant tumors. J Exp Clin Cancer Res 2023; 42:312. [PMID: 37993926 PMCID: PMC10664667 DOI: 10.1186/s13046-023-02891-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2023] Open
Affiliation(s)
- Guosong Wang
- State Key Laboratory of Vaccines for Infectious Diseases, National Institute of Diagnostics and Vaccine Development in Infectious Diseases, State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, Collaborative Innovation Center of Biologic Products National Innovation Platform for Industry-Education Intergration in Vaccine ResearchSchool of Life Sciences, School of Public Health, Xiang An Biomedicine Laboratory, Xiamen University, Xiamen, People's Republic of China
| | - Jiali Cao
- Department of Laboratory Medicine, Fujian Key Clinical Specialty of Laboratory Medicine, Women and Children's Hospital, School of Medicine, Xiamen University, Xiamen, People's Republic of China
| | - Mengxuan Gui
- State Key Laboratory of Vaccines for Infectious Diseases, National Institute of Diagnostics and Vaccine Development in Infectious Diseases, State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, Collaborative Innovation Center of Biologic Products National Innovation Platform for Industry-Education Intergration in Vaccine ResearchSchool of Life Sciences, School of Public Health, Xiang An Biomedicine Laboratory, Xiamen University, Xiamen, People's Republic of China
| | - Pengfei Huang
- State Key Laboratory of Vaccines for Infectious Diseases, National Institute of Diagnostics and Vaccine Development in Infectious Diseases, State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, Collaborative Innovation Center of Biologic Products National Innovation Platform for Industry-Education Intergration in Vaccine ResearchSchool of Life Sciences, School of Public Health, Xiang An Biomedicine Laboratory, Xiamen University, Xiamen, People's Republic of China
| | - Liang Zhang
- State Key Laboratory of Vaccines for Infectious Diseases, National Institute of Diagnostics and Vaccine Development in Infectious Diseases, State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, Collaborative Innovation Center of Biologic Products National Innovation Platform for Industry-Education Intergration in Vaccine ResearchSchool of Life Sciences, School of Public Health, Xiang An Biomedicine Laboratory, Xiamen University, Xiamen, People's Republic of China
| | - Ruoyao Qi
- State Key Laboratory of Vaccines for Infectious Diseases, National Institute of Diagnostics and Vaccine Development in Infectious Diseases, State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, Collaborative Innovation Center of Biologic Products National Innovation Platform for Industry-Education Intergration in Vaccine ResearchSchool of Life Sciences, School of Public Health, Xiang An Biomedicine Laboratory, Xiamen University, Xiamen, People's Republic of China
| | - Ruiqi Chen
- State Key Laboratory of Vaccines for Infectious Diseases, National Institute of Diagnostics and Vaccine Development in Infectious Diseases, State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, Collaborative Innovation Center of Biologic Products National Innovation Platform for Industry-Education Intergration in Vaccine ResearchSchool of Life Sciences, School of Public Health, Xiang An Biomedicine Laboratory, Xiamen University, Xiamen, People's Republic of China
| | - Lina Lin
- State Key Laboratory of Vaccines for Infectious Diseases, National Institute of Diagnostics and Vaccine Development in Infectious Diseases, State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, Collaborative Innovation Center of Biologic Products National Innovation Platform for Industry-Education Intergration in Vaccine ResearchSchool of Life Sciences, School of Public Health, Xiang An Biomedicine Laboratory, Xiamen University, Xiamen, People's Republic of China
| | - Qiangyuan Han
- State Key Laboratory of Vaccines for Infectious Diseases, National Institute of Diagnostics and Vaccine Development in Infectious Diseases, State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, Collaborative Innovation Center of Biologic Products National Innovation Platform for Industry-Education Intergration in Vaccine ResearchSchool of Life Sciences, School of Public Health, Xiang An Biomedicine Laboratory, Xiamen University, Xiamen, People's Republic of China
| | - Yanhua Lin
- State Key Laboratory of Vaccines for Infectious Diseases, National Institute of Diagnostics and Vaccine Development in Infectious Diseases, State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, Collaborative Innovation Center of Biologic Products National Innovation Platform for Industry-Education Intergration in Vaccine ResearchSchool of Life Sciences, School of Public Health, Xiang An Biomedicine Laboratory, Xiamen University, Xiamen, People's Republic of China
| | - Tian Chen
- State Key Laboratory of Vaccines for Infectious Diseases, National Institute of Diagnostics and Vaccine Development in Infectious Diseases, State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, Collaborative Innovation Center of Biologic Products National Innovation Platform for Industry-Education Intergration in Vaccine ResearchSchool of Life Sciences, School of Public Health, Xiang An Biomedicine Laboratory, Xiamen University, Xiamen, People's Republic of China
| | - Peiqing He
- State Key Laboratory of Vaccines for Infectious Diseases, National Institute of Diagnostics and Vaccine Development in Infectious Diseases, State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, Collaborative Innovation Center of Biologic Products National Innovation Platform for Industry-Education Intergration in Vaccine ResearchSchool of Life Sciences, School of Public Health, Xiang An Biomedicine Laboratory, Xiamen University, Xiamen, People's Republic of China
| | - Jian Ma
- State Key Laboratory of Vaccines for Infectious Diseases, National Institute of Diagnostics and Vaccine Development in Infectious Diseases, State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, Collaborative Innovation Center of Biologic Products National Innovation Platform for Industry-Education Intergration in Vaccine ResearchSchool of Life Sciences, School of Public Health, Xiang An Biomedicine Laboratory, Xiamen University, Xiamen, People's Republic of China
| | - Rao Fu
- State Key Laboratory of Vaccines for Infectious Diseases, National Institute of Diagnostics and Vaccine Development in Infectious Diseases, State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, Collaborative Innovation Center of Biologic Products National Innovation Platform for Industry-Education Intergration in Vaccine ResearchSchool of Life Sciences, School of Public Health, Xiang An Biomedicine Laboratory, Xiamen University, Xiamen, People's Republic of China
| | - Junping Hong
- State Key Laboratory of Vaccines for Infectious Diseases, National Institute of Diagnostics and Vaccine Development in Infectious Diseases, State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, Collaborative Innovation Center of Biologic Products National Innovation Platform for Industry-Education Intergration in Vaccine ResearchSchool of Life Sciences, School of Public Health, Xiang An Biomedicine Laboratory, Xiamen University, Xiamen, People's Republic of China
| | - Qian Wu
- State Key Laboratory of Vaccines for Infectious Diseases, National Institute of Diagnostics and Vaccine Development in Infectious Diseases, State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, Collaborative Innovation Center of Biologic Products National Innovation Platform for Industry-Education Intergration in Vaccine ResearchSchool of Life Sciences, School of Public Health, Xiang An Biomedicine Laboratory, Xiamen University, Xiamen, People's Republic of China
| | - Hai Yu
- State Key Laboratory of Vaccines for Infectious Diseases, National Institute of Diagnostics and Vaccine Development in Infectious Diseases, State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, Collaborative Innovation Center of Biologic Products National Innovation Platform for Industry-Education Intergration in Vaccine ResearchSchool of Life Sciences, School of Public Health, Xiang An Biomedicine Laboratory, Xiamen University, Xiamen, People's Republic of China
| | - Junyu Chen
- State Key Laboratory of Vaccines for Infectious Diseases, National Institute of Diagnostics and Vaccine Development in Infectious Diseases, State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, Collaborative Innovation Center of Biologic Products National Innovation Platform for Industry-Education Intergration in Vaccine ResearchSchool of Life Sciences, School of Public Health, Xiang An Biomedicine Laboratory, Xiamen University, Xiamen, People's Republic of China
| | - Chenghao Huang
- State Key Laboratory of Vaccines for Infectious Diseases, National Institute of Diagnostics and Vaccine Development in Infectious Diseases, State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, Collaborative Innovation Center of Biologic Products National Innovation Platform for Industry-Education Intergration in Vaccine ResearchSchool of Life Sciences, School of Public Health, Xiang An Biomedicine Laboratory, Xiamen University, Xiamen, People's Republic of China.
| | - Tianying Zhang
- State Key Laboratory of Vaccines for Infectious Diseases, National Institute of Diagnostics and Vaccine Development in Infectious Diseases, State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, Collaborative Innovation Center of Biologic Products National Innovation Platform for Industry-Education Intergration in Vaccine ResearchSchool of Life Sciences, School of Public Health, Xiang An Biomedicine Laboratory, Xiamen University, Xiamen, People's Republic of China.
| | - Quan Yuan
- State Key Laboratory of Vaccines for Infectious Diseases, National Institute of Diagnostics and Vaccine Development in Infectious Diseases, State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, Collaborative Innovation Center of Biologic Products National Innovation Platform for Industry-Education Intergration in Vaccine ResearchSchool of Life Sciences, School of Public Health, Xiang An Biomedicine Laboratory, Xiamen University, Xiamen, People's Republic of China.
| | - Jun Zhang
- State Key Laboratory of Vaccines for Infectious Diseases, National Institute of Diagnostics and Vaccine Development in Infectious Diseases, State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, Collaborative Innovation Center of Biologic Products National Innovation Platform for Industry-Education Intergration in Vaccine ResearchSchool of Life Sciences, School of Public Health, Xiang An Biomedicine Laboratory, Xiamen University, Xiamen, People's Republic of China.
| | - Yixin Chen
- State Key Laboratory of Vaccines for Infectious Diseases, National Institute of Diagnostics and Vaccine Development in Infectious Diseases, State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, Collaborative Innovation Center of Biologic Products National Innovation Platform for Industry-Education Intergration in Vaccine ResearchSchool of Life Sciences, School of Public Health, Xiang An Biomedicine Laboratory, Xiamen University, Xiamen, People's Republic of China.
| | - Ningshao Xia
- State Key Laboratory of Vaccines for Infectious Diseases, National Institute of Diagnostics and Vaccine Development in Infectious Diseases, State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, Collaborative Innovation Center of Biologic Products National Innovation Platform for Industry-Education Intergration in Vaccine ResearchSchool of Life Sciences, School of Public Health, Xiang An Biomedicine Laboratory, Xiamen University, Xiamen, People's Republic of China.
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16
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Ithurbide M, Wang H, Fassier T, Li Z, Pires J, Larsen T, Cao J, Rupp R, Friggens NC. Multivariate analysis of milk metabolite measures shows potential for deriving new resilience phenotypes. J Dairy Sci 2023; 106:8072-8086. [PMID: 37268569 DOI: 10.3168/jds.2023-23332] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2023] [Accepted: 04/25/2023] [Indexed: 06/04/2023]
Abstract
In a context of growing interest in breeding more resilient animals, a noninvasive indicator of resilience would be very valuable. We hypothesized that the time-course of concentrations of several milk metabolites through a short-term underfeeding challenge could reflect the variation of resilience mechanisms to such a challenge. We submitted 138 one-year-old primiparous goats, selected for extreme functional longevity (i.e., productive longevity corrected for milk yield [60 low longevity line goats and 78 high longevity line goats]), to a 2-d underfeeding challenge during early lactation. We measured the concentration of 13 milk metabolites and the activity of 1 enzyme during prechallenge, challenge, and recovery periods. Functional principal component analysis summarized the trends of milk metabolite concentration over time efficiently without preliminary assumptions concerning the shapes of the curves. We first ran a supervised prediction of the longevity line of the goats based on the milk metabolite curves. The partial least square analysis could not predict the longevity line accurately. We thus decided to explore the large overall variability of milk metabolite curves with an unsupervised clustering. The large year × facility effect on the metabolite concentrations was precorrected for. This resulted in 3 clusters of goats defined by different metabolic responses to underfeeding. The cluster that showed higher β-hydroxybutyrate, cholesterol, and triacylglycerols increase during the underfeeding challenge was associated with poorer survival compared with the other 2 clusters. These results suggest that multivariate analysis of noninvasive milk measures show potential for deriving new resilience phenotypes.
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Affiliation(s)
- M Ithurbide
- GenPhySE, Université de Toulouse, INRAE, Castanet Tolosan, France 31326.
| | - H Wang
- Department of Statistics and Actuarial Science, Simon Fraser University, Burnaby BC, Canada V5A 1S6
| | - T Fassier
- Domaine de Bourges, INRAE, Osmoy, France 78910
| | - Z Li
- Department of Statistics and Actuarial Science, Simon Fraser University, Burnaby BC, Canada V5A 1S6
| | - J Pires
- INRAE, Université Clermont Auvergne, Vetagro Sup, UMR Herbivores, Saint-Genès-Champanelle, France 63122
| | - T Larsen
- Department of Animal Science, Aarhus University, 8830 Tjele, Denmark
| | - J Cao
- Department of Statistics and Actuarial Science, Simon Fraser University, Burnaby BC, Canada V5A 1S6
| | - R Rupp
- GenPhySE, Université de Toulouse, INRAE, Castanet Tolosan, France 31326
| | - N C Friggens
- UMR 0791 Modélisation Systémique Appliquée aux Ruminants, INRAE, AgroParisTech, Université Paris-Saclay, 75005 Paris, France
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17
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Song Y, Yoon DH, Yang H, Cao J, Ji D, Koh Y, Jing H, Eom H, Kwak J, Lee W, Lee J, Shin H, Jin J, Wang M, Yang Z, Kim WS, Zhu J. Phase I dose escalation and expansion study of golidocitinib, a highly selective JAK1 inhibitor, in relapsed or refractory peripheral T-cell lymphomas. Ann Oncol 2023; 34:1055-1063. [PMID: 37673210 DOI: 10.1016/j.annonc.2023.08.013] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2023] [Revised: 08/09/2023] [Accepted: 08/22/2023] [Indexed: 09/08/2023] Open
Abstract
BACKGROUND Relapsed or refractory peripheral T-cell lymphomas (r/r PTCLs) are a group of rare and aggressive diseases that lack effective therapies. Constitutive activation of the Janus kinase (JAK)/signal transducer and activator of transcription (STAT) pathway is reported to be associated with PTCLs. Golidocitinib is an oral, potent JAK1 selective inhibitor evaluated in a phase I/II multinational study in patients with r/r PTCLs. PATIENTS AND METHODS Patients with r/r PTCLs were eligible. The primary objectives were to assess safety and tolerability of golidocitinib and to define its recommended phase II dose (RP2D). The secondary objectives were to evaluate its antitumor activity and pharmacokinetics (PK). RESULTS A total of 51 patients were enrolled and received golidocitinib treatment at 150 or 250 mg once daily (QD). The median prior lines of therapies were 2 (range: 1-8). Golidocitinib was tolerated at both doses tested, while a higher incidence of serious adverse events and dose modifications at 250 mg were observed. The most common grade ≥3 drug-related treatment-emergent adverse events were neutropenia (27.5%) and thrombocytopenia (11.8%). An objective response rate of 39.2% and a complete response rate of 21.6% were observed. With median follow-up time of 14.7 and 15.9 months, the median duration of response (DoR) and progression-free survival were 8.0 and 3.3 months, respectively. Based on these data, 150 mg QD was defined as the RP2D. Golidocitinib demonstrated a favorable PK profile as an oral agent. Biomarker analysis suggested a potential correlation between JAK/STAT pathway aberrations and clinical activity of golidocitinib. CONCLUSIONS In this phase I study, golidocitinib demonstrated an acceptable safety profile and encouraging antitumor efficacy in heavily pretreated patients with r/r PTCLs. These results support the initiation of the multinational pivotal study in patients with r/r PTCLs.
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Affiliation(s)
- Y Song
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Department of Lymphoma, Peking University Cancer Hospital and Institute, Beijing, China
| | - D H Yoon
- Department of Oncology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, South Korea
| | - H Yang
- Department of Lymphoma, Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Hangzhou
| | - J Cao
- Department of Medical Oncology, Fudan University Shanghai Cancer Center, Shanghai, China
| | - D Ji
- Department of Medical Oncology, Fudan University Shanghai Cancer Center, Shanghai, China
| | - Y Koh
- Department of Internal Medicine, Division of Hematology and Medical Oncology, Seoul National University Hospital, Seoul, South Korea
| | - H Jing
- Department of Hematology and Lymphoma Research Center, Peking University Third Hospital, Beijing, China
| | - H Eom
- Hematology-Oncology Clinic, National Cancer Center, Goyang
| | - J Kwak
- Department of Internal Medicine, Chonbuk National University Medical School, Jeonju
| | - W Lee
- Department of Hematology-Oncology, Inje University College of Medicine, Busan Paik Hospital, Busan
| | - J Lee
- Division of Hematology-Oncology, Department of Internal Medicine, Seoul National University Bundang Hospital, Seongnam
| | - H Shin
- Division of Hematology-Oncology, Department of Internal Medicine, Medical Research Institute, Pusan National University Hospital, Pusan National University School of Medicine, Busan, South Korea
| | - J Jin
- Department of Hematology, The First Affiliated Hospital, Zhejiang University College of Medicine, Hangzhou
| | - M Wang
- Dizal Pharmaceutical, Jiangsu, China
| | - Z Yang
- Dizal Pharmaceutical, Jiangsu, China
| | - W S Kim
- Division of Hematology and Oncology, Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea.
| | - J Zhu
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Department of Lymphoma, Peking University Cancer Hospital and Institute, Beijing, China.
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18
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Long SY, Shang L, Shi H, Zhao S, Cao J, He Y. The Future Landscape of Endothelial Cells Research in Psoriasis: Bibliometric Analysis and Literature Review. Clin Cosmet Investig Dermatol 2023; 16:3107-3120. [PMID: 37927385 PMCID: PMC10624204 DOI: 10.2147/ccid.s435085] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2023] [Accepted: 10/24/2023] [Indexed: 11/07/2023]
Abstract
Background Psoriasis is a global health concern as a chronic inflammatory skin disease. Endothelial dysfunction has been implicated in psoriasis pathogenesis. Objective This study aims to explore the scientific literature on the relationship between psoriasis and endothelial cells using bibliometric analysis, identifying research trends and public interest in this topic. Methods We analyzed articles on the topic of endothelial cells and psoriasis in the Web of Science (WoS) Core Collection from 1987 to 2022, examining their distribution by publication year, country, organization, author, and journal. We used bibliometric software, including CiteSpace and R package bibliometrix, to visualize co-authorship relations, keyword citation burst analysis, co citation networks, keyword time zone map, burst references and cluster analysis. Results Our analysis included 993 publications. The bibliometric analysis revealed a steady increase in the number of publications on psoriasis and endothelial cells over the past decade. The United States was the leading contributor to this field. The Journal of Investigative Dermatology was the most high-yield publication journal. Burst references analysis identified key articles that have significantly influenced the field, including studies on the role of endothelial dysfunction in psoriasis pathogenesis and the association between psoriasis severity and cardiovascular outcomes. 9 clusters were grouped in the key-word citation network. "Expression", "inflammation", "endothelial growth factor" and "angiogenesis" were the research focuses, while "cardiovascular disease", "atherosclerosis", "endothelial dysfunction", and "oxidative stress" might be the future research hotspots. Conclusion This bibliometric analysis sheds light on the growing acknowledgement of the involvement of endothelial cells in psoriasis, with the United States taking the lead. It also emphasizes the necessity for additional research to unravel the underlying mechanisms connecting psoriasis, endothelial dysfunction, and cardiovascular comorbidities. Ultimately, this research will contribute to the development of enhanced management strategies for psoriasis patients.
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Affiliation(s)
- Si-Yu Long
- Department of Dermatology, Beijing Chao-Yang Hospital, Capital Medical University, National Clinical Research Center for Skin and Immune Diseases, Beijing, People’s Republic of China
| | - Lin Shang
- Department of Dermatology, Beijing Chao-Yang Hospital, Capital Medical University, National Clinical Research Center for Skin and Immune Diseases, Beijing, People’s Republic of China
| | - Huijuan Shi
- Department of Dermatology, Beijing Chao-Yang Hospital, Capital Medical University, National Clinical Research Center for Skin and Immune Diseases, Beijing, People’s Republic of China
| | - Siqi Zhao
- Department of Dermatology, Beijing Chao-Yang Hospital, Capital Medical University, National Clinical Research Center for Skin and Immune Diseases, Beijing, People’s Republic of China
| | - Jiali Cao
- Department of Dermatology, Beijing Chao-Yang Hospital, Capital Medical University, National Clinical Research Center for Skin and Immune Diseases, Beijing, People’s Republic of China
| | - Yanling He
- Department of Dermatology, Beijing Chao-Yang Hospital, Capital Medical University, National Clinical Research Center for Skin and Immune Diseases, Beijing, People’s Republic of China
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Wang G, Cao J, Gui M, Huang P, Zhang L, Qi R, Chen R, Lin L, Han Q, Lin Y, Chen T, He P, Ma J, Fu R, Hong J, Wu Q, Yu H, Chen J, Huang C, Zhang T, Yuan Q, Zhang J, Chen Y, Xia N. The potential of swine pseudorabies virus attenuated vaccine for oncolytic therapy against malignant tumors. J Exp Clin Cancer Res 2023; 42:284. [PMID: 37891570 PMCID: PMC10604416 DOI: 10.1186/s13046-023-02848-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2023] [Accepted: 10/01/2023] [Indexed: 10/29/2023] Open
Abstract
BACKGROUND Oncolytic viruses are now well recognized as potential immunotherapeutic agents against cancer. However, the first FDA-approved oncolytic herpes simplex virus 1 (HSV-1), T-VEC, showed limited benefits in some patients in clinical trials. Thus, the identification of novel oncolytic viruses that can strengthen oncolytic virus therapy is warranted. Here, we identified a live-attenuated swine pseudorabies virus (PRV-LAV) as a promising oncolytic agent with broad-spectrum antitumor activity in vitro and in vivo. METHODS PRV cytotoxicity against tumor cells and normal cells was tested in vitro using a CCK8 cell viability assay. A cell kinase inhibitor library was used to screen for key targets that affect the proliferation of PRV-LAV. The potential therapeutic efficacy of PRV-LAV was tested against syngeneic tumors in immunocompetent mice, and against subcutaneous xenografts of human cancer cell lines in nude mice. Cytometry by time of flight (CyTOF) and flow cytometry were used to uncover the immunological mechanism of PRV-LAV treatment in regulating the tumor immune microenvironment. RESULTS Through various tumor-specific analyses, we show that PRV-LAV infects cancer cells via the NRP1/EGFR signaling pathway, which is commonly overexpressed in cancer. Further, we show that PRV-LAV kills cancer cells by inducing endoplasmic reticulum (ER) stress. Moreover, PRV-LAV is responsible for reprogramming the tumor microenvironment from immunologically naïve ("cold") to inflamed ("hot"), thereby increasing immune cell infiltration and restoring CD8+ T cell function against cancer. When delivered in combination with immune checkpoint inhibitors (ICIs), the anti-tumor response is augmented, suggestive of synergistic activity. CONCLUSIONS PRV-LAV can infect cancer cells via NRP1/EGFR signaling and induce cancer cells apoptosis via ER stress. PRV-LAV treatment also restores CD8+ T cell function against cancer. The combination of PRV-LAV and immune checkpoint inhibitors has a significant synergistic effect. Overall, these findings point to PRV-LAV as a serious potential candidate for the treatment of NRP1/EGFR pathway-associated tumors.
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Affiliation(s)
- Guosong Wang
- State Key Laboratory of Vaccines for Infectious Diseases, National Institute of Diagnostics and Vaccine Development in Infectious Diseases, State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, Collaborative Innovation Center of Biologic ProductsNational Innovation Platform for Industry-Education Intergration in Vaccine ResearchSchool of Life Sciences, School of Public Health, Xiang An Biomedicine Laboratory, Xiamen University, Xiamen, People's Republic of China
| | - Jiali Cao
- Department of Laboratory Medicine, Fujian Key Clinical Specialty of Laboratory Medicine, Women and Children's Hospital, School of Medicine, Xiamen University, Xiamen, People's Republic of China
| | - Mengxuan Gui
- State Key Laboratory of Vaccines for Infectious Diseases, National Institute of Diagnostics and Vaccine Development in Infectious Diseases, State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, Collaborative Innovation Center of Biologic ProductsNational Innovation Platform for Industry-Education Intergration in Vaccine ResearchSchool of Life Sciences, School of Public Health, Xiang An Biomedicine Laboratory, Xiamen University, Xiamen, People's Republic of China
| | - Pengfei Huang
- State Key Laboratory of Vaccines for Infectious Diseases, National Institute of Diagnostics and Vaccine Development in Infectious Diseases, State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, Collaborative Innovation Center of Biologic ProductsNational Innovation Platform for Industry-Education Intergration in Vaccine ResearchSchool of Life Sciences, School of Public Health, Xiang An Biomedicine Laboratory, Xiamen University, Xiamen, People's Republic of China
| | - Liang Zhang
- State Key Laboratory of Vaccines for Infectious Diseases, National Institute of Diagnostics and Vaccine Development in Infectious Diseases, State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, Collaborative Innovation Center of Biologic ProductsNational Innovation Platform for Industry-Education Intergration in Vaccine ResearchSchool of Life Sciences, School of Public Health, Xiang An Biomedicine Laboratory, Xiamen University, Xiamen, People's Republic of China
| | - Ruoyao Qi
- State Key Laboratory of Vaccines for Infectious Diseases, National Institute of Diagnostics and Vaccine Development in Infectious Diseases, State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, Collaborative Innovation Center of Biologic ProductsNational Innovation Platform for Industry-Education Intergration in Vaccine ResearchSchool of Life Sciences, School of Public Health, Xiang An Biomedicine Laboratory, Xiamen University, Xiamen, People's Republic of China
| | - Ruiqi Chen
- State Key Laboratory of Vaccines for Infectious Diseases, National Institute of Diagnostics and Vaccine Development in Infectious Diseases, State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, Collaborative Innovation Center of Biologic ProductsNational Innovation Platform for Industry-Education Intergration in Vaccine ResearchSchool of Life Sciences, School of Public Health, Xiang An Biomedicine Laboratory, Xiamen University, Xiamen, People's Republic of China
| | - Lina Lin
- State Key Laboratory of Vaccines for Infectious Diseases, National Institute of Diagnostics and Vaccine Development in Infectious Diseases, State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, Collaborative Innovation Center of Biologic ProductsNational Innovation Platform for Industry-Education Intergration in Vaccine ResearchSchool of Life Sciences, School of Public Health, Xiang An Biomedicine Laboratory, Xiamen University, Xiamen, People's Republic of China
| | - Qiangyuan Han
- State Key Laboratory of Vaccines for Infectious Diseases, National Institute of Diagnostics and Vaccine Development in Infectious Diseases, State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, Collaborative Innovation Center of Biologic ProductsNational Innovation Platform for Industry-Education Intergration in Vaccine ResearchSchool of Life Sciences, School of Public Health, Xiang An Biomedicine Laboratory, Xiamen University, Xiamen, People's Republic of China
| | - Yanhua Lin
- State Key Laboratory of Vaccines for Infectious Diseases, National Institute of Diagnostics and Vaccine Development in Infectious Diseases, State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, Collaborative Innovation Center of Biologic ProductsNational Innovation Platform for Industry-Education Intergration in Vaccine ResearchSchool of Life Sciences, School of Public Health, Xiang An Biomedicine Laboratory, Xiamen University, Xiamen, People's Republic of China
| | - Tian Chen
- State Key Laboratory of Vaccines for Infectious Diseases, National Institute of Diagnostics and Vaccine Development in Infectious Diseases, State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, Collaborative Innovation Center of Biologic ProductsNational Innovation Platform for Industry-Education Intergration in Vaccine ResearchSchool of Life Sciences, School of Public Health, Xiang An Biomedicine Laboratory, Xiamen University, Xiamen, People's Republic of China
| | - Peiqing He
- State Key Laboratory of Vaccines for Infectious Diseases, National Institute of Diagnostics and Vaccine Development in Infectious Diseases, State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, Collaborative Innovation Center of Biologic ProductsNational Innovation Platform for Industry-Education Intergration in Vaccine ResearchSchool of Life Sciences, School of Public Health, Xiang An Biomedicine Laboratory, Xiamen University, Xiamen, People's Republic of China
| | - Jian Ma
- State Key Laboratory of Vaccines for Infectious Diseases, National Institute of Diagnostics and Vaccine Development in Infectious Diseases, State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, Collaborative Innovation Center of Biologic ProductsNational Innovation Platform for Industry-Education Intergration in Vaccine ResearchSchool of Life Sciences, School of Public Health, Xiang An Biomedicine Laboratory, Xiamen University, Xiamen, People's Republic of China
| | - Rao Fu
- State Key Laboratory of Vaccines for Infectious Diseases, National Institute of Diagnostics and Vaccine Development in Infectious Diseases, State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, Collaborative Innovation Center of Biologic ProductsNational Innovation Platform for Industry-Education Intergration in Vaccine ResearchSchool of Life Sciences, School of Public Health, Xiang An Biomedicine Laboratory, Xiamen University, Xiamen, People's Republic of China
| | - Junping Hong
- State Key Laboratory of Vaccines for Infectious Diseases, National Institute of Diagnostics and Vaccine Development in Infectious Diseases, State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, Collaborative Innovation Center of Biologic ProductsNational Innovation Platform for Industry-Education Intergration in Vaccine ResearchSchool of Life Sciences, School of Public Health, Xiang An Biomedicine Laboratory, Xiamen University, Xiamen, People's Republic of China
| | - Qian Wu
- State Key Laboratory of Vaccines for Infectious Diseases, National Institute of Diagnostics and Vaccine Development in Infectious Diseases, State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, Collaborative Innovation Center of Biologic ProductsNational Innovation Platform for Industry-Education Intergration in Vaccine ResearchSchool of Life Sciences, School of Public Health, Xiang An Biomedicine Laboratory, Xiamen University, Xiamen, People's Republic of China
| | - Hai Yu
- State Key Laboratory of Vaccines for Infectious Diseases, National Institute of Diagnostics and Vaccine Development in Infectious Diseases, State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, Collaborative Innovation Center of Biologic ProductsNational Innovation Platform for Industry-Education Intergration in Vaccine ResearchSchool of Life Sciences, School of Public Health, Xiang An Biomedicine Laboratory, Xiamen University, Xiamen, People's Republic of China
| | - Junyu Chen
- State Key Laboratory of Vaccines for Infectious Diseases, National Institute of Diagnostics and Vaccine Development in Infectious Diseases, State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, Collaborative Innovation Center of Biologic ProductsNational Innovation Platform for Industry-Education Intergration in Vaccine ResearchSchool of Life Sciences, School of Public Health, Xiang An Biomedicine Laboratory, Xiamen University, Xiamen, People's Republic of China
| | - Chenghao Huang
- State Key Laboratory of Vaccines for Infectious Diseases, National Institute of Diagnostics and Vaccine Development in Infectious Diseases, State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, Collaborative Innovation Center of Biologic ProductsNational Innovation Platform for Industry-Education Intergration in Vaccine ResearchSchool of Life Sciences, School of Public Health, Xiang An Biomedicine Laboratory, Xiamen University, Xiamen, People's Republic of China.
| | - Tianying Zhang
- State Key Laboratory of Vaccines for Infectious Diseases, National Institute of Diagnostics and Vaccine Development in Infectious Diseases, State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, Collaborative Innovation Center of Biologic ProductsNational Innovation Platform for Industry-Education Intergration in Vaccine ResearchSchool of Life Sciences, School of Public Health, Xiang An Biomedicine Laboratory, Xiamen University, Xiamen, People's Republic of China.
| | - Quan Yuan
- State Key Laboratory of Vaccines for Infectious Diseases, National Institute of Diagnostics and Vaccine Development in Infectious Diseases, State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, Collaborative Innovation Center of Biologic ProductsNational Innovation Platform for Industry-Education Intergration in Vaccine ResearchSchool of Life Sciences, School of Public Health, Xiang An Biomedicine Laboratory, Xiamen University, Xiamen, People's Republic of China.
| | - Jun Zhang
- State Key Laboratory of Vaccines for Infectious Diseases, National Institute of Diagnostics and Vaccine Development in Infectious Diseases, State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, Collaborative Innovation Center of Biologic ProductsNational Innovation Platform for Industry-Education Intergration in Vaccine ResearchSchool of Life Sciences, School of Public Health, Xiang An Biomedicine Laboratory, Xiamen University, Xiamen, People's Republic of China.
| | - Yixin Chen
- State Key Laboratory of Vaccines for Infectious Diseases, National Institute of Diagnostics and Vaccine Development in Infectious Diseases, State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, Collaborative Innovation Center of Biologic ProductsNational Innovation Platform for Industry-Education Intergration in Vaccine ResearchSchool of Life Sciences, School of Public Health, Xiang An Biomedicine Laboratory, Xiamen University, Xiamen, People's Republic of China.
| | - Ningshao Xia
- State Key Laboratory of Vaccines for Infectious Diseases, National Institute of Diagnostics and Vaccine Development in Infectious Diseases, State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, Collaborative Innovation Center of Biologic ProductsNational Innovation Platform for Industry-Education Intergration in Vaccine ResearchSchool of Life Sciences, School of Public Health, Xiang An Biomedicine Laboratory, Xiamen University, Xiamen, People's Republic of China.
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Sun Q, Qi YK, Qi KM, Yan ZL, Cheng H, Chen W, Zhu F, Sang W, Li DP, Cao J, Shi M, Li ZY, Xu KL. [Observation of liver indexes in patients with relapsed/refractory multiple myeloma treated with CAR-T-cells based on BCMA]. Zhonghua Xue Ye Xue Za Zhi 2023; 44:832-837. [PMID: 38049335 PMCID: PMC10694074 DOI: 10.3760/cma.j.issn.0253-2727.2023.10.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Subscribe] [Scholar Register] [Received: 08/25/2023] [Indexed: 12/06/2023]
Abstract
Objective: To observe the characteristics of the evolution of liver indexes in patients with relapsed/refractory multiple myeloma (RRMM) treated with CAR-T-cells based on BCMA. Methods: Retrospective analysis was performed of patients with RRMM who received an infusion of anti-BCMA CAR-T-cells and anti-BCMA combined with anti-CD19 CAR-T-cells at our center between June 1, 2019, and February 28, 2023. Clinical data were collected to observe the characteristics of changes in liver indexes such as alanine aminotransferase (ALT), aspartate aminotransferase (AST), total bilirubin (TBIL), and direct bilirubin (DBIL) in patients, and its relationship with cytokine-release syndrome (CRS) . Results: Ninety-two patients were included in the analysis, including 41 patients (44.6%) in the group receiving a single infusion of anti-BCMA CAR-T-cells, and 51 patients (55.4%) in the group receiving an infusion of anti-BCMA combined with anti-CD19 CAR-T-cells. After infusing CAR-T-cells, 31 patients (33.7%) experienced changes in liver indexes at or above grade 2, which included 20 patients (21.7%) with changes in one index, five patients (5.4%) with changes in two indexes, and six patients (6.5%) with changes in three or more indexes. The median time of peak values of ALT and AST were d17 and d14, respectively, and the median duration of exceeding grade 2 was 5.0 and 3.5 days, respectively. The median time of peak values of TBIL and DBIL was on d19 and d21, respectively, and the median duration of exceeding grade 2 was 4.0 days, respectively. The median time of onset of CRS was d8, and the peak time of fever was d9. The ALT, AST, and TBIL of patients with CRS were higher than those of patients without CRS (P=0.011, 0.002, and 0.015, respectively). CRS is an independent factor that affects ALT and TBIL levels (OR=19.668, 95% CI 18.959-20.173, P=0.001). The evolution of liver indexes can be reversed through anti-CRS and liver-protection treatments, and no patient died of liver injury. Conclusions: In BCMA-based CAR-T-cell therapy for RRMM, CRS is an important factor causing the evolution of liver indexes. The evolution of liver indexes after CAR-T-cell infusion is transient and reversible after treatment.
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Affiliation(s)
- Q Sun
- Hematology Institute of Xuzhou Medical University, Hematology Department of The Affiliated Hospital of Xuzhou Medical University, Jiangsu Provincial Key Laboratory of Bone Marrow Stem Cells, Xuzhou 221002, China
| | - Y K Qi
- Hematology Institute of Xuzhou Medical University, Hematology Department of The Affiliated Hospital of Xuzhou Medical University, Jiangsu Provincial Key Laboratory of Bone Marrow Stem Cells, Xuzhou 221002, China
| | - K M Qi
- Hematology Institute of Xuzhou Medical University, Hematology Department of The Affiliated Hospital of Xuzhou Medical University, Jiangsu Provincial Key Laboratory of Bone Marrow Stem Cells, Xuzhou 221002, China
| | - Z L Yan
- Hematology Institute of Xuzhou Medical University, Hematology Department of The Affiliated Hospital of Xuzhou Medical University, Jiangsu Provincial Key Laboratory of Bone Marrow Stem Cells, Xuzhou 221002, China
| | - H Cheng
- Hematology Institute of Xuzhou Medical University, Hematology Department of The Affiliated Hospital of Xuzhou Medical University, Jiangsu Provincial Key Laboratory of Bone Marrow Stem Cells, Xuzhou 221002, China
| | - W Chen
- Hematology Institute of Xuzhou Medical University, Hematology Department of The Affiliated Hospital of Xuzhou Medical University, Jiangsu Provincial Key Laboratory of Bone Marrow Stem Cells, Xuzhou 221002, China
| | - F Zhu
- Hematology Institute of Xuzhou Medical University, Hematology Department of The Affiliated Hospital of Xuzhou Medical University, Jiangsu Provincial Key Laboratory of Bone Marrow Stem Cells, Xuzhou 221002, China
| | - W Sang
- Hematology Institute of Xuzhou Medical University, Hematology Department of The Affiliated Hospital of Xuzhou Medical University, Jiangsu Provincial Key Laboratory of Bone Marrow Stem Cells, Xuzhou 221002, China
| | - D P Li
- Hematology Institute of Xuzhou Medical University, Hematology Department of The Affiliated Hospital of Xuzhou Medical University, Jiangsu Provincial Key Laboratory of Bone Marrow Stem Cells, Xuzhou 221002, China
| | - J Cao
- Hematology Institute of Xuzhou Medical University, Hematology Department of The Affiliated Hospital of Xuzhou Medical University, Jiangsu Provincial Key Laboratory of Bone Marrow Stem Cells, Xuzhou 221002, China
| | - M Shi
- Hematology Institute of Xuzhou Medical University, Hematology Department of The Affiliated Hospital of Xuzhou Medical University, Jiangsu Provincial Key Laboratory of Bone Marrow Stem Cells, Xuzhou 221002, China
| | - Z Y Li
- Hematology Institute of Xuzhou Medical University, Hematology Department of The Affiliated Hospital of Xuzhou Medical University, Jiangsu Provincial Key Laboratory of Bone Marrow Stem Cells, Xuzhou 221002, China
| | - K L Xu
- Hematology Institute of Xuzhou Medical University, Hematology Department of The Affiliated Hospital of Xuzhou Medical University, Jiangsu Provincial Key Laboratory of Bone Marrow Stem Cells, Xuzhou 221002, China
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Bi N, Deng L, Hu X, Shayan G, Zhao L, Zhang L, Jiang W, Zhang J, Zhu X, Wang Y, Ge H, Cao J, Lin Q, Chen M, Wang L. 30 Gy vs. 45 Gy Consolidative Thoracic Radiation (cTRT) for Extensive Stage Small Cell Lung Cancer (ES-SCLC): A Multicenter, Randomized, Phase 3 Trial. Int J Radiat Oncol Biol Phys 2023; 117:S56-S57. [PMID: 37784527 DOI: 10.1016/j.ijrobp.2023.06.350] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/04/2023]
Abstract
PURPOSE/OBJECTIVE(S) Consolidative thoracic radiotherapy (cTRT) showed potential benefit to extensive stage small cell lung cancer (ES-SCLC). However, the optimum dose of cTRT is unknown. The purpose of this randomized trial was to compare the effect of 45 Gy in 15 fractions with 30 Gy in 10 fractions cTRT in ES-SCLC. MATERIALS/METHODS This phase III, randomized trial was conducted in 12 public hospitals in China. Eligible patients with pathologically confirmed ES-SCLC who responded to 4-6 cycles of etoposide plus cisplatin (EP) or carboplatin (EC) chemotherapy were randomized 1:1 to receive either 30 Gy in 10 fractions or 45 Gy in 15 fractions cTRT. The primary outcome was 2-year overall survival (OS). Secondary outcomes included 2-year progression-free survival (PFS), 2-year local control (LC) and radiation treatment related toxicity. The primary objective was to detect an OS improvement in 45 Gy cTRT group at 2 years from 13% to 26% assuming a two-sided a = 0.05 and power of 85%, with a planned sample size of 186 patients. This trial was registered with Clinical Trials.gov, number NCT02675088. RESULTS Between January 15, 2016, and September 20, 2022, 90 patients were randomly assigned either 30 Gy in 10 fractions (n = 50) or 45 Gy in 15 fractions (n = 40) cTRT group. Recruitment to the trial closed early due to slow accrual since first-line chemoimmunotherapy has become the new standard of care for ES-SCLC. The median age of patients was 58 years, 87.8% were male, 76.7% had a smoking history, 95.6% received IMRT, and 58.9% received prophylactic cranial irradiation. At a median follow-up of 39.9 months (IQR 27.2-59.2), there was no significant difference in the 2-year OS between the 45 Gy group and the 30 Gy group, at 43.4% (95% CI 29.3%-64.3%) and 40.0% (95% CI 27.9%-59.1%), respectively (log-rank p = 0.62; HR 1.13 [95% CI 0.69-1.84]). The 2-year PFS was 12.1% (95% CI 4.3%-33.8%) in the 45 Gy group and 9.0% (95% CI 3.2%-25.2%) in the 30 Gy group (log-rank p = 0.25, HR 0.76(95% CI [0.478-1.22]). There were also no significant differences in locoregional recurrence free survival (log-rank p = 0.75; HR 0.888 [95% CI 0.423-1.863]) and distant metastasis free survival (log-rank p = 0.95; HR 1.015 [95% CI 0.624-1.651]) between two groups. No grade 5 toxicity was observed in both groups. Patients treated with higher cTRT dose presented with increased incidence of grade 3+ radiation pneumonitis (10% vs 2%) and hematological toxicity (20% vs 12.5%). CONCLUSION This randomized trial did not find a higher probability of survival improvement in patients with ES-SCLC receiving cTRT of 45 Gy in 15 fractions compared with 30 Gy in 10 fractions. In contrast, there was an increase in toxicity, especially radiation pneumonitis. Additional randomized studies investigating the role of cTRT in ES-SCLC after a response to chemoimmunotherapy are warranted.
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Affiliation(s)
- N Bi
- Department of Radiation Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - L Deng
- Department of Radiation Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - X Hu
- Department of Radiation Oncology, Zhejiang Cancer Hospital, Institute of Basic Medical Sciences and Cancer Research, Chinese Academy of Sciences, Zhejiang Provincial Key Laboratory of Radiation Oncology, Hangzhou, China
| | - G Shayan
- Department of Radiation Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - L Zhao
- Department of Radiation Oncology, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin, Tianjin's Clinical Research Center for Cancer, Tianjin, China
| | - L Zhang
- Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - W Jiang
- Department of Radiation Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital & Shenzhen Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Shenzhen, China, Shenzhen, China
| | - J Zhang
- Shanghai Medical College, Fudan University, Shanghai, China
| | - X Zhu
- Jiangsu Cancer Hospital & Jiangsu Institute of Cancer Research & Nanjing Medical University Affiliated Cancer Hospital, Nanjing, China
| | - Y Wang
- Department of Radiotherapy, Air Force Medical Center, Beijing, China
| | - H Ge
- The Affiliated Cancer Hospital of Zhengzhou University, Zhengzhou, China
| | - J Cao
- Shanxi Province Cancer Hospital/ Shanxi Hospital Affiliated to Cancer Hospital, Chinese Academy of Medical Sciences/Cancer Hospital Affiliated to Shanxi Medical University, Taiyuan, China
| | - Q Lin
- The First Affiliated Hospital of Xiamen University, Xiamen, China
| | - M Chen
- Zhejiang Cancer Hospital, Hangzhou, China; Department of Radiation Oncology, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - L Wang
- Department of Radiation Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China, Beijing, China; Department of Radiation Oncology, National Cancer Center/Cancer Hospital & Shenzhen Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Shenzhen, China
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Cao J, Qi X, Wang N, Chen Y, Xie B, Ma C, Chen Z, Xiong W. Ceruloplasmin regulating fibrosis in orbital fibroblasts provides a novel therapeutic target for Graves' orbitopathy. J Endocrinol Invest 2023; 46:2005-2016. [PMID: 36849849 DOI: 10.1007/s40618-023-02033-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/13/2022] [Accepted: 02/03/2023] [Indexed: 03/01/2023]
Abstract
PURPOSE In diagnosing the pathogenesis of Graves' orbitopathy (GO), there is a growing interest in fibrosis generated by orbital fibroblasts (OFs); nevertheless, the involvement of ceruloplasmin (CP) in OFs remains unknown. METHODS Differentially expressed genes (DEGs) were identified through bioinformatic analysis. OFs were isolated from orbital tissue and identified with immunofluorescent staining. The levels of DEGs were validated in GO tissue samples and TGF-β-challenged OFs, and CP was selected for the following laboratory investigations. CP overexpression or knockdown was achieved, and cell viability and fibrosis-associated proteins were investigated to assess the cell phenotype and function. Signaling pathways were subsequently investigated to explore the mechanism of CP function in OFs. RESULTS CP and cathepsin C (CTSC) are two overlapped DEGs in GSE58331 and GSE105149. OFs were isolated and identified through fibrotic biomarkers. CP and CTSC were downregulated in GO tissue samples and TGF-β-challenged OFs. CP overexpression or knockdown was achieved in OFs by transducing a CP overexpression vector or small interfering RNA against CP (si1-CP or si2-CP) and verified using a qRT-PCR. CP overexpression inhibited cell viability and reduced the levels of α-SMA, vimentin, fibronectin, and collagen I, whereas CP knockdown exerted opposite effects on OFs. CP overexpression inhibited the phosphorylation of Smad3, Erk1/2, p38, JNK, and AKT; conversely, CP knockdown exerted opposite effects on the phosphorylation of factors mentioned above. CONCLUSION CP was downregulated in GO and suppressed the expression of fibrosis-associated proteins in both GO and normal OFs. CP might serve as a promising therapeutic agent in the treatment regimens for GO.
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Affiliation(s)
- J Cao
- Department of Ophthalmology, Third Xiangya Hospital, Central South University, Changsha, 410013, Hunan, China
| | - X Qi
- Department of Ophthalmology, Second Xiangya Hospital, Central South University, Changsha, China
| | - N Wang
- Department of Ophthalmology, Third Xiangya Hospital, Central South University, Changsha, 410013, Hunan, China
| | - Y Chen
- Department of Ophthalmology, Third Xiangya Hospital, Central South University, Changsha, 410013, Hunan, China
| | - B Xie
- Department of Ophthalmology, Third Xiangya Hospital, Central South University, Changsha, 410013, Hunan, China
| | - C Ma
- Department of Ophthalmology, Third Xiangya Hospital, Central South University, Changsha, 410013, Hunan, China
| | - Z Chen
- Department of Ophthalmology, Third Xiangya Hospital, Central South University, Changsha, 410013, Hunan, China
| | - W Xiong
- Department of Ophthalmology, Third Xiangya Hospital, Central South University, Changsha, 410013, Hunan, China.
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Shiau C, Cao J, Gregory M, Kim Y, He S, Reeves J, Wang S, Lester NA, Su J, Wang PL, Beechem J, Hong TS, Wo JY, Ting D, Hemberg M, Hwang WL. Intercellular Mechanisms of Therapeutic Resistance at the Tumor-Stromal Interface Using Ultra High-Plex Single-Cell Spatial Transcriptomics and Genetically-Engineered Tumoroids. Int J Radiat Oncol Biol Phys 2023; 117:S101-S102. [PMID: 37784270 DOI: 10.1016/j.ijrobp.2023.06.056] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/04/2023]
Abstract
PURPOSE/OBJECTIVE(S) There is a major gap in knowledge regarding how intercellular interactions in the tumor microenvironment (TME) mediate therapeutic resistance. Achievement of this goal has been limited by a lack of (1) spatial context in dissociated single-cell methods; (2) single-cell resolution in spatial profiling approaches; (3) high quality data and yield with FFPE patient specimens; and (4) computational methods for ligand-receptor analyses that consider both gene expression and spatial coordinates. MATERIALS/METHODS We developed an innovative spatial biology paradigm that combines cutting-edge experimental and computational methods to enable high-resolution, spatially-guided discovery of critical mediators of therapeutic resistance. We applied this approach to dissect the single-cell spatial transcriptomic landscape of untreated vs. chemoradiotherapy-treated primary human pancreatic ductal adenocarcinoma (PDAC; n = 21) using ultra-high plex spatial molecular imaging (SMI) optimized for high-sensitivity, subcellular detection of up to 6000 gene transcripts in FFPE sections-an order of magnitude greater than contemporary methods. RESULTS We recovered over 1,000,000 high-quality single cells in situ representing more than 20 distinct cell types, including epithelial, immune, endothelial, endocrine, and diverse stromal cells. We developed an optimal transport-based computational method to infer cell-cell communication at the cancer-stromal interface. Treatment with chemoradiotherapy was associated with the largest increase in fibroblast-malignant interactions. Comparing the SMI data with orthogonal single-nucleus RNA-sequencing and digital spatial profiling data, we identified CLCF1-CNTFR as the fibroblast-malignant interaction most associated with resistance to chemoradiotherapy in PDAC. CLCF1 is a gp130-family cytokine that activates Jak-STAT signaling and acts as a potent neurotrophic factor. Notably, the CLCF1-CNTRF (fibroblast-malignant) interaction has prominent pro-oncogenic effects in lung adenocarcinoma and an engineered CNTFR decoy receptor with therapeutic potential has been developed. To functionally validate the role of the CLCF1-CNTFR (fibroblast-malignant) interaction in mediating resistance to cytotoxic therapy, we created CRISPR-engineered cancer-fibroblast tumoroids and modulated expression of this ligand-receptor pair. Pancreatic cancer cell viability in the presence of 5-fluorouracil was better maintained with increased CLCF1-CNTFR signaling. CONCLUSION In this study, we integrated ultra high-plex single-cell spatial transcriptomics, optimal transport ligand-receptor predictions, and genetically-engineered stromal tumoroids to identify and validate CLCF1-CNTFR as an important intercellular mechanism of resistance to chemoradiotherapy in PDAC-pioneering a paradigm for translating single-cell spatial biology to clinical oncology.
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Affiliation(s)
- C Shiau
- Massachusetts General Hospital, Boston, MA
| | - J Cao
- Brigham and Women's Hospital, Boston, MA
| | - M Gregory
- Nanostring Technologies, Seattle, WA
| | - Y Kim
- Nanostring Technologies, Seattle, WA
| | - S He
- Nanostring Technologies, Seattle, WA
| | - J Reeves
- Nanostring Technologies, Seattle, WA
| | - S Wang
- Columbia University, New York, NY
| | - N A Lester
- Massaschusetts General Hospital, Boston, MA
| | - J Su
- Massachusetts General Hospital, BOSTON, MA
| | - P L Wang
- Massaschusetts General Hospital, Boston, MA
| | - J Beechem
- Nanostring Technologies, Seattle, WA
| | - T S Hong
- Department of Radiation Oncology, Massachusetts General Hospital, Harvard Medical School, Boston, MA
| | - J Y Wo
- Newton-Wellesley Hospital, Newton, MA
| | - D Ting
- Massachusetts General Hospital Cancer Center, Harvard Medical School, Boston, MA
| | - M Hemberg
- Brigham and Women's Hospital, Boston, MA
| | - W L Hwang
- Broad Institute of MIT and Harvard, Cambridge, MA
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Liu Y, Xing Z, Geng C, Liu Y, Cao J, Yang Y, Pan T, Yu L. Use of peripheral blood eosinophils to guide post-operative glucocorticoid therapy in patients with chronic rhinosinusitis with nasal polyps: a randomised, controlled trial. J Laryngol Otol 2023; 137:890-901. [PMID: 36444128 DOI: 10.1017/s0022215122002481] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
OBJECTIVE This study aimed to explore the utility of the eosinophil percentage in peripheral blood for guiding post-operative glucocorticoid therapy in patients with chronic rhinosinusitis with nasal polyps. METHODS Forty-four patients with chronic rhinosinusitis with nasal polyps underwent functional endoscopic sinus surgery and were randomly divided into two groups. Patients in the standard treatment group used oral and nasal spray glucocorticoids. In the biomarker treatment group, patients with peripheral blood eosinophil percentage values less than 3.05 per cent did not receive glucocorticoid treatment, whereas patients with values 3.05 per cent or above were part of the standard treatment group. Visual Analogue Scale, Sino-Nasal Outcome Test-22 scores, endoscopic Lund-Kennedy scores, eosinophils, interleukin-5 and eosinophil cationic protein in peripheral blood, and nasal secretions were measured. RESULTS After functional endoscopic sinus surgery, the Visual Analogue Scale, Sino-Nasal Outcome Test-22 and Lund-Kennedy scores were significantly reduced in both groups; there were no significant differences in those indicators between the groups during the three follow-up visits. CONCLUSION Peripheral blood eosinophil percentage offers a potential biomarker to guide post-operative glucocorticoid therapy in patients with chronic rhinosinusitis with nasal polyps.
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Affiliation(s)
- Y Liu
- Department of Otorhinolaryngology, Head and Neck Surgery, Peking University People's Hospital, Peking University, Beijing, China
| | - Z Xing
- Department of Otorhinolaryngology, Head and Neck Surgery, Peking University People's Hospital, Peking University, Beijing, China
| | - C Geng
- Department of Otorhinolaryngology, Head and Neck Surgery, Peking University People's Hospital, Peking University, Beijing, China
| | - Y Liu
- Department of Otorhinolaryngology, Head and Neck Surgery, Peking University People's Hospital, Peking University, Beijing, China
| | - J Cao
- Department of Otorhinolaryngology, Head and Neck Surgery, Beijing Friendship Hospital, Capital Medical University, Beijing, China
| | - Y Yang
- Department of Otorhinolaryngology, Head and Neck Surgery, Peking University People's Hospital, Peking University, Beijing, China
| | - T Pan
- Department of Otorhinolaryngology, Head and Neck Surgery, Peking University People's Hospital, Peking University, Beijing, China
| | - L Yu
- Department of Otorhinolaryngology, Head and Neck Surgery, Peking University People's Hospital, Peking University, Beijing, China
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Cao J, Wang Z, Zhu M, Huang Y, Jin Z, Xiong Z. Low-density lipoprotein cholesterol and risk of hepatocellular carcinoma: a Mendelian randomization and mediation analysis. Lipids Health Dis 2023; 22:110. [PMID: 37525197 PMCID: PMC10388495 DOI: 10.1186/s12944-023-01877-1] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2023] [Accepted: 07/21/2023] [Indexed: 08/02/2023] Open
Abstract
BACKGROUND A previous study demonstrated that low-density lipoprotein cholesterol (LDL-C) is associated with hepatocellular carcinoma (HCC); however, the causality between them has not been proven due to conflicting research results and the interference of confounders. This study utilized Mendelian randomization (MR) to investigate the causal relationship between LDL-C and HCC and identify the mediating factors. METHODS LDL-C, HCC, and coronary artery disease (CAD) genome-wide association study (GWAS) data were obtained from a public database. To investigate causality, inverse variance weighting (IVW) was the main analysis approach. MR‒Egger, simple mode, weighted median (WM), and weighted mode were employed as supplementary analytic methods. In addition, horizontal pleiotropy and heterogeneity were tested. To evaluate the stability of the MR results, a "leave-one-out" approach was used. Multivariate MR (MVMR) was utilized to correct the confounders that might affect causality, and mediation analysis was used to investigate the potential mediating effects. Finally, we used HCC risk to infer the reverse causality with LDL-C level. RESULTS Random effects IVW results were (LDL-C-HCC: odds ratio (OR) = 0.703, 95% confidence interval (CI) = [0.508, 0.973], P = 0.034; CAD-HCC: OR = 0.722, 95% CI = [0.645, 0.808], P = 1.50 × 10-8; LDL-C-CAD: OR = 2.103, 95% CI = [1.862, 2.376], P = 5.65 × 10-33), demonstrating a causal link between LDL-C levels and a lower risk of HCC. Through MVMR, after mutual correction, the causal effect of LDL-C and CAD on HCC remained significant (P < 0.05). Through mediation analysis, it was proven that CAD mediated the causative connection between LDL-C and HCC, and the proportion of mediating effect on HCC was 58.52%. Reverse MR showed that HCC could affect LDL-C levels with a negative correlation (ORIVW = 0.979, 95% CI = [0.961, 0.997], P = 0.025). CONCLUSION This MR study confirmed the causal effect between LDL-C levels and HCC risk, with CAD playing a mediating role. It may provide a new view on HCC occurrence and development mechanisms, as well as new metabolic intervention targets for treatment.
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Affiliation(s)
- Jiali Cao
- Department of Gastroenterology, Liyuan Hospital, Tongji Medical College of Huazhong University of Science and Technology, Wuhan, 430077, China
| | - Ziwen Wang
- Department of Gastroenterology, Liyuan Hospital, Tongji Medical College of Huazhong University of Science and Technology, Wuhan, 430077, China
| | - Mengpei Zhu
- Department of Gastroenterology, Liyuan Hospital, Tongji Medical College of Huazhong University of Science and Technology, Wuhan, 430077, China
| | - Yumei Huang
- Department of Gastroenterology, Liyuan Hospital, Tongji Medical College of Huazhong University of Science and Technology, Wuhan, 430077, China
| | - Ze Jin
- Department of Gastroenterology, Liyuan Hospital, Tongji Medical College of Huazhong University of Science and Technology, Wuhan, 430077, China
| | - Zhifan Xiong
- Department of Gastroenterology, Liyuan Hospital, Tongji Medical College of Huazhong University of Science and Technology, Wuhan, 430077, China.
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Zhang Y, Cao Y, Yang K, Wang W, Yang M, Chai L, Gu J, Li M, Lu Y, Zhou H, Zhu G, Cao J, Lu G. [Risk predictive models of healthcare-seeking delay among imported malaria patients in Jiangsu Province based on the machine learning]. Zhongguo Xue Xi Chong Bing Fang Zhi Za Zhi 2023; 35:225-235. [PMID: 37455092 DOI: 10.16250/j.32.1374.2022290] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Subscribe] [Scholar Register] [Indexed: 07/18/2023]
Abstract
OBJECTIVE To create risk predictive models of healthcare-seeking delay among imported malaria patients in Jiangsu Province based on machine learning algorithms, so as to provide insights into early identification of imported malaria cases in Jiangsu Province. METHODS Case investigation, first symptoms and time of initial diagnosis of imported malaria patients in Jiangsu Province in 2019 were captured from Infectious Disease Report Information Management System and Parasitic Disease Prevention and Control Information Management System of Chinese Center for Disease Control and Prevention. The risk predictive models of healthcare-seeking delay among imported malaria patients were created with the back propagation (BP) neural network model, logistic regression model, random forest model and Bayesian model using thirteen factors as independent variables, including occupation, species of malaria parasite, main clinical manifestations, presence of complications, severity of disease, age, duration of residing abroad, frequency of malaria parasite infections abroad, incubation period, level of institution at initial diagnosis, country of origin, number of individuals travelling with patients and way to go abroad, and time of healthcare-seeking delay as a dependent variable. Logistic regression model was visualized using a nomogram, and the nomogram was evaluated using calibration curves. In addition, the efficiency of the four models for prediction of risk of healthcare-seeking delay among imported malaria patients was evaluated using the area under curve (AUC) of receiver operating characteristic curve (ROC). The importance of each characteristic was quantified and attributed by using SHAP to examine the positive and negative effects of the value of each characteristic on the predictive efficiency. RESULTS A total of 244 imported malaria patients were enrolled, including 100 cases (40.98%) with the duration from onset of first symptoms to time of initial diagnosis that exceeded 24 hours. Logistic regression analysis identified a history of malaria parasite infection [odds ratio (OR) = 3.075, 95% confidential interval (CI): (1.597, 5.923)], long incubation period [OR = 1.010, 95% CI: (1.001, 1.018)] and seeking healthcare in provincial or municipal medical facilities [OR = 12.550, 95% CI: (1.158, 135.963)] as risk factors for delay in seeking healthcare among imported malaria cases. BP neural network modeling showed that duration of residing abroad, incubation period and age posed great impacts on delay in healthcare-seek among imported malaria patients. Random forest modeling showed that the top five factors with the greatest impact on healthcare-seeking delay included main clinical manifestations, the way to go abroad, incubation period, duration of residing abroad and age among imported malaria patients, and Bayesian modeling revealed that the top five factors affecting healthcare-seeking delay among imported malaria patients included level of institutions at initial diagnosis, age, country of origin, history of malaria parasite infection and individuals travelling with imported malaria patients. ROC curve analysis showed higher overall performance of the BP neural network model and the logistic regression model for prediction of the risk of healthcare-seeking delay among imported malaria patients (Z = 2.700 to 4.641, all P values < 0.01), with no statistically significant difference in the AUC among four models (Z = 1.209, P > 0.05). The sensitivity (71.00%) and Youden index (43.92%) of the logistic regression model was higher than those of the BP neural network (63.00% and 36.61%, respectively), and the specificity of the BP neural network model (73.61%) was higher than that of the logistic regression model (72.92%). CONCLUSIONS Imported malaria cases with long duration of residing abroad, a history of malaria parasite infection, long incubation period, advanced age and seeking healthcare in provincial or municipal medical institutions have a high likelihood of delay in healthcare-seeking in Jiangsu Province. The models created based on the logistic regression and BP neural network show a high efficiency for prediction of the risk of healthcare-seeking among imported malaria patients in Jiangsu Province, which may provide insights into health management of imported malaria patients.
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Affiliation(s)
- Y Zhang
- School of Public Health, Yangzhou University, Yangzhou, Jiangsu 225007, China
| | - Y Cao
- National Health Commission of Key Laboratory for Parasitic Disease Prevention and Control, Jiangsu Provincial Key Laboratory on Parasite and Vector Control Technology, Jiangsu Institute of Parasitic Diseases, China
| | - K Yang
- School of Artificial Intelligence, Yangzhou University, China
| | - W Wang
- National Health Commission of Key Laboratory for Parasitic Disease Prevention and Control, Jiangsu Provincial Key Laboratory on Parasite and Vector Control Technology, Jiangsu Institute of Parasitic Diseases, China
| | - M Yang
- National Health Commission of Key Laboratory for Parasitic Disease Prevention and Control, Jiangsu Provincial Key Laboratory on Parasite and Vector Control Technology, Jiangsu Institute of Parasitic Diseases, China
| | - L Chai
- School of Public Health, Yangzhou University, Yangzhou, Jiangsu 225007, China
| | - J Gu
- School of Public Health, Yangzhou University, Yangzhou, Jiangsu 225007, China
| | - M Li
- School of Nursing, Yangzhou University, China
| | - Y Lu
- Health and Quarantine Office, Nanjing Customs, China
| | - H Zhou
- National Health Commission of Key Laboratory for Parasitic Disease Prevention and Control, Jiangsu Provincial Key Laboratory on Parasite and Vector Control Technology, Jiangsu Institute of Parasitic Diseases, China
| | - G Zhu
- National Health Commission of Key Laboratory for Parasitic Disease Prevention and Control, Jiangsu Provincial Key Laboratory on Parasite and Vector Control Technology, Jiangsu Institute of Parasitic Diseases, China
| | - J Cao
- National Health Commission of Key Laboratory for Parasitic Disease Prevention and Control, Jiangsu Provincial Key Laboratory on Parasite and Vector Control Technology, Jiangsu Institute of Parasitic Diseases, China
| | - G Lu
- School of Public Health, Yangzhou University, Yangzhou, Jiangsu 225007, China
- Jiangsu Key Laboratory of Zoonoses, Yangzhou University, Yangzhou, Jiangsu 225007, China
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Zhang J, Qin Y, Shen Y, Wang Y, Cao J, Su Y, Liu H. [Prevalence and genotyping of Cryptosporidium spp. and Giardia lamblia in dogs and cats from a pet hospital in Shanghai Municipality]. Zhongguo Xue Xi Chong Bing Fang Zhi Za Zhi 2023; 35:258-262. [PMID: 37455096 DOI: 10.16250/j.32.1374.2023098] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Subscribe] [Scholar Register] [Indexed: 07/18/2023]
Abstract
OBJECTIVE To investigate the prevalence and genotypes of Cryptosporidium spp. and Giardia lamblia in dogs and cats from a pet hospital in Shanghai Municipality. METHODS A total of 145 fresh fecal samples were collected from pet dogs and cats in a pet hospital in Shanghai Municipality during the period from November 2021 to June 2022, including 99 dog fecal samples and 46 cat fecal samples. The small subunit ribosomal ribonucleic acid (SSU rRNA) gene of Cryptosporidium and the triose phosphate isomerase (TPI) gene of G. lamblia were amplified using nested PCR assay, and the positive amplification products were sequenced from both directions. The sequence assembly was performed using the software Clustal X 2.1, and sequence alignment was conducted using BLAST. A phylogenetic tree was created with the Neighbor-Joining method using MEGA 11.0 to identify parasite species or genotype. RESULTS The overall prevalence of Cryptosporidium and G. lamblia was 20.00% (29/145) in 145 pet dog and cat fecal samples, with the prevalence of 0.69% (1/145) and 19.31% (28/145) in Cryptosporidium and G. lamblia, respectively. G. lamblia was only detected in dog fecal samples, with prevalence of 18.18% (18/99), while the detection rates of Cryptosporidium and G. lamblia were 2.17% (1/46) and 21.74% (10/46) in cat fecal samples. Nucleotide sequence analysis showed that one Cryptosporidium positive sample was characterized as C. felis, and 28 G. lamblia positive samples were all characterized as Giardia assemblage A, which showed 100% sequence homology with human isolates of Giardia. Phylogenetic analysis revealed that the sequences obtained in this study belonged to the same branch with the reported Giardia assemblage A. CONCLUSIONS Cryptosporidium and G. lamblia infection was prevalent in pet dogs and cats from the study pet hospital in Shanghai Municipality, and there is a zoonotic risk for the species and genotype. Intensified surveillance of Cryptosporidium and Giardia infection is recommended in pets and their owners, and improved management of pet keeping is required.
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Affiliation(s)
- J Zhang
- National Institute of Parasitic Diseases, Chinese Center for Disease Control and Prevention (Chinese Center for Tropical Diseases Research); National Health Commission Key Laboratory of Parasite and Vector Biology; WHO Collaborating Centre for Tropical Diseases; National Center for International Research on Tropical Diseases, Shanghai 200025, China
| | - Y Qin
- National Institute of Parasitic Diseases, Chinese Center for Disease Control and Prevention (Chinese Center for Tropical Diseases Research); National Health Commission Key Laboratory of Parasite and Vector Biology; WHO Collaborating Centre for Tropical Diseases; National Center for International Research on Tropical Diseases, Shanghai 200025, China
| | - Y Shen
- National Institute of Parasitic Diseases, Chinese Center for Disease Control and Prevention (Chinese Center for Tropical Diseases Research); National Health Commission Key Laboratory of Parasite and Vector Biology; WHO Collaborating Centre for Tropical Diseases; National Center for International Research on Tropical Diseases, Shanghai 200025, China
| | - Y Wang
- National Institute of Parasitic Diseases, Chinese Center for Disease Control and Prevention (Chinese Center for Tropical Diseases Research); National Health Commission Key Laboratory of Parasite and Vector Biology; WHO Collaborating Centre for Tropical Diseases; National Center for International Research on Tropical Diseases, Shanghai 200025, China
| | - J Cao
- National Institute of Parasitic Diseases, Chinese Center for Disease Control and Prevention (Chinese Center for Tropical Diseases Research); National Health Commission Key Laboratory of Parasite and Vector Biology; WHO Collaborating Centre for Tropical Diseases; National Center for International Research on Tropical Diseases, Shanghai 200025, China
| | - Y Su
- National Institute of Parasitic Diseases, Chinese Center for Disease Control and Prevention (Chinese Center for Tropical Diseases Research); National Health Commission Key Laboratory of Parasite and Vector Biology; WHO Collaborating Centre for Tropical Diseases; National Center for International Research on Tropical Diseases, Shanghai 200025, China
| | - H Liu
- National Institute of Parasitic Diseases, Chinese Center for Disease Control and Prevention (Chinese Center for Tropical Diseases Research); National Health Commission Key Laboratory of Parasite and Vector Biology; WHO Collaborating Centre for Tropical Diseases; National Center for International Research on Tropical Diseases, Shanghai 200025, China
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28
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An FP, Bai WD, Balantekin AB, Bishai M, Blyth S, Cao GF, Cao J, Chang JF, Chang Y, Chen HS, Chen HY, Chen SM, Chen Y, Chen YX, Cheng J, Cheng J, Cheng YC, Cheng ZK, Cherwinka JJ, Chu MC, Cummings JP, Dalager O, Deng FS, Ding YY, Diwan MV, Dohnal T, Dolzhikov D, Dove J, Dugas KV, Duyang HY, Dwyer DA, Gallo JP, Gonchar M, Gong GH, Gong H, Gu WQ, Guo JY, Guo L, Guo XH, Guo YH, Guo Z, Hackenburg RW, Han Y, Hans S, He M, Heeger KM, Heng YK, Hor YK, Hsiung YB, Hu BZ, Hu JR, Hu T, Hu ZJ, Huang HX, Huang JH, Huang XT, Huang YB, Huber P, Jaffe DE, Jen KL, Ji XL, Ji XP, Johnson RA, Jones D, Kang L, Kettell SH, Kohn S, Kramer M, Langford TJ, Lee J, Lee JHC, Lei RT, Leitner R, Leung JKC, Li F, Li HL, Li JJ, Li QJ, Li RH, Li S, Li SC, Li WD, Li XN, Li XQ, Li YF, Li ZB, Liang H, Lin CJ, Lin GL, Lin S, Ling JJ, Link JM, Littenberg L, Littlejohn BR, Liu JC, Liu JL, Liu JX, Lu C, Lu HQ, Luk KB, Ma BZ, Ma XB, Ma XY, Ma YQ, Mandujano RC, Marshall C, McDonald KT, McKeown RD, Meng Y, Napolitano J, Naumov D, Naumova E, Nguyen TMT, Ochoa-Ricoux JP, Olshevskiy A, Park J, Patton S, Peng JC, Pun CSJ, Qi FZ, Qi M, Qian X, Raper N, Ren J, Morales Reveco C, Rosero R, Roskovec B, Ruan XC, Russell B, Steiner H, Sun JL, Tmej T, Treskov K, Tse WH, Tull CE, Tung YC, Viren B, Vorobel V, Wang CH, Wang J, Wang M, Wang NY, Wang RG, Wang W, Wang X, Wang Y, Wang YF, Wang Z, Wang Z, Wang ZM, Wei HY, Wei LH, Wen LJ, Whisnant K, White CG, Wong HLH, Worcester E, Wu DR, Wu Q, Wu WJ, Xia DM, Xie ZQ, Xing ZZ, Xu HK, Xu JL, Xu T, Xue T, Yang CG, Yang L, Yang YZ, Yao HF, Ye M, Yeh M, Young BL, Yu HZ, Yu ZY, Yue BB, Zavadskyi V, Zeng S, Zeng Y, Zhan L, Zhang C, Zhang FY, Zhang HH, Zhang JL, Zhang JW, Zhang QM, Zhang SQ, Zhang XT, Zhang YM, Zhang YX, Zhang YY, Zhang ZJ, Zhang ZP, Zhang ZY, Zhao J, Zhao RZ, Zhou L, Zhuang HL, Zou JH. Improved Measurement of the Evolution of the Reactor Antineutrino Flux and Spectrum at Daya Bay. Phys Rev Lett 2023; 130:211801. [PMID: 37295075 DOI: 10.1103/physrevlett.130.211801] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/03/2022] [Revised: 02/10/2023] [Accepted: 04/27/2023] [Indexed: 06/12/2023]
Abstract
Reactor neutrino experiments play a crucial role in advancing our knowledge of neutrinos. In this Letter, the evolution of the flux and spectrum as a function of the reactor isotopic content is reported in terms of the inverse-beta-decay yield at Daya Bay with 1958 days of data and improved systematic uncertainties. These measurements are compared with two signature model predictions: the Huber-Mueller model based on the conversion method and the SM2018 model based on the summation method. The measured average flux and spectrum, as well as the flux evolution with the ^{239}Pu isotopic fraction, are inconsistent with the predictions of the Huber-Mueller model. In contrast, the SM2018 model is shown to agree with the average flux and its evolution but fails to describe the energy spectrum. Altering the predicted inverse-beta-decay spectrum from ^{239}Pu fission does not improve the agreement with the measurement for either model. The models can be brought into better agreement with the measurements if either the predicted spectrum due to ^{235}U fission is changed or the predicted ^{235}U, ^{238}U, ^{239}Pu, and ^{241}Pu spectra are changed in equal measure.
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Affiliation(s)
- F P An
- Sun Yat-Sen (Zhongshan) University, Guangzhou
| | - W D Bai
- Sun Yat-Sen (Zhongshan) University, Guangzhou
| | | | - M Bishai
- Brookhaven National Laboratory, Upton, New York 11973
| | - S Blyth
- Department of Physics, National Taiwan University, Taipei
| | - G F Cao
- Institute of High Energy Physics, Beijing
| | - J Cao
- Institute of High Energy Physics, Beijing
| | - J F Chang
- Institute of High Energy Physics, Beijing
| | - Y Chang
- National United University, Miao-Li
| | - H S Chen
- Institute of High Energy Physics, Beijing
| | - H Y Chen
- Department of Engineering Physics, Tsinghua University, Beijing
| | - S M Chen
- Department of Engineering Physics, Tsinghua University, Beijing
| | - Y Chen
- Sun Yat-Sen (Zhongshan) University, Guangzhou
- Shenzhen University, Shenzhen
| | - Y X Chen
- North China Electric Power University, Beijing
| | - J Cheng
- North China Electric Power University, Beijing
| | - J Cheng
- North China Electric Power University, Beijing
| | - Y-C Cheng
- Department of Physics, National Taiwan University, Taipei
| | - Z K Cheng
- Sun Yat-Sen (Zhongshan) University, Guangzhou
| | | | - M C Chu
- Chinese University of Hong Kong, Hong Kong
| | | | - O Dalager
- Department of Physics and Astronomy, University of California, Irvine, California 92697
| | - F S Deng
- University of Science and Technology of China, Hefei
| | - Y Y Ding
- Institute of High Energy Physics, Beijing
| | - M V Diwan
- Brookhaven National Laboratory, Upton, New York 11973
| | - T Dohnal
- Charles University, Faculty of Mathematics and Physics, Prague
| | - D Dolzhikov
- Joint Institute for Nuclear Research, Dubna, Moscow Region
| | - J Dove
- Department of Physics, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801
| | - K V Dugas
- Department of Physics and Astronomy, University of California, Irvine, California 92697
| | | | - D A Dwyer
- Lawrence Berkeley National Laboratory, Berkeley, California 94720
| | - J P Gallo
- Department of Physics, Illinois Institute of Technology, Chicago, Illinois 60616
| | - M Gonchar
- Joint Institute for Nuclear Research, Dubna, Moscow Region
| | - G H Gong
- Department of Engineering Physics, Tsinghua University, Beijing
| | - H Gong
- Department of Engineering Physics, Tsinghua University, Beijing
| | - W Q Gu
- Brookhaven National Laboratory, Upton, New York 11973
| | - J Y Guo
- Sun Yat-Sen (Zhongshan) University, Guangzhou
| | - L Guo
- Department of Engineering Physics, Tsinghua University, Beijing
| | - X H Guo
- Beijing Normal University, Beijing
| | - Y H Guo
- Department of Nuclear Science and Technology, School of Energy and Power Engineering, Xi'an Jiaotong University, Xi'an
| | - Z Guo
- Department of Engineering Physics, Tsinghua University, Beijing
| | | | - Y Han
- Sun Yat-Sen (Zhongshan) University, Guangzhou
| | - S Hans
- Brookhaven National Laboratory, Upton, New York 11973
| | - M He
- Institute of High Energy Physics, Beijing
| | - K M Heeger
- Wright Laboratory and Department of Physics, Yale University, New Haven, Connecticut 06520
| | - Y K Heng
- Institute of High Energy Physics, Beijing
| | - Y K Hor
- Sun Yat-Sen (Zhongshan) University, Guangzhou
| | - Y B Hsiung
- Department of Physics, National Taiwan University, Taipei
| | - B Z Hu
- Department of Physics, National Taiwan University, Taipei
| | - J R Hu
- Institute of High Energy Physics, Beijing
| | - T Hu
- Institute of High Energy Physics, Beijing
| | - Z J Hu
- Sun Yat-Sen (Zhongshan) University, Guangzhou
| | - H X Huang
- China Institute of Atomic Energy, Beijing
| | - J H Huang
- Institute of High Energy Physics, Beijing
| | | | - Y B Huang
- Guangxi University, No. 100 Daxue East Road, Nanning
| | - P Huber
- Center for Neutrino Physics, Virginia Tech, Blacksburg, Virginia 24061
| | - D E Jaffe
- Brookhaven National Laboratory, Upton, New York 11973
| | - K L Jen
- Institute of Physics, National Chiao-Tung University, Hsinchu
| | - X L Ji
- Institute of High Energy Physics, Beijing
| | - X P Ji
- Brookhaven National Laboratory, Upton, New York 11973
| | - R A Johnson
- Department of Physics, University of Cincinnati, Cincinnati, Ohio 45221
| | - D Jones
- Department of Physics, College of Science and Technology, Temple University, Philadelphia, Pennsylvania 19122
| | - L Kang
- Dongguan University of Technology, Dongguan
| | - S H Kettell
- Brookhaven National Laboratory, Upton, New York 11973
| | - S Kohn
- Department of Physics, University of California, Berkeley, California 94720
| | - M Kramer
- Lawrence Berkeley National Laboratory, Berkeley, California 94720
- Department of Physics, University of California, Berkeley, California 94720
| | - T J Langford
- Wright Laboratory and Department of Physics, Yale University, New Haven, Connecticut 06520
| | - J Lee
- Lawrence Berkeley National Laboratory, Berkeley, California 94720
| | - J H C Lee
- Department of Physics, The University of Hong Kong, Pokfulam, Hong Kong
| | - R T Lei
- Dongguan University of Technology, Dongguan
| | - R Leitner
- Charles University, Faculty of Mathematics and Physics, Prague
| | - J K C Leung
- Department of Physics, The University of Hong Kong, Pokfulam, Hong Kong
| | - F Li
- Institute of High Energy Physics, Beijing
| | - H L Li
- Institute of High Energy Physics, Beijing
| | - J J Li
- Department of Engineering Physics, Tsinghua University, Beijing
| | - Q J Li
- Institute of High Energy Physics, Beijing
| | - R H Li
- Institute of High Energy Physics, Beijing
| | - S Li
- Dongguan University of Technology, Dongguan
| | - S C Li
- Center for Neutrino Physics, Virginia Tech, Blacksburg, Virginia 24061
| | - W D Li
- Institute of High Energy Physics, Beijing
| | - X N Li
- Institute of High Energy Physics, Beijing
| | - X Q Li
- School of Physics, Nankai University, Tianjin
| | - Y F Li
- Institute of High Energy Physics, Beijing
| | - Z B Li
- Sun Yat-Sen (Zhongshan) University, Guangzhou
| | - H Liang
- University of Science and Technology of China, Hefei
| | - C J Lin
- Lawrence Berkeley National Laboratory, Berkeley, California 94720
| | - G L Lin
- Institute of Physics, National Chiao-Tung University, Hsinchu
| | - S Lin
- Dongguan University of Technology, Dongguan
| | - J J Ling
- Sun Yat-Sen (Zhongshan) University, Guangzhou
| | - J M Link
- Center for Neutrino Physics, Virginia Tech, Blacksburg, Virginia 24061
| | - L Littenberg
- Brookhaven National Laboratory, Upton, New York 11973
| | - B R Littlejohn
- Department of Physics, Illinois Institute of Technology, Chicago, Illinois 60616
| | - J C Liu
- Institute of High Energy Physics, Beijing
| | - J L Liu
- Department of Physics and Astronomy, Shanghai Jiao Tong University, Shanghai Laboratory for Particle Physics and Cosmology, Shanghai
| | - J X Liu
- Institute of High Energy Physics, Beijing
| | - C Lu
- Joseph Henry Laboratories, Princeton University, Princeton, New Jersey 08544
| | - H Q Lu
- Institute of High Energy Physics, Beijing
| | - K B Luk
- Lawrence Berkeley National Laboratory, Berkeley, California 94720
- Department of Physics, University of California, Berkeley, California 94720
- The Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong
| | - B Z Ma
- Shandong University, Jinan
| | - X B Ma
- North China Electric Power University, Beijing
| | - X Y Ma
- Institute of High Energy Physics, Beijing
| | - Y Q Ma
- Institute of High Energy Physics, Beijing
| | - R C Mandujano
- Department of Physics and Astronomy, University of California, Irvine, California 92697
| | - C Marshall
- Lawrence Berkeley National Laboratory, Berkeley, California 94720
| | - K T McDonald
- Joseph Henry Laboratories, Princeton University, Princeton, New Jersey 08544
| | - R D McKeown
- California Institute of Technology, Pasadena, California 91125
- College of William and Mary, Williamsburg, Virginia 23187
| | - Y Meng
- Department of Physics and Astronomy, Shanghai Jiao Tong University, Shanghai Laboratory for Particle Physics and Cosmology, Shanghai
| | - J Napolitano
- Department of Physics, College of Science and Technology, Temple University, Philadelphia, Pennsylvania 19122
| | - D Naumov
- Joint Institute for Nuclear Research, Dubna, Moscow Region
| | - E Naumova
- Joint Institute for Nuclear Research, Dubna, Moscow Region
| | - T M T Nguyen
- Institute of Physics, National Chiao-Tung University, Hsinchu
| | - J P Ochoa-Ricoux
- Department of Physics and Astronomy, University of California, Irvine, California 92697
| | - A Olshevskiy
- Joint Institute for Nuclear Research, Dubna, Moscow Region
| | - J Park
- Center for Neutrino Physics, Virginia Tech, Blacksburg, Virginia 24061
| | - S Patton
- Lawrence Berkeley National Laboratory, Berkeley, California 94720
| | - J C Peng
- Department of Physics, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801
| | - C S J Pun
- Department of Physics, The University of Hong Kong, Pokfulam, Hong Kong
| | - F Z Qi
- Institute of High Energy Physics, Beijing
| | - M Qi
- Nanjing University, Nanjing
| | - X Qian
- Brookhaven National Laboratory, Upton, New York 11973
| | - N Raper
- Sun Yat-Sen (Zhongshan) University, Guangzhou
| | - J Ren
- China Institute of Atomic Energy, Beijing
| | - C Morales Reveco
- Department of Physics and Astronomy, University of California, Irvine, California 92697
| | - R Rosero
- Brookhaven National Laboratory, Upton, New York 11973
| | - B Roskovec
- Charles University, Faculty of Mathematics and Physics, Prague
| | - X C Ruan
- China Institute of Atomic Energy, Beijing
| | - B Russell
- Lawrence Berkeley National Laboratory, Berkeley, California 94720
| | - H Steiner
- Lawrence Berkeley National Laboratory, Berkeley, California 94720
- Department of Physics, University of California, Berkeley, California 94720
| | - J L Sun
- China General Nuclear Power Group, Shenzhen
| | - T Tmej
- Charles University, Faculty of Mathematics and Physics, Prague
| | - K Treskov
- Joint Institute for Nuclear Research, Dubna, Moscow Region
| | - W-H Tse
- Chinese University of Hong Kong, Hong Kong
| | - C E Tull
- Lawrence Berkeley National Laboratory, Berkeley, California 94720
| | - Y C Tung
- Department of Physics, National Taiwan University, Taipei
| | - B Viren
- Brookhaven National Laboratory, Upton, New York 11973
| | - V Vorobel
- Charles University, Faculty of Mathematics and Physics, Prague
| | - C H Wang
- National United University, Miao-Li
| | - J Wang
- Sun Yat-Sen (Zhongshan) University, Guangzhou
| | - M Wang
- Shandong University, Jinan
| | - N Y Wang
- Beijing Normal University, Beijing
| | - R G Wang
- Institute of High Energy Physics, Beijing
| | - W Wang
- Sun Yat-Sen (Zhongshan) University, Guangzhou
- College of William and Mary, Williamsburg, Virginia 23187
| | - X Wang
- College of Electronic Science and Engineering, National University of Defense Technology, Changsha
| | - Y Wang
- Nanjing University, Nanjing
| | - Y F Wang
- Institute of High Energy Physics, Beijing
| | - Z Wang
- Institute of High Energy Physics, Beijing
| | - Z Wang
- Department of Engineering Physics, Tsinghua University, Beijing
| | - Z M Wang
- Institute of High Energy Physics, Beijing
| | - H Y Wei
- Brookhaven National Laboratory, Upton, New York 11973
| | - L H Wei
- Institute of High Energy Physics, Beijing
| | - L J Wen
- Institute of High Energy Physics, Beijing
| | | | - C G White
- Department of Physics, Illinois Institute of Technology, Chicago, Illinois 60616
| | - H L H Wong
- Lawrence Berkeley National Laboratory, Berkeley, California 94720
- Department of Physics, University of California, Berkeley, California 94720
| | - E Worcester
- Brookhaven National Laboratory, Upton, New York 11973
| | - D R Wu
- Institute of High Energy Physics, Beijing
| | - Q Wu
- Shandong University, Jinan
| | - W J Wu
- Institute of High Energy Physics, Beijing
| | - D M Xia
- Chongqing University, Chongqing
| | - Z Q Xie
- Institute of High Energy Physics, Beijing
| | - Z Z Xing
- Institute of High Energy Physics, Beijing
| | - H K Xu
- Institute of High Energy Physics, Beijing
| | - J L Xu
- Institute of High Energy Physics, Beijing
| | - T Xu
- Department of Engineering Physics, Tsinghua University, Beijing
| | - T Xue
- Department of Engineering Physics, Tsinghua University, Beijing
| | - C G Yang
- Institute of High Energy Physics, Beijing
| | - L Yang
- Dongguan University of Technology, Dongguan
| | - Y Z Yang
- Department of Engineering Physics, Tsinghua University, Beijing
| | - H F Yao
- Institute of High Energy Physics, Beijing
| | - M Ye
- Institute of High Energy Physics, Beijing
| | - M Yeh
- Brookhaven National Laboratory, Upton, New York 11973
| | - B L Young
- Iowa State University, Ames, Iowa 50011
| | - H Z Yu
- Sun Yat-Sen (Zhongshan) University, Guangzhou
| | - Z Y Yu
- Institute of High Energy Physics, Beijing
| | - B B Yue
- Sun Yat-Sen (Zhongshan) University, Guangzhou
| | - V Zavadskyi
- Brookhaven National Laboratory, Upton, New York 11973
- Joint Institute for Nuclear Research, Dubna, Moscow Region
| | - S Zeng
- Institute of High Energy Physics, Beijing
| | - Y Zeng
- Sun Yat-Sen (Zhongshan) University, Guangzhou
| | - L Zhan
- Institute of High Energy Physics, Beijing
| | - C Zhang
- Brookhaven National Laboratory, Upton, New York 11973
| | - F Y Zhang
- Department of Physics and Astronomy, Shanghai Jiao Tong University, Shanghai Laboratory for Particle Physics and Cosmology, Shanghai
| | - H H Zhang
- Sun Yat-Sen (Zhongshan) University, Guangzhou
| | | | - J W Zhang
- Institute of High Energy Physics, Beijing
| | - Q M Zhang
- Department of Nuclear Science and Technology, School of Energy and Power Engineering, Xi'an Jiaotong University, Xi'an
| | - S Q Zhang
- Sun Yat-Sen (Zhongshan) University, Guangzhou
| | - X T Zhang
- Institute of High Energy Physics, Beijing
| | - Y M Zhang
- Sun Yat-Sen (Zhongshan) University, Guangzhou
| | - Y X Zhang
- China General Nuclear Power Group, Shenzhen
| | - Y Y Zhang
- Department of Physics and Astronomy, Shanghai Jiao Tong University, Shanghai Laboratory for Particle Physics and Cosmology, Shanghai
| | - Z J Zhang
- Dongguan University of Technology, Dongguan
| | - Z P Zhang
- University of Science and Technology of China, Hefei
| | - Z Y Zhang
- Institute of High Energy Physics, Beijing
| | - J Zhao
- Institute of High Energy Physics, Beijing
| | - R Z Zhao
- Institute of High Energy Physics, Beijing
| | - L Zhou
- Institute of High Energy Physics, Beijing
| | - H L Zhuang
- Institute of High Energy Physics, Beijing
| | - J H Zou
- Institute of High Energy Physics, Beijing
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Cao F, Guo Y, Guo S, Zhou Z, Cao J, Tong L, Mi W. [Activation of GABAergic neurons in the zona incerta accelerates anesthesia induction with sevoflurane and propofol without affecting anesthesia maintenance or awakening in mice]. Nan Fang Yi Ke Da Xue Xue Bao 2023; 43:718-726. [PMID: 37313812 DOI: 10.12122/j.issn.1673-4254.2023.05.06] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
OBJECTIVE To explore the regulatory effects of GABAergic neurons in the zona incerta (ZI) on sevoflurane and propofol anesthesia. METHODS Forty-eight male C57BL/6J mice divided into 8 groups (n=6) were used in this study. In the study of sevoflurane anesthesia, chemogenetic experiment was performed in 2 groups of mice with injection of either adeno-associated virus carrying hM3Dq (hM3Dq group) or a virus carrying only mCherry (mCherry group). The optogenetic experiment was performed in another two groups of mice injected with an adeno-associated virus carrying ChR2 (ChR2 group) or GFP only (GFP group). The same experiments were also performed in mice for studying propofol anesthesia. Chemogenetics or optogenetics were used to induce the activation of GABAergic neurons in the ZI, and their regulatory effects on anesthesia induction and arousal with sevoflurane and propofol were observed; EEG monitoring was used to observe the changes in sevoflurane anesthesia maintenance after activation of the GABAergic neurons. RESULTS In sevoflurane anesthesia, the induction time of anesthesia was significantly shorter in hM3Dq group than in mCherry group (P < 0.05), and also shorter in ChR2 group than in GFP group (P < 0.01), but no significant difference was found in the awakening time between the two groups in either chemogenetic or optogenetic tests. Similar results were observed in chemogenetic and optogenetic experiments with propofol (P < 0.05 or 0.01). Photogenetic activation of the GABAergic neurons in the ZI did not cause significant changes in EEG spectrum during sevoflurane anesthesia maintenance. CONCLUSION Activation of the GABAergic neurons in the ZI promotes anesthesia induction of sevoflurane and propofol but does not affect anesthesia maintenance or awakening.
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Affiliation(s)
- F Cao
- Department of Anesthesia, First Medical Center of Chinese PLA General Hospital, Beijing 100853, China
- Department of Anesthesia, Sixth Medical Center of Chinese PLA General Hospital, Beijing 100048, China
| | - Y Guo
- Department of Anesthesia, First Medical Center of Chinese PLA General Hospital, Beijing 100853, China
| | - S Guo
- Department of Anesthesia, First Medical Center of Chinese PLA General Hospital, Beijing 100853, China
| | - Z Zhou
- Department of Anesthesia, First Medical Center of Chinese PLA General Hospital, Beijing 100853, China
| | - J Cao
- Department of Anesthesia, First Medical Center of Chinese PLA General Hospital, Beijing 100853, China
| | - L Tong
- Department of Anesthesia, First Medical Center of Chinese PLA General Hospital, Beijing 100853, China
| | - W Mi
- Department of Anesthesia, First Medical Center of Chinese PLA General Hospital, Beijing 100853, China
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Wang Z, Huang Y, Zhu M, Cao J, Xiong Z. TLR2 and CASP7 as the biomarkers associated with non-alcoholic fatty liver disease and chronic kidney disease. Biochem Biophys Res Commun 2023; 667:50-57. [PMID: 37209562 DOI: 10.1016/j.bbrc.2023.05.038] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2023] [Revised: 05/04/2023] [Accepted: 05/13/2023] [Indexed: 05/22/2023]
Abstract
BACKGROUND Non-alcoholic fatty liver disease (NAFLD) and chronic kidney disease (CKD) are both highly prevalent worldwide. Studies have confirmed the association between them, but the underlying pathophysiological mechanisms are not clear yet. This study aims to identify the genetic and molecular mechanisms influencing both diseases through a bioinformatics approach. RESULTS Fifty-four overlapping differentially expressed genes associated with NAFLD and CKD were obtained by analysis of microarray datasets GSE63067 and GSE66494 downloaded from Gene Expression Omnibus. Next, we performed Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analysis. Nine hub genes were screened using protein-protein interaction network and Cytoscape software, including TLR2, ICAM1, RELB, BIRC3, HIF1A, RIPK2, CASP7, IFNGR1 and MAP2K4. The receiver operating characteristic curve results showed that all hub genes have good diagnostic values for patients with NAFLD and CKD. The mRNA expression of nine hub genes was detected in NAFLD and CKD animal models, and it was found that the expression of TLR2 and CASP7 was significantly increased in both disease models. CONCLUSIONS TLR2 and CASP7 can be used as biomarkers for both diseases. Our study provided new insights for identifying potential biomarkers and valuable therapeutic leads in NAFLD and CKD.
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Affiliation(s)
- Ziwen Wang
- Department of Gastroenterology, Liyuan Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
| | - Yumei Huang
- Department of Gastroenterology, Liyuan Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
| | - Mengpei Zhu
- Department of Gastroenterology, Liyuan Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
| | - Jiali Cao
- Department of Gastroenterology, Liyuan Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
| | - Zhifan Xiong
- Department of Gastroenterology, Liyuan Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
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Cao J, Zhang X, Xing X, Fan J. Biologic TNF-α Inhibitors for Stevens-Johnson Syndrome, Toxic Epidermal Necrolysis, and TEN-SJS Overlap: A Study-Level and Patient-Level Meta-Analysis. Dermatol Ther (Heidelb) 2023:10.1007/s13555-023-00928-w. [PMID: 37178320 DOI: 10.1007/s13555-023-00928-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2023] [Accepted: 04/13/2023] [Indexed: 05/15/2023] Open
Abstract
INTRODUCTION Stevens-Johnson syndrome (SJS) and toxic epidermal necrolysis (TEN) are severe cutaneous adverse reactions with high morbidity and mortality and not clearly established treatment protocol. This meta-analysis aimed to evaluate the efficacy and safety of three biologic TNF-α inhibitors (infliximab, etanercept, adalimumab) in the treatment of SJS, SJS-TEN overlap, and TEN. METHODS Electronic databases were searched for original studies containing human participants diagnosed with SJS/TEN and treated with biologic TNF-α inhibitors. Individual patient data were collected and summarized to provide a comprehensive overview on therapeutic efficacy of different biologic TNF-α inhibitors for SJS, SJS-TEN overlap, and TEN, respectively. Meta-analyses on aggregated study data were conducted using random-effects model. RESULTS Overall, 55 studies with 125 sets of individual patient data were included. Infliximab was used to treat 3 patients with SJS-TEN overlap and 28 patients with TEN, and the actual mortality rate was 33.3% and 17%, respectively. Etanercept was administered to 17 patients with SJS, 9 patients with SJS-TEN overlap, and 64 patients with TEN, and mortality rate was reported to be 0%, 0%, and 12.5%, respectively. For participants with TEN, no significant difference was found in time of reepithelialization, hospitalization time, and mortality rate comparing etanercept with infliximab. More sequelae were reported in patients receiving infliximab than in patients treated with etanercept (39.3% versus 6.4%). Adalimumab was administered to four patients with TEN, and mortality rate was 25%. Meta-analyses on aggregated study data revealed significantly shortened hospitalization time in etanercept compared with non-etanercept groups [weighted mean differences (WMD) -5.30; 95% confidence interval (CI) -8.65 to -1.96]. Etanercept was associated with a survival benefit for patients when compared with non-etanercept treatment, however, the analysis was not statistically significant (odds ratio 0.55; 95% CI 0.23-1.33). CONCLUSIONS On the basis of the current findings, etanercept is currently the most promising biologic therapy for SJS/TEN. Further evaluation in prospective studies is required to confirm its efficacy and safety.
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Affiliation(s)
- Jiali Cao
- Department of Dermatology, Beijing Chaoyang Hospital, Capital Medical University, No. 8 Gongti South Road, Chaoyang District, Beijing, 100020, China.
| | - Xuan Zhang
- Department of Dermatology, Beijing Chaoyang Hospital, Capital Medical University, No. 8 Gongti South Road, Chaoyang District, Beijing, 100020, China
| | - Xinzhu Xing
- Department of Dermatology, Beijing Chaoyang Hospital, Capital Medical University, No. 8 Gongti South Road, Chaoyang District, Beijing, 100020, China
| | - Jie Fan
- Medical Department, Shunyi Maternal and Children's Hospital of Beijing Children's Hospital, Beijing, 101300, China
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Koss KM, Son T, Li C, Hao Y, Cao J, Churchward MA, Zhang ZJ, Wertheim JA, Derda R, Todd KG. Toward discovering a novel family of peptides targeting neuroinflammatory states of brain microglia and astrocytes. J Neurochem 2023:10.1111/jnc.15840. [PMID: 37171455 PMCID: PMC10640667 DOI: 10.1111/jnc.15840] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2022] [Revised: 04/17/2023] [Accepted: 04/19/2023] [Indexed: 05/13/2023]
Abstract
Microglia are immune-derived cells critical to the development and healthy function of the brain and spinal cord, yet are implicated in the active pathology of many neuropsychiatric disorders. A range of functional phenotypes associated with the healthy brain or disease states has been suggested from in vivo work and were modeled in vitro as surveying, reactive, and primed sub-types of primary rat microglia and mixed microglia/astrocytes. It was hypothesized that the biomolecular profile of these cells undergoes a phenotypical change as well, and these functional phenotypes were explored for potential novel peptide binders using a custom 7 amino acid-presenting M13 phage library (SX7) to identify unique peptides that bind differentially to these respective cell types. Surveying glia were untreated, reactive were induced with a lipopolysaccharide treatment, recovery was modeled with a potent anti-inflammatory treatment dexamethasone, and priming was determined by subsequently challenging the cells with interferon gamma. Microglial function was profiled by determining the secretion of cytokines and nitric oxide, and expression of inducible nitric oxide synthase. After incubation with the SX7 phage library, populations of SX7-positive microglia and/or astrocytes were collected using fluorescence-activated cell sorting, SX7 phage was amplified in Escherichia coli culture, and phage DNA was sequenced via next-generation sequencing. Binding validation was done with synthesized peptides via in-cell westerns. Fifty-eight unique peptides were discovered, and their potential functions were assessed using a basic local alignment search tool. Peptides potentially originated from proteins ranging in function from a variety of supportive glial roles, including synapse support and pruning, to inflammatory incitement including cytokine and interleukin activation, and potential regulation in neurodegenerative and neuropsychiatric disorders.
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Affiliation(s)
- K M Koss
- Comprehensive Transplant Center and Department of Surgery, Feinberg School of Medicine, Northwestern University, Illinois, Chicago, USA
- Neurochemical Research Unit, Department of Psychiatry, University of Alberta, Alberta, Edmonton, Canada
- Department of Surgery, University of Arizona College of Medicine, Arizona, Tucson, USA
| | - T Son
- Comprehensive Transplant Center and Department of Surgery, Feinberg School of Medicine, Northwestern University, Illinois, Chicago, USA
| | - C Li
- Department of Chemistry, University of Alberta, 11227 Saskatchewan Dr NW, Edmonton, AB T6G 2G2, Canada
| | - Y Hao
- Department of Chemistry, University of Alberta, 11227 Saskatchewan Dr NW, Edmonton, AB T6G 2G2, Canada
| | - J Cao
- Department of Chemistry, University of Alberta, 11227 Saskatchewan Dr NW, Edmonton, AB T6G 2G2, Canada
- 48Hour Discovery Inc, 11421 Saskatchewan Dr NW, Edmonton, AB T6G 2M9, Canada
| | - M A Churchward
- Neurochemical Research Unit, Department of Psychiatry, University of Alberta, Alberta, Edmonton, Canada
- Neuroscience and Mental Health Institute, University of Alberta, Alberta, Edmonton, Canada
- Department of Biology and Environmental Sciences, Concordia University of Edmonton, Alberta, Edmonton, Canada
| | - Z J Zhang
- Comprehensive Transplant Center and Department of Surgery, Feinberg School of Medicine, Northwestern University, Illinois, Chicago, USA
| | - J A Wertheim
- Comprehensive Transplant Center and Department of Surgery, Feinberg School of Medicine, Northwestern University, Illinois, Chicago, USA
- Department of Surgery, University of Arizona College of Medicine, Arizona, Tucson, USA
| | - R Derda
- Department of Chemistry, University of Alberta, 11227 Saskatchewan Dr NW, Edmonton, AB T6G 2G2, Canada
- 48Hour Discovery Inc, 11421 Saskatchewan Dr NW, Edmonton, AB T6G 2M9, Canada
| | - K G Todd
- Neurochemical Research Unit, Department of Psychiatry, University of Alberta, Alberta, Edmonton, Canada
- Neuroscience and Mental Health Institute, University of Alberta, Alberta, Edmonton, Canada
- Department of Biomedical Engineering, University of Alberta, Alberta, Edmonton, Canada
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Mao C, Ji D, Ding Y, Zhang Y, Song W, Liu L, Wu Y, Song L, Feng X, Zhang J, Cao J, Xu N. Suvemcitug as second-line treatment of advanced or metastatic solid tumors and with FOLFIRI for pretreated metastatic colorectal cancer: phase Ia/Ib open label, dose-escalation trials. ESMO Open 2023; 8:101540. [PMID: 37178668 DOI: 10.1016/j.esmoop.2023.101540] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2023] [Revised: 03/20/2023] [Accepted: 03/29/2023] [Indexed: 05/15/2023] Open
Abstract
BACKGROUND Suvemcitug (BD0801), a novel humanized rabbit monoclonal antibody against vascular endothelial growth factor, has demonstrated promising antitumor activities in preclinical studies. PATIENTS AND METHODS The phase Ia/b trials investigated the safety and tolerability and antitumor activities of suvemcitug for pretreated advanced solid tumors and in combination with FOLFIRI (leucovorin and fluorouracil plus irinotecan) in second-line treatment of metastatic colorectal cancer using a 3 + 3 dose-escalation design. Patients received escalating doses of suvemcitug (phase Ia: 2, 4, 5, 6, and 7.5 mg/kg; phase Ib: 1, 2, 3, 4, and 5 mg/kg plus FOLFIRI). The primary endpoint was safety and tolerability in both trials. RESULTS All patients in the phase Ia trial had at least one adverse event (AE). Dose-limiting toxicities included grade 3 hyperbilirubinemia (one patient), hypertension and proteinuria (one patient), and proteinuria (one patient). The maximum tolerated dose was 5 mg/kg. The most common grade 3 and above AEs were proteinuria (9/25, 36%) and hypertension (8/25, 32%). Forty-eight patients (85.7%) in phase Ib had grade 3 and above AEs, including neutropenia (25/56, 44.6%), reduced leucocyte count (12/56, 21.4%), proteinuria (10/56, 17.9%), and elevated blood pressure (9/56, 16.1%). Only 1 patient in the phase Ia trial showed partial response, [objective response rate 4.0%, 95% confidence interval (CI) 0.1% to 20.4%] whereas 18/53 patients in the phase Ib trial exhibited partial response (objective response rate 34.0%, 95% CI 21.5% to 48.3%). The median progression-free survival was 7.2 months (95% CI 5.1-8.7 months). CONCLUSIONS Suvemcitug has an acceptable toxicity profile and exhibits antitumor activities in pretreated patients with advanced solid tumors or metastatic colorectal cancer.
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Affiliation(s)
- C Mao
- Department of Medical Oncology, The First Affiliated Hospital of Medical College of Zhejiang University, Shangcheng District, Hangzhou, Zhejiang Province
| | - D Ji
- Department of Head & Neck Tumors and Neuroendocrine Tumors, Fudan University Shanghai Cancer Hospital, Xuhui District, Shanghai; Department of Oncology, Shanghai Medical College, Fudan University, Xuhui District, Shanghai, China
| | - Y Ding
- Phase I Clinical Trials Unit, The First Hospital of Jilin University, Chaoyang District, Changchun, Jilin Province, China
| | - Y Zhang
- Department of Gastrointestinal Medical Oncology, Harbin Medical University Cancer Hospital, Nangang District, Harbin, China
| | - W Song
- Clinical Science, Shandong Simcere Bio-Pharmaceutical Co., Ltd., Yantai, Shandong Province, China
| | - L Liu
- Clinical Statistics, Shandong Simcere Bio-Pharmaceutical Co., Ltd., Yantai, Shandong Province, China
| | - Y Wu
- Clinical Science, Shandong Simcere Bio-Pharmaceutical Co., Ltd., Yantai, Shandong Province, China
| | - L Song
- Clinical Pharmacology, Shandong Simcere Bio-Pharmaceutical Co., Ltd., Yantai, Shandong Province, China
| | - X Feng
- Clinical Science, Shandong Simcere Bio-Pharmaceutical Co., Ltd., Yantai, Shandong Province, China
| | - J Zhang
- Clinical Science, Shandong Simcere Bio-Pharmaceutical Co., Ltd., Yantai, Shandong Province, China
| | - J Cao
- Department of Lymphoma, Fudan University Shanghai Cancer Hospital, Xuhui District, Shanghai, China.
| | - N Xu
- Department of Medical Oncology, The First Affiliated Hospital of Medical College of Zhejiang University, Shangcheng District, Hangzhou, Zhejiang Province.
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Zhu G, Cao J. [Regular assessment is an effective approach to maintaining the capacity of prevention of re-establishment from imported malaria in China]. Zhongguo Xue Xi Chong Bing Fang Zhi Za Zhi 2023; 35:113-115. [PMID: 37253558 DOI: 10.16250/j.32.1374.2023049] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Subscribe] [Scholar Register] [Indexed: 06/01/2023]
Abstract
After achieving malaria elimination, preventing re-establishment from imported malaria and consolidating malaria elimination achievements are top priorities of the national malaria control program in China. Due to the long-term existence of overseas imported malaria cases and incomplete eradication of local epidemic conditions, there are multiple challenges for prevention of re-establishment from imported malaria in China. Hereby, we propose that regular assessment is an effective approach to maintaining the capability of prevention of re-establishment from imported malaria, and describe the purpose, significance, management and implementation of the capability assessment for prevention of re-establishment from imported malaria, so as to provide insights into the formulation and adjustment of malaria control strategies during the post-elimination phase.
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Affiliation(s)
- G Zhu
- Key Laboratory of National Health Commission on Parasitic Disease Control and Prevention, Key Laboratory of Jiangsu Province on Parasite and Vector Control Technology, Jiangsu Institute of Parasitic Diseases, Wuxi, Jiangsu 214064, China
- Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, Jiangsu 211166, China
| | - J Cao
- Key Laboratory of National Health Commission on Parasitic Disease Control and Prevention, Key Laboratory of Jiangsu Province on Parasite and Vector Control Technology, Jiangsu Institute of Parasitic Diseases, Wuxi, Jiangsu 214064, China
- Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, Jiangsu 211166, China
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Lu Y, Cao J, Zhu EJ, Gao MX, Mou TT, Zhang Y, Xie XF, Tian Y, Yun MK, Meng JJ, Yang XB, Lai YQ, Dong R, Zhang XL. [Predictive value of the proportion of hibernating myocardium in total perfusion defect on reverse remodeling in patients with HFrEF underwent coronary artery bypass graft]. Zhonghua Xin Xue Guan Bing Za Zhi 2023; 51:384-392. [PMID: 37057325 DOI: 10.3760/cma.j.cn112148-20221121-00912] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Subscribe] [Scholar Register] [Indexed: 04/15/2023]
Abstract
Objective: To evaluate the predictive value of the proportion of hibernating myocardium (HM) in total perfusion defect (TPD) on reverse left ventricle remodeling (RR) after coronary artery bypass graft (CABG) in patients with heart failure with reduced ejection fraction (HFrEF) by 99mTc-methoxyisobutylisonitrile (MIBI) single photon emission computed tomography (SPECT) myocardial perfusion imaging (MPI) combined with 18F-flurodeoxyglucose (FDG) gated myocardial imaging positron emission computed tomography (PET). Methods: Inpatients diagnosed with HFrEF at the Cardiac Surgery Center, Anzhen Hospital of Capital Medical University from January 2016 to January 2022 were prospectively recruited. MPI combined with 18F-FDG gated PET was performed before surgery for viability assessment and the patients received follow-up MPI and 18F-FDG gated PET at different stages (3-12 months) after surgery. Δ indicated changes (post-pre). Left ventricular end-systolic volume (ESV) reduced at least 10% was defined as RR, patients were divided into reverse remodeling (RR+) group and the non-reverse group (RR-). Binary logistic regression analysis was used to identify predictors of RR. Receiver operating characteristic (ROC) curve analysis was performed and the area under the curve (AUC) was calculated to assess the cut-off value for predicting RR. Additionally, we retrospectively enrolled inpatients with HFrEF at the Cardiac Surgery Center, Anzhen Hospital of Capital Medical University from January 2021 to January 2022 as the validation group, who underwent MPI and 18F-FDG gated PET before surgery. Echocardiography was performed before CABG and after CABG (3-12 months). In the validation group, the reliability of obtaining the cut-off value for the ROC curve was verified. Results: A total of 28 patients with HFrEF (26 males; age (56.9±8.7) years) were included in the prospective cohort. HM/TPD was significantly higher in the RR+ group than in the RR- group ((51.8%±17.9%) vs. (35.7%±13.9%), P=0.016). Binary logistic regression analysis revealed that HM/TPD was an independent predictor of RR (Odds ratio=1.073, 95% Confidence interval: 1.005-1.145, P=0.035). ROC curve analysis revealed that HM/TPD=38.3% yielded the highest sensitivity, specificity, and accuracy (all 75%) for predicting RR and the AUC was 0.786 (P=0.011). Meanwhile, a total of 100 patients with HFrEF (90 males; age (59.7±9.6) years) were included in the validation group. In the validation group, HM/TPD=38.3% predicted RR in HFrEF patients after CABG with the highest sensitivity, specificity and accuracy (82%, 60% and 73% respectively). Compared with the HFrEF patients in the HM/TPD<38.3% group (n=36), RR and cardiac function improved more significantly in the HM/TPD≥38.3% group (n=64) (all P<0.05). Conclusions: Preoperative HM/TPD ratio is an independent factor for predicting RR in patients with HFrEF after CABG, and HM/TPD≥38.3% can accurately predict RR and the improvement of cardiac function after CABG.
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Affiliation(s)
- Y Lu
- Department of Nuclear Medicine, Laboratory for Molecular Imaging, Beijing Anzhen Hospital, Capital Medical University, Beijing 100029, China
| | - J Cao
- Heart Surgery Center, Beijing Anzhen Hospital, Capital Medical University, Beijing 100029, China
| | - E J Zhu
- Heart Surgery Center, Beijing Anzhen Hospital, Capital Medical University, Beijing 100029, China
| | - M X Gao
- Heart Surgery Center, Beijing Anzhen Hospital, Capital Medical University, Beijing 100029, China
| | - T T Mou
- Department of Nuclear Medicine, Laboratory for Molecular Imaging, Beijing Anzhen Hospital, Capital Medical University, Beijing 100029, China
| | - Y Zhang
- Department of Nuclear Medicine, Laboratory for Molecular Imaging, Beijing Anzhen Hospital, Capital Medical University, Beijing 100029, China
| | - X F Xie
- Department of Nuclear Medicine, Laboratory for Molecular Imaging, Beijing Anzhen Hospital, Capital Medical University, Beijing 100029, China
| | - Y Tian
- Department of Nuclear Medicine, Laboratory for Molecular Imaging, Beijing Anzhen Hospital, Capital Medical University, Beijing 100029, China
| | - M K Yun
- Department of Nuclear Medicine, Laboratory for Molecular Imaging, Beijing Anzhen Hospital, Capital Medical University, Beijing 100029, China
| | - J J Meng
- Department of Nuclear Medicine, Laboratory for Molecular Imaging, Beijing Anzhen Hospital, Capital Medical University, Beijing 100029, China
| | - X B Yang
- Heart Surgery Center, Beijing Anzhen Hospital, Capital Medical University, Beijing 100029, China
| | - Y Q Lai
- Heart Surgery Center, Beijing Anzhen Hospital, Capital Medical University, Beijing 100029, China
| | - R Dong
- Heart Surgery Center, Beijing Anzhen Hospital, Capital Medical University, Beijing 100029, China
| | - X L Zhang
- Department of Nuclear Medicine, Laboratory for Molecular Imaging, Beijing Anzhen Hospital, Capital Medical University, Beijing 100029, China
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An FP, Bai WD, Balantekin AB, Bishai M, Blyth S, Cao GF, Cao J, Chang JF, Chang Y, Chen HS, Chen HY, Chen SM, Chen Y, Chen YX, Chen ZY, Cheng J, Cheng ZK, Cherwinka JJ, Chu MC, Cummings JP, Dalager O, Deng FS, Ding YY, Ding XY, Diwan MV, Dohnal T, Dolzhikov D, Dove J, Duyang HY, Dwyer DA, Gallo JP, Gonchar M, Gong GH, Gong H, Gu WQ, Guo JY, Guo L, Guo XH, Guo YH, Guo Z, Hackenburg RW, Han Y, Hans S, He M, Heeger KM, Heng YK, Hor YK, Hsiung YB, Hu BZ, Hu JR, Hu T, Hu ZJ, Huang HX, Huang JH, Huang XT, Huang YB, Huber P, Jaffe DE, Jen KL, Ji XL, Ji XP, Johnson RA, Jones D, Kang L, Kettell SH, Kohn S, Kramer M, Langford TJ, Lee J, Lee JHC, Lei RT, Leitner R, Leung JKC, Li F, Li HL, Li JJ, Li QJ, Li RH, Li S, Li SC, Li WD, Li XN, Li XQ, Li YF, Li ZB, Liang H, Lin CJ, Lin GL, Lin S, Ling JJ, Link JM, Littenberg L, Littlejohn BR, Liu JC, Liu JL, Liu JX, Lu C, Lu HQ, Luk KB, Ma BZ, Ma XB, Ma XY, Ma YQ, Mandujano RC, Marshall C, McDonald KT, McKeown RD, Meng Y, Napolitano J, Naumov D, Naumova E, Nguyen TMT, Ochoa-Ricoux JP, Olshevskiy A, Pan HR, Park J, Patton S, Peng JC, Pun CSJ, Qi FZ, Qi M, Qian X, Raper N, Ren J, Morales Reveco C, Rosero R, Roskovec B, Ruan XC, Russell B, Steiner H, Sun JL, Tmej T, Treskov K, Tse WH, Tull CE, Viren B, Vorobel V, Wang CH, Wang J, Wang M, Wang NY, Wang RG, Wang W, Wang X, Wang Y, Wang YF, Wang Z, Wang Z, Wang ZM, Wei HY, Wei LH, Wei W, Wen LJ, Whisnant K, White CG, Wong HLH, Worcester E, Wu DR, Wu Q, Wu WJ, Xia DM, Xie ZQ, Xing ZZ, Xu HK, Xu JL, Xu T, Xue T, Yang CG, Yang L, Yang YZ, Yao HF, Ye M, Yeh M, Young BL, Yu HZ, Yu ZY, Yue BB, Zavadskyi V, Zeng S, Zeng Y, Zhan L, Zhang C, Zhang FY, Zhang HH, Zhang JL, Zhang JW, Zhang QM, Zhang SQ, Zhang XT, Zhang YM, Zhang YX, Zhang YY, Zhang ZJ, Zhang ZP, Zhang ZY, Zhao J, Zhao RZ, Zhou L, Zhuang HL, Zou JH. Precision Measurement of Reactor Antineutrino Oscillation at Kilometer-Scale Baselines by Daya Bay. Phys Rev Lett 2023; 130:161802. [PMID: 37154643 DOI: 10.1103/physrevlett.130.161802] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/01/2022] [Accepted: 02/24/2023] [Indexed: 05/10/2023]
Abstract
We present a new determination of the smallest neutrino mixing angle θ_{13} and the mass-squared difference Δm_{32}^{2} using a final sample of 5.55×10^{6} inverse beta-decay (IBD) candidates with the final-state neutron captured on gadolinium. This sample is selected from the complete dataset obtained by the Daya Bay reactor neutrino experiment in 3158 days of operation. Compared to the previous Daya Bay results, selection of IBD candidates has been optimized, energy calibration refined, and treatment of backgrounds further improved. The resulting oscillation parameters are sin^{2}2θ_{13}=0.0851±0.0024, Δm_{32}^{2}=(2.466±0.060)×10^{-3} eV^{2} for the normal mass ordering or Δm_{32}^{2}=-(2.571±0.060)×10^{-3} eV^{2} for the inverted mass ordering.
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Affiliation(s)
- F P An
- Sun Yat-Sen (Zhongshan) University, Guangzhou
| | - W D Bai
- Sun Yat-Sen (Zhongshan) University, Guangzhou
| | | | - M Bishai
- Brookhaven National Laboratory, Upton, New York 11973
| | - S Blyth
- Department of Physics, National Taiwan University, Taipei
| | - G F Cao
- Institute of High Energy Physics, Beijing
| | - J Cao
- Institute of High Energy Physics, Beijing
| | - J F Chang
- Institute of High Energy Physics, Beijing
| | - Y Chang
- National United University, Miao-Li
| | - H S Chen
- Institute of High Energy Physics, Beijing
| | - H Y Chen
- Department of Engineering Physics, Tsinghua University, Beijing
| | - S M Chen
- Department of Engineering Physics, Tsinghua University, Beijing
| | - Y Chen
- Sun Yat-Sen (Zhongshan) University, Guangzhou
- Shenzhen University, Shenzhen
| | - Y X Chen
- North China Electric Power University, Beijing
| | - Z Y Chen
- Institute of High Energy Physics, Beijing
| | - J Cheng
- North China Electric Power University, Beijing
| | - Z K Cheng
- Sun Yat-Sen (Zhongshan) University, Guangzhou
| | | | - M C Chu
- Chinese University of Hong Kong, Hong Kong
| | | | - O Dalager
- Department of Physics and Astronomy, University of California, Irvine, California 92697
| | - F S Deng
- University of Science and Technology of China, Hefei
| | - Y Y Ding
- Institute of High Energy Physics, Beijing
| | | | - M V Diwan
- Brookhaven National Laboratory, Upton, New York 11973
| | - T Dohnal
- Charles University, Faculty of Mathematics and Physics, Prague
| | - D Dolzhikov
- Joint Institute for Nuclear Research, Dubna, Moscow Region
| | - J Dove
- Department of Physics, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801
| | | | - D A Dwyer
- Lawrence Berkeley National Laboratory, Berkeley, California 94720
| | - J P Gallo
- Department of Physics, Illinois Institute of Technology, Chicago, Illinois 60616
| | - M Gonchar
- Joint Institute for Nuclear Research, Dubna, Moscow Region
| | - G H Gong
- Department of Engineering Physics, Tsinghua University, Beijing
| | - H Gong
- Department of Engineering Physics, Tsinghua University, Beijing
| | - W Q Gu
- Brookhaven National Laboratory, Upton, New York 11973
| | - J Y Guo
- Sun Yat-Sen (Zhongshan) University, Guangzhou
| | - L Guo
- Department of Engineering Physics, Tsinghua University, Beijing
| | - X H Guo
- Beijing Normal University, Beijing
| | - Y H Guo
- Department of Nuclear Science and Technology, School of Energy and Power Engineering, Xi'an Jiaotong University, Xi'an
| | - Z Guo
- Department of Engineering Physics, Tsinghua University, Beijing
| | | | - Y Han
- Sun Yat-Sen (Zhongshan) University, Guangzhou
| | - S Hans
- Brookhaven National Laboratory, Upton, New York 11973
| | - M He
- Institute of High Energy Physics, Beijing
| | - K M Heeger
- Wright Laboratory and Department of Physics, Yale University, New Haven, Connecticut 06520
| | - Y K Heng
- Institute of High Energy Physics, Beijing
| | - Y K Hor
- Sun Yat-Sen (Zhongshan) University, Guangzhou
| | - Y B Hsiung
- Department of Physics, National Taiwan University, Taipei
| | - B Z Hu
- Department of Physics, National Taiwan University, Taipei
| | - J R Hu
- Institute of High Energy Physics, Beijing
| | - T Hu
- Institute of High Energy Physics, Beijing
| | - Z J Hu
- Sun Yat-Sen (Zhongshan) University, Guangzhou
| | - H X Huang
- China Institute of Atomic Energy, Beijing
| | - J H Huang
- Institute of High Energy Physics, Beijing
| | | | - Y B Huang
- Guangxi University, No.100 Daxue East Road, Nanning
| | - P Huber
- Center for Neutrino Physics, Virginia Tech, Blacksburg, Virginia 24061
| | - D E Jaffe
- Brookhaven National Laboratory, Upton, New York 11973
| | - K L Jen
- Institute of Physics, National Chiao-Tung University, Hsinchu
| | - X L Ji
- Institute of High Energy Physics, Beijing
| | - X P Ji
- Brookhaven National Laboratory, Upton, New York 11973
| | - R A Johnson
- Department of Physics, University of Cincinnati, Cincinnati, Ohio 45221
| | - D Jones
- Department of Physics, College of Science and Technology, Temple University, Philadelphia, Pennsylvania 19122
| | - L Kang
- Dongguan University of Technology, Dongguan
| | - S H Kettell
- Brookhaven National Laboratory, Upton, New York 11973
| | - S Kohn
- Department of Physics, University of California, Berkeley, California 94720
| | - M Kramer
- Lawrence Berkeley National Laboratory, Berkeley, California 94720
- Department of Physics, University of California, Berkeley, California 94720
| | - T J Langford
- Wright Laboratory and Department of Physics, Yale University, New Haven, Connecticut 06520
| | - J Lee
- Lawrence Berkeley National Laboratory, Berkeley, California 94720
| | - J H C Lee
- Department of Physics, The University of Hong Kong, Pokfulam, Hong Kong
| | - R T Lei
- Dongguan University of Technology, Dongguan
| | - R Leitner
- Charles University, Faculty of Mathematics and Physics, Prague
| | - J K C Leung
- Department of Physics, The University of Hong Kong, Pokfulam, Hong Kong
| | - F Li
- Institute of High Energy Physics, Beijing
| | - H L Li
- Institute of High Energy Physics, Beijing
| | - J J Li
- Department of Engineering Physics, Tsinghua University, Beijing
| | - Q J Li
- Institute of High Energy Physics, Beijing
| | - R H Li
- Institute of High Energy Physics, Beijing
| | - S Li
- Dongguan University of Technology, Dongguan
| | - S C Li
- Center for Neutrino Physics, Virginia Tech, Blacksburg, Virginia 24061
| | - W D Li
- Institute of High Energy Physics, Beijing
| | - X N Li
- Institute of High Energy Physics, Beijing
| | - X Q Li
- School of Physics, Nankai University, Tianjin
| | - Y F Li
- Institute of High Energy Physics, Beijing
| | - Z B Li
- Sun Yat-Sen (Zhongshan) University, Guangzhou
| | - H Liang
- University of Science and Technology of China, Hefei
| | - C J Lin
- Lawrence Berkeley National Laboratory, Berkeley, California 94720
| | - G L Lin
- Institute of Physics, National Chiao-Tung University, Hsinchu
| | - S Lin
- Dongguan University of Technology, Dongguan
| | - J J Ling
- Sun Yat-Sen (Zhongshan) University, Guangzhou
| | - J M Link
- Center for Neutrino Physics, Virginia Tech, Blacksburg, Virginia 24061
| | - L Littenberg
- Brookhaven National Laboratory, Upton, New York 11973
| | - B R Littlejohn
- Department of Physics, Illinois Institute of Technology, Chicago, Illinois 60616
| | - J C Liu
- Institute of High Energy Physics, Beijing
| | - J L Liu
- Department of Physics and Astronomy, Shanghai Jiao Tong University, Shanghai Laboratory for Particle Physics and Cosmology, Shanghai
| | - J X Liu
- Institute of High Energy Physics, Beijing
| | - C Lu
- Joseph Henry Laboratories, Princeton University, Princeton, New Jersey 08544
| | - H Q Lu
- Institute of High Energy Physics, Beijing
| | - K B Luk
- Lawrence Berkeley National Laboratory, Berkeley, California 94720
- Department of Physics, University of California, Berkeley, California 94720
- The Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong
| | - B Z Ma
- Shandong University, Jinan
| | - X B Ma
- North China Electric Power University, Beijing
| | - X Y Ma
- Institute of High Energy Physics, Beijing
| | - Y Q Ma
- Institute of High Energy Physics, Beijing
| | - R C Mandujano
- Department of Physics and Astronomy, University of California, Irvine, California 92697
| | - C Marshall
- Lawrence Berkeley National Laboratory, Berkeley, California 94720
| | - K T McDonald
- Joseph Henry Laboratories, Princeton University, Princeton, New Jersey 08544
| | - R D McKeown
- California Institute of Technology, Pasadena, California 91125
- College of William and Mary, Williamsburg, Virginia 23187
| | - Y Meng
- Department of Physics and Astronomy, Shanghai Jiao Tong University, Shanghai Laboratory for Particle Physics and Cosmology, Shanghai
| | - J Napolitano
- Department of Physics, College of Science and Technology, Temple University, Philadelphia, Pennsylvania 19122
| | - D Naumov
- Joint Institute for Nuclear Research, Dubna, Moscow Region
| | - E Naumova
- Joint Institute for Nuclear Research, Dubna, Moscow Region
| | - T M T Nguyen
- Institute of Physics, National Chiao-Tung University, Hsinchu
| | - J P Ochoa-Ricoux
- Department of Physics and Astronomy, University of California, Irvine, California 92697
| | - A Olshevskiy
- Joint Institute for Nuclear Research, Dubna, Moscow Region
| | - H-R Pan
- Department of Physics, National Taiwan University, Taipei
| | - J Park
- Center for Neutrino Physics, Virginia Tech, Blacksburg, Virginia 24061
| | - S Patton
- Lawrence Berkeley National Laboratory, Berkeley, California 94720
| | - J C Peng
- Department of Physics, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801
| | - C S J Pun
- Department of Physics, The University of Hong Kong, Pokfulam, Hong Kong
| | - F Z Qi
- Institute of High Energy Physics, Beijing
| | - M Qi
- Nanjing University, Nanjing
| | - X Qian
- Brookhaven National Laboratory, Upton, New York 11973
| | - N Raper
- Sun Yat-Sen (Zhongshan) University, Guangzhou
| | - J Ren
- China Institute of Atomic Energy, Beijing
| | - C Morales Reveco
- Department of Physics and Astronomy, University of California, Irvine, California 92697
| | - R Rosero
- Brookhaven National Laboratory, Upton, New York 11973
| | - B Roskovec
- Charles University, Faculty of Mathematics and Physics, Prague
| | - X C Ruan
- China Institute of Atomic Energy, Beijing
| | - B Russell
- Lawrence Berkeley National Laboratory, Berkeley, California 94720
| | - H Steiner
- Lawrence Berkeley National Laboratory, Berkeley, California 94720
- Department of Physics, University of California, Berkeley, California 94720
| | - J L Sun
- China General Nuclear Power Group, Shenzhen
| | - T Tmej
- Charles University, Faculty of Mathematics and Physics, Prague
| | - K Treskov
- Joint Institute for Nuclear Research, Dubna, Moscow Region
| | - W-H Tse
- Chinese University of Hong Kong, Hong Kong
| | - C E Tull
- Lawrence Berkeley National Laboratory, Berkeley, California 94720
| | - B Viren
- Brookhaven National Laboratory, Upton, New York 11973
| | - V Vorobel
- Charles University, Faculty of Mathematics and Physics, Prague
| | - C H Wang
- National United University, Miao-Li
| | - J Wang
- Sun Yat-Sen (Zhongshan) University, Guangzhou
| | - M Wang
- Shandong University, Jinan
| | - N Y Wang
- Beijing Normal University, Beijing
| | - R G Wang
- Institute of High Energy Physics, Beijing
| | - W Wang
- Sun Yat-Sen (Zhongshan) University, Guangzhou
- College of William and Mary, Williamsburg, Virginia 23187
| | - X Wang
- College of Electronic Science and Engineering, National University of Defense Technology, Changsha
| | - Y Wang
- Nanjing University, Nanjing
| | - Y F Wang
- Institute of High Energy Physics, Beijing
| | - Z Wang
- Institute of High Energy Physics, Beijing
| | - Z Wang
- Department of Engineering Physics, Tsinghua University, Beijing
| | - Z M Wang
- Institute of High Energy Physics, Beijing
| | - H Y Wei
- Brookhaven National Laboratory, Upton, New York 11973
| | - L H Wei
- Institute of High Energy Physics, Beijing
| | - W Wei
- Shandong University, Jinan
| | - L J Wen
- Institute of High Energy Physics, Beijing
| | | | - C G White
- Department of Physics, Illinois Institute of Technology, Chicago, Illinois 60616
| | - H L H Wong
- Lawrence Berkeley National Laboratory, Berkeley, California 94720
- Department of Physics, University of California, Berkeley, California 94720
| | - E Worcester
- Brookhaven National Laboratory, Upton, New York 11973
| | - D R Wu
- Institute of High Energy Physics, Beijing
| | - Q Wu
- Shandong University, Jinan
| | - W J Wu
- Institute of High Energy Physics, Beijing
| | - D M Xia
- Chongqing University, Chongqing
| | - Z Q Xie
- Institute of High Energy Physics, Beijing
| | - Z Z Xing
- Institute of High Energy Physics, Beijing
| | - H K Xu
- Institute of High Energy Physics, Beijing
| | - J L Xu
- Institute of High Energy Physics, Beijing
| | - T Xu
- Department of Engineering Physics, Tsinghua University, Beijing
| | - T Xue
- Department of Engineering Physics, Tsinghua University, Beijing
| | - C G Yang
- Institute of High Energy Physics, Beijing
| | - L Yang
- Dongguan University of Technology, Dongguan
| | - Y Z Yang
- Department of Engineering Physics, Tsinghua University, Beijing
| | - H F Yao
- Institute of High Energy Physics, Beijing
| | - M Ye
- Institute of High Energy Physics, Beijing
| | - M Yeh
- Brookhaven National Laboratory, Upton, New York 11973
| | - B L Young
- Iowa State University, Ames, Iowa 50011
| | - H Z Yu
- Sun Yat-Sen (Zhongshan) University, Guangzhou
| | - Z Y Yu
- Institute of High Energy Physics, Beijing
| | - B B Yue
- Sun Yat-Sen (Zhongshan) University, Guangzhou
| | - V Zavadskyi
- Joint Institute for Nuclear Research, Dubna, Moscow Region
| | - S Zeng
- Institute of High Energy Physics, Beijing
| | - Y Zeng
- Sun Yat-Sen (Zhongshan) University, Guangzhou
| | - L Zhan
- Institute of High Energy Physics, Beijing
| | - C Zhang
- Brookhaven National Laboratory, Upton, New York 11973
| | - F Y Zhang
- Department of Physics and Astronomy, Shanghai Jiao Tong University, Shanghai Laboratory for Particle Physics and Cosmology, Shanghai
| | - H H Zhang
- Sun Yat-Sen (Zhongshan) University, Guangzhou
| | | | - J W Zhang
- Institute of High Energy Physics, Beijing
| | - Q M Zhang
- Department of Nuclear Science and Technology, School of Energy and Power Engineering, Xi'an Jiaotong University, Xi'an
| | - S Q Zhang
- Sun Yat-Sen (Zhongshan) University, Guangzhou
| | - X T Zhang
- Institute of High Energy Physics, Beijing
| | - Y M Zhang
- Sun Yat-Sen (Zhongshan) University, Guangzhou
| | - Y X Zhang
- China General Nuclear Power Group, Shenzhen
| | - Y Y Zhang
- Department of Physics and Astronomy, Shanghai Jiao Tong University, Shanghai Laboratory for Particle Physics and Cosmology, Shanghai
| | - Z J Zhang
- Dongguan University of Technology, Dongguan
| | - Z P Zhang
- University of Science and Technology of China, Hefei
| | - Z Y Zhang
- Institute of High Energy Physics, Beijing
| | - J Zhao
- Institute of High Energy Physics, Beijing
| | - R Z Zhao
- Institute of High Energy Physics, Beijing
| | - L Zhou
- Institute of High Energy Physics, Beijing
| | - H L Zhuang
- Institute of High Energy Physics, Beijing
| | - J H Zou
- Institute of High Energy Physics, Beijing
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Zhang D, He X, Cao J. [Progress of researches on antimalarial peptides]. Zhongguo Xue Xi Chong Bing Fang Zhi Za Zhi 2023; 35:191-198. [PMID: 37253570 DOI: 10.16250/j.32.1374.2023011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Subscribe] [Scholar Register] [Indexed: 06/01/2023]
Abstract
Malaria remains a major global public health concern, and nearly half of the global populations are still at risk of malaria infection. However, continuous emergence and spread of drug-resistant malaria parasite strains lead to ineffectiveness of conventional antimalarials. Therefore, development of novel antimalarial agents is of urgent need for malaria elimination. As an important component of the host natural immune defense system, antibacterial peptides provide the first line of defense against pathogenic invasion, and the mechanism of preferentially attacking the cell membrane makes them difficult to develop drug resistance. Antimicrobial peptides are therefore considered as a promising candidate for novel antimalarial agents. This review summarizes the advances in researches on antimicrobial peptides with antimalarial actions and discusses the potential of antimalarial peptides as novel antimalarials.
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Affiliation(s)
- D Zhang
- School of Public Health, Nanjing Medical University, Nanjing, Jiangsu 211166, China
- National Health Commission Key Laboratory on Parasitic Disease Control and Prevention, Jiangsu Provincial Key Laboratory on Parasite and Vector Control Technology, Jiangsu Institute of Parasitic Diseases, Wuxi, Jiangsu 214064, China
| | - X He
- School of Public Health, Nanjing Medical University, Nanjing, Jiangsu 211166, China
- National Health Commission Key Laboratory on Parasitic Disease Control and Prevention, Jiangsu Provincial Key Laboratory on Parasite and Vector Control Technology, Jiangsu Institute of Parasitic Diseases, Wuxi, Jiangsu 214064, China
| | - J Cao
- School of Public Health, Nanjing Medical University, Nanjing, Jiangsu 211166, China
- National Health Commission Key Laboratory on Parasitic Disease Control and Prevention, Jiangsu Provincial Key Laboratory on Parasite and Vector Control Technology, Jiangsu Institute of Parasitic Diseases, Wuxi, Jiangsu 214064, China
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Hu J, Tang X, Guo R, Wang Y, Shen H, Wang H, Yao Y, Cai X, Yu Z, Dong G, Liang F, Cao J, Zeng L, Su M, Kong W, Liu L, Huang W, Cai C, Xie Y, Mao W. 37P Pralsetinib in acquired RET fusion-positive advanced non-small cell lung cancer patients after resistance to EGFR/ALK-TKI: A China multi-center, real-world data (RWD) analysis. J Thorac Oncol 2023. [DOI: 10.1016/s1556-0864(23)00291-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/03/2023]
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Cao J, Sun D, Mu JH, Wang ZL, Tian FH, Guo LZ, Liu P. Application of combined anterior and posterior approaches for the treatment of cervical tuberculosis with anterior cervical abscess formation and kyphosis using a Jackson operating table: a case report and literature review. Eur Rev Med Pharmacol Sci 2023; 27:3448-3456. [PMID: 37140294 DOI: 10.26355/eurrev_202304_32115] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 05/05/2023]
Abstract
BACKGROUND There have been insufficient reports to date regarding the treatment of cervical spinal tuberculosis, and the optimal surgical approaches to treating this condition have yet to be established. CASE REPORT This report describes the treatment of a case of tuberculosis associated with a large abscess and pronounced kyphosis through the use of a combined anterior and posterior approach with the aid of the Jackson operating table. This patient did not exhibit any sensorimotor abnormalities of the upper extremities, lower extremities, or trunk, and presented with symmetrical bilateral hyperreflexia of the knee tendons, while being negative for Hoffmann's sign and Babinski's sign. Laboratory test results revealed an erythrocyte sedimentation rate (ESR) of 42.0 mm/h and a C-reactive protein (CRP) of 47.09 mg/L. Acid-fast staining was negative, and spine magnetic resonance imaging revealed the destruction of the C3-C4 vertebral body and a posterior convex deformity of the cervical spine. The patient reported a visual analog pain score (VAS) of 6, and exhibited an Oswestry disability index (ODI) score of 65. Jackson table-assisted anterior and posterior cervical resection decompression was performed to treat this patient, and at 3 months post-surgery the patient's VAS and ODI scores were respectively reduced to 2 and 17. Computed tomography analyses of the cervical spine at this follow-up time point revealed good structural fusion of the autologous iliac bone graft with internal fixation and improvement of the originally observed cervical kyphosis. CONCLUSIONS This case suggests that Jackson table-assisted anterior-posterior lesion removal and bone graft fusion can safely and effectively treat cervical tuberculosis with a large anterior cervical abscess combined with cervical kyphosis, providing a foundation for future efforts to treat spinal tuberculosis.
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Affiliation(s)
- J Cao
- Department of Orthopaedics, China-Japan Union Hospital of Jilin University, Changchun, Jilin, China.
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40
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Chen X, Cao S, Fu X, Ni Y, Huang B, Wu J, Chen L, Huang S, Cao J, Yu W, Ye H. The risk factors for Group B Streptococcus colonization during pregnancy and influences of intrapartum antibiotic prophylaxis on maternal and neonatal outcomes. BMC Pregnancy Childbirth 2023; 23:207. [PMID: 36973793 PMCID: PMC10041798 DOI: 10.1186/s12884-023-05478-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2022] [Accepted: 02/28/2023] [Indexed: 03/29/2023] Open
Abstract
BACKGROUND Group B Streptococcus (GBS), also referred as Streptococcus agalactiae, is one of the leading causes of life-threatening invasive diseases such as bacteremia, meningitis, pneumonia and urinary tract infection in pregnant women and neonates. Rates of GBS colonization vary by regions, but large-sample studies on maternal GBS status are limited in southern China. As a result, the prevalence of GBS among pregnant women and its associated risk factors and the efficacy of intrapartum antibiotic prophylaxis (IAP) intervention in preventing adverse pregnancy and neonatal outcomes remain poorly understood in southern China. METHODS To fill this gap, we retrospectively analyzed demographic and obstetrical data of pregnant women who have undergone GBS screening and delivered between 2016 and 2018 in Xiamen, China. A total of 43,822 pregnant women were enrolled and only a few GBS-positive women did not receive IAP administration. Possible risk factors for GBS colonization were assayed by univariate and multivariate logistic regression analysis. Generalized linear regression model was applicated to analyze whether IAP is one of the impact factors of the hospital length of stay of the target women. RESULTS The overall GBS colonization rate was 13.47% (5902/43,822). Although women > 35 years old (P = 0.0363) and women with diabetes mellitus (DM, P = 0.001) had a higher prevalence of GBS colonization, the interaction between ages and GBS colonization was not statistically significant in Logistic Regression analysis (adjusted OR = 1.0014; 95% CI, 0.9950, 1.0077). The rate of multiple births was significantly dropped in GBS-positive group than that of GBS-negative group (P = 0.0145), with no significant difference in the rate of fetal reduction (P = 0.3304). Additionally, the modes of delivery and the incidences of abortion, premature delivery, premature rupture of membranes, abnormal amniotic fluid and puerperal infection were not significantly different between the two groups. The hospitalization stays of the subjects were not influenced by GBS infection. As for neonatal outcomes, the cases of fetal death in maternal GBS-positive group did not statistically differ from that in maternal GBS-negative group. CONCLUSION Our data identified that pregnant women with DM are at high risk of GBS infection and IAP is highly effective in prevention of adverse pregnancy and neonatal outcomes. This stressed the necessity of universal screening of maternal GBS status and IAP administration to the target population in China, and women with DM should be considered as priorities.
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Affiliation(s)
- Xiaoli Chen
- Department of Clinical Laboratory, Women and Children's Hospital, School of Medicine, Xiamen University, Xiamen, China
- School of Basic Medical Science, Fujian Medical University, Fuzhou, China
| | - Sijia Cao
- Department of Clinical Laboratory, Women and Children's Hospital, School of Medicine, Xiamen University, Xiamen, China
| | - Xiaochun Fu
- Department of Clinical Laboratory, Women and Children's Hospital, School of Medicine, Xiamen University, Xiamen, China
| | - Yan Ni
- Department of Obstetrics, Women and Children's Hospital, School of Medicine, Xiamen University, Xiamen, China
| | - Bixuan Huang
- School of Public Health, Xiamen University, Xiamen, China
| | - Jiayin Wu
- Department of Clinical Laboratory, Women and Children's Hospital, School of Medicine, Xiamen University, Xiamen, China
| | - Ling Chen
- Department of Clinical Laboratory, Women and Children's Hospital, School of Medicine, Xiamen University, Xiamen, China
| | - Shuying Huang
- Department of Clinical Laboratory, Women and Children's Hospital, School of Medicine, Xiamen University, Xiamen, China
| | - Jiali Cao
- Department of Clinical Laboratory, Women and Children's Hospital, School of Medicine, Xiamen University, Xiamen, China
| | - Weiwei Yu
- Department of Obstetrics, Women and Children's Hospital, School of Medicine, Xiamen University, Xiamen, China.
| | - Huiming Ye
- Department of Clinical Laboratory, Women and Children's Hospital, School of Medicine, Xiamen University, Xiamen, China.
- School of Public Health, Xiamen University, Xiamen, China.
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Cao J, Ji Q. 25P RC48-ADC for metastatic salivary duct carcinoma with HER2-expression: A single-center retrospective study. ESMO Open 2023. [DOI: 10.1016/j.esmoop.2023.101046] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/05/2023] Open
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Cao J, Ji Q. 6P RC48-ADC for metastatic salivary duct carcinoma with HER2 expression: A single-center retrospective study. ESMO Open 2023. [DOI: 10.1016/j.esmoop.2023.100972] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/05/2023] Open
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Jiang T, Hu Y, Cao J. [The role of sinusoidal endothelial cells in liver injury: a review]. Zhongguo Xue Xi Chong Bing Fang Zhi Za Zhi 2023; 35:92-97. [PMID: 36974022 DOI: 10.16250/j.32.1374.2022118] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Subscribe] [Scholar Register] [Indexed: 03/29/2023]
Abstract
Liver sinusoidal endothelial cells (LSECs) locate on the surface of hepatic sinusoids. As the first line of defense between the liver and blood, LSECs are the most abundant non-parenchymal cells in the liver. Under physiological conditions, LSECs may induce liver immune tolerance through participating in substance transport and metabolic waste removal, thereby maintaining liver homeostasis, and under pathological conditions, LSECs may promote liver immune response via antigen presentation. LSECs have been found to play a crucial regulatory role in maintaining the balance between liver regeneration and liver fibrosis. This article reviews the progress of researches on LSECs functions, LSECs changes in liver injury, signal pathways associated with regulation of LSECs functions, and the interaction between LSECs and other types of cells in the liver, aiming to elucidate the function of LSECs and their roles in liver diseases.
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Affiliation(s)
- T Jiang
- National Institute of Parasitic Diseases, Chinese Center for Disease Control and Prevention (Chinese Center for Tropical Diseases Research); National Health Commission Key Laboratory of Parasite and Vector Biology; WHO Collaborating Centre for Tropical Diseases; National Center for International Research on Tropical Diseases, Shanghai 200025, China
| | - Y Hu
- National Institute of Parasitic Diseases, Chinese Center for Disease Control and Prevention (Chinese Center for Tropical Diseases Research); National Health Commission Key Laboratory of Parasite and Vector Biology; WHO Collaborating Centre for Tropical Diseases; National Center for International Research on Tropical Diseases, Shanghai 200025, China
| | - J Cao
- National Institute of Parasitic Diseases, Chinese Center for Disease Control and Prevention (Chinese Center for Tropical Diseases Research); National Health Commission Key Laboratory of Parasite and Vector Biology; WHO Collaborating Centre for Tropical Diseases; National Center for International Research on Tropical Diseases, Shanghai 200025, China
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Zeng XL, Chen Q, Yang H, Cao J, Zhou NY. [Research progress on the relationship between air pollution and gestational diabetes]. Zhonghua Yu Fang Yi Xue Za Zhi 2023; 57:159-165. [PMID: 36797571 DOI: 10.3760/cma.j.cn112150-20220218-00153] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Subscribe] [Scholar Register] [Indexed: 02/18/2023]
Abstract
Gestational diabetes mellitus (GDM) is one of the most common pregnancy complications and has serious implications for the health of mothers and their offspring. In recent years, studies have confirmed that air pollution is one of the main risk factors for diabetes, and there is increasing evidence that air pollution exposure is closely related to the occurrence of gestational diabetes. However, current studies on the association between air pollutant exposure and the incidence of gestational diabetes are inconsistent, and the window period of pollutant exposure is still unclear. Limited mechanistic studies suggest that airborne particulate matter and gaseous pollutants may affect GDM through multiple mechanisms, including inflammation, oxidative stress, disruption of adipokine secretion, and imbalance of intestinal flora. This review summarizes the relationship between air pollutant exposure and the incidence of GDM in recent years, as well as the possible molecular mechanism of the occurrence and development of GDM caused by air pollutants, in order to provide scientific basis for preventing pollutant exposure, reducing the risk of GDM, improving maternal and fetal outcomes and improving the quality of the birth population.
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Affiliation(s)
- X L Zeng
- Institute of Toxicology, Facutly of Military Preventive Medicine, Army Medical University (Third Military Medical University), Chongqing 400038, China School of Public Health, China Medical University, Shenyang 110122, China
| | - Q Chen
- Institute of Toxicology, Facutly of Military Preventive Medicine, Army Medical University (Third Military Medical University), Chongqing 400038, China
| | - H Yang
- Institute of Toxicology, Facutly of Military Preventive Medicine, Army Medical University (Third Military Medical University), Chongqing 400038, China
| | - J Cao
- Institute of Toxicology, Facutly of Military Preventive Medicine, Army Medical University (Third Military Medical University), Chongqing 400038, China
| | - N Y Zhou
- Chongqing Health Center for Women and Children/Department of Clinical Research Center of Science and Education, Women and Children's Hospital of Chongqing Medical University, Chongqing 400021, China
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Wang M, Yang X, Meng Y, Jin Z, Cao J, Xiong L, Xiong Z. Comprehensive analysis of the tumor-promoting effect and immune infiltration correlation MAZ from pan-cancer to hepatocellular carcinoma. Int Immunopharmacol 2023; 115:109660. [PMID: 36623412 DOI: 10.1016/j.intimp.2022.109660] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2022] [Revised: 12/19/2022] [Accepted: 12/28/2022] [Indexed: 01/09/2023]
Abstract
BACKGROUND Myc-associated zinc-finger protein (MAZ) is a transcription factor, which has been confirmed to be abnormally expressed in many tumors and involved in regulating the proliferation, migration, apoptosis, and autophagy of tumor cells. Currently, there is a lack of comprehensive analysis of MAZ in pan-cancer, and the mechanism of MAZ in hepatocellular carcinoma (HCC) and its association with immunotherapy remains unclear. METHODS The expression, prognostic mutation, sCNA, and tumor immunity characteristics of MAZ in 33 types of tumors were analyzed by The Cancer Genome Atlas (TCGA), GEPIA, and TIMER databases. The association of MAZ expression levels with drug sensitivity, immunotherapy, immune checkpoints, and HLA-associated genes was further analyzed. Transwell, CCK-8, wound healing, and flow cytometry verified that MAZ affected the malignant cell behavior of HCC. The signaling pathways and cellular functions affected by MAZ in HCC were revealed by GSEA enrichment analysis. RESULTS The expression level of MAZ was up-regulated, and the high expression of MAZ indicated a high-risk prognostic factor in most tumors, including ACC, BLCA, KIRP, LIHC, PRAD, SKCM, and THCA (p < 0.05). MAZ expression was positively correlated with the sensitivity of most chemotherapy drugs (p < 0.05). HLA-DQB2, HLA-H, and most immune checkpoint genes were remarkably up-regulated in the high MAZ expression group (p < 0.05). GSEA analysis revealed that MAZ expression was highly correlated with the intracellular immune-related functions and cancer-related signaling pathway, including the B cell receptor signaling pathway, complement activation, humoral immune response, TGF-β signaling pathway, and Wnt signaling pathway. The overexpression of MAZ in HCC cells could promote the abilities of cell proliferation and migration and inhibit tumor cell apoptosis. CONCLUSION Our study revealed that MAZ might play a role in promoting the progression of HCC. It was closely related to the tumor microenvironment, immune cell infiltration, and immune escape in pan-cancer. Moreover, this study provides new insights into MAZ as a prognostic marker and potential therapeutic target in HCC.
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Affiliation(s)
- Mengmeng Wang
- Division of Gastroenterology, Liyuan Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430077, Hubei, China
| | - Xiongjun Yang
- Division of Gastroenterology, Liyuan Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430077, Hubei, China
| | - Yajun Meng
- Division of Gastroenterology, Liyuan Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430077, Hubei, China
| | - Ze Jin
- Division of Gastroenterology, Liyuan Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430077, Hubei, China
| | - Jiali Cao
- Division of Gastroenterology, Liyuan Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430077, Hubei, China
| | - Lina Xiong
- Division of Gastroenterology, Liyuan Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430077, Hubei, China
| | - Zhifan Xiong
- Division of Gastroenterology, Liyuan Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430077, Hubei, China.
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Li Y, Luo B, Tong B, Xie Z, Cao J, Bai X, Peng Y, Wu Y, Wang W, Qi X. The role and molecular mechanism of gut microbiota in Graves' orbitopathy. J Endocrinol Invest 2023; 46:305-317. [PMID: 35986869 DOI: 10.1007/s40618-022-01902-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/05/2022] [Accepted: 08/10/2022] [Indexed: 01/25/2023]
Abstract
PURPOSE Graves' orbitopathy (GO) is an autoimmune orbital disorder. Gut microbiota dysfunction plays a vital role in autoimmune diseases, including Graves' disease (GD) and GO. In the present study, we aimed to investigate the change of gut microbiota in GD/GO using mouse model. METHODS The murine model of GD/GO was established by the challenge of adenovirus expressing thyroid-stimulating hormone (TSH) receptor (TSHR) (Ad-TSHR). The histological changes of orbital and thyroid tissues were analyzed by hematoxylin and eosin (H&E), Masson staining, and immunohistochemistry (IHC) staining. The fecal samples were collected for 16S rRNA gene sequencing and bioinformatics analysis. RESULTS The GD/GO model was established successfully, as manifested as the broadened eyelid, exophthalmia and conjunctive redness, severe inflammatory infiltration among thyroid glands and between extraocular muscle space, hypertrophic extraocular muscles, elevated thyroxine (T4) and decreased TSH, and positive CD34, CD40, collagen I, and α-SMA staining. A total of 222 operational taxonomic units (OUTs) were overlapped between mice in the Ad-NC and Ad-TSHR groups. The microbial composition of the samples in the two groups was mainly Bacteroidia and Clostridia, and the Ad-NC group had a significantly lower content of Bacteroidia and higher content of Clostridia. KEGG orthology analysis results revealed differences in dehydrogenase, aspartic acid, bile acid, chalcone synthase, acetyltransferase, glutamylcyclotransferase, glycogenin, and 1-phosphatidylinositol-4-phosphate 5-kinase between two groups; enzyme commission (EC) analysis results revealed differences in several dehydrogenase, oxidase, thioxy/reductase between two groups; MetaCyc pathways analysis results revealed differences in isoleucine degradation, oxidation of C1 compounds, tricarboxylic acid (TCA) cycle IV, taurine degradation, and biosynthesis of paromamine, heme, colonic acid building blocks, butanediol, lysine/threonine/methionine, and histidine/purine/pyrimidine between two groups. CONCLUSION This study induced a mouse model of GD/GO by Ad-TSHR challenge, and gut microbiota characteristics were identified in the GD/GO mice. The Bacteroidia and Clostridia abundance was changed in the GD/GO mice. These findings may lay a solid experimental foundation for developing personalized treatment regimens for GD patients according to the individual gut microbiota. Given the potential impact of regional differences on intestinal microbiota, this study in China may provide a reference for the global overview of the gut-thyroid axis hypothesis.
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Affiliation(s)
- Y Li
- Department of Ophthalmology, The Second Xiangya Hospital, Central South University, No. 139, Renmin Middle Road, Changsha, 410011, Hunan, China
| | - B Luo
- Department of Ophthalmology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, Hubei, China
| | - B Tong
- Department of Ophthalmology, The Second Xiangya Hospital, Central South University, No. 139, Renmin Middle Road, Changsha, 410011, Hunan, China
| | - Z Xie
- Department of Ophthalmology, The Second Xiangya Hospital, Central South University, No. 139, Renmin Middle Road, Changsha, 410011, Hunan, China
| | - J Cao
- Department of Ophthalmology, The Third Xiangya Hospital, Central South University, Changsha, 410013, Hunan, China
| | - X Bai
- Department of Ophthalmology, The Second Xiangya Hospital, Central South University, No. 139, Renmin Middle Road, Changsha, 410011, Hunan, China
| | - Y Peng
- Department of Ophthalmology, The Second Xiangya Hospital, Central South University, No. 139, Renmin Middle Road, Changsha, 410011, Hunan, China
| | - Y Wu
- Department of Ophthalmology, The Second Xiangya Hospital, Central South University, No. 139, Renmin Middle Road, Changsha, 410011, Hunan, China
| | - W Wang
- Department of Ophthalmology, Xiangya Hospital, Central South University, Changsha, 410000, Hunan, China
| | - X Qi
- Department of Ophthalmology, The Second Xiangya Hospital, Central South University, No. 139, Renmin Middle Road, Changsha, 410011, Hunan, China.
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Zhang J, Zhao S, Xing X, Shang L, Cao J, He Y. Effects of Neuropeptides on Dendritic Cells in the Pathogenesis of Psoriasis. J Inflamm Res 2023; 16:35-43. [PMID: 36636251 PMCID: PMC9831526 DOI: 10.2147/jir.s397079] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2022] [Accepted: 12/24/2022] [Indexed: 01/06/2023] Open
Abstract
Psoriasis is an autoimmune disease that is characterized by discolored, scaled patches of skin. Clinically, it is found that psychological factors often induce or aggravate the disease. Current research suggests that the pathogenesis of psoriasis involves the nervous and immune systems. This article reviews how neuropeptides secreted by nerve fibers affect dendritic cells in psoriasis. In this review, we describe that the neuropeptides calcitonin gene-related peptide, substance P, and vasoactive intestinal peptide can act on dendritic cells and participate in the pathogenesis of psoriasis. These neuropeptides can affect the secretion of interleukin (IL)-12 and IL-23 by dendritic cells, which stimulate T helper (Th)1, Th17, and Th22 cells to produce immune responses and cause the manifestation of psoriasis. The application of neuropeptide inhibitors can improve the skin lesions of psoriasis, which has been confirmed in clinical trials. Therefore, neuroimmune response may be a new direction to develop new drug treatments and perspectives in the development of psoriasis.
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Affiliation(s)
- Jingya Zhang
- Department of Dermatology, Beijing Chaoyang Hospital, Capital Medical University, Beijing, People’s Republic of China
| | - Siqi Zhao
- Department of Dermatology, Beijing Chaoyang Hospital, Capital Medical University, Beijing, People’s Republic of China
| | - Xinzhu Xing
- Department of Dermatology, Beijing Chaoyang Hospital, Capital Medical University, Beijing, People’s Republic of China
| | - Lin Shang
- Department of Dermatology, Beijing Chaoyang Hospital, Capital Medical University, Beijing, People’s Republic of China
| | - Jiali Cao
- Department of Dermatology, Beijing Chaoyang Hospital, Capital Medical University, Beijing, People’s Republic of China
| | - Yanling He
- Department of Dermatology, Beijing Chaoyang Hospital, Capital Medical University, Beijing, People’s Republic of China,National Clinical Research Center for Skin and Immune Diseases, Branch in Beijing Chaoyang Hospital, Beijing, People’s Republic of China,Correspondence: Yanling He, Department of Dermatology, Beijing Chaoyang Hospital, Capital Medical University, 8 Gongti South Road, Chaoyang District, Beijing, 100020, People’s Republic of China, Tel/Fax +86-10-85231889, Email
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Zhang Q, Xin Guo Z, Zhang J, Yang DL, Jiang P, Cao J, Li S. Effect of Trichostatin A on Bleomycin Induced Pulmonary Fibrosis in Mice and its Mechanism. Indian J Pharm Sci 2023. [DOI: 10.36468/pharmaceutical-sciences.spl.630] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/19/2023] Open
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Wang Z, Zhu M, Huang Y, Cao J, Xiong Z. High blood pressure mediated the effect of fasting insulin level on nonalcoholic fatty liver disease risk: A Mendelian randomization study. Digit Health 2023; 9:20552076231216682. [PMID: 38025107 PMCID: PMC10666686 DOI: 10.1177/20552076231216682] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 11/08/2023] [Indexed: 12/01/2023] Open
Abstract
Objective The interactions between fasting insulin levels, high blood pressure and nonalcoholic fatty liver disease (NAFLD) are still unclear. We examined the causal mechanisms between these three cardiometabolic traits using Mendelian randomization (MR) approach by utilizing genetic instruments. Methods Three different genome-wide association studies resources of European ancestry were utilized for the present study. Two-sample MRs were used to assess causal effects between fasting insulin levels, high blood pressure and NAFLD. Multivariate MR was used to calculate the mediating effect. The inverse variance-weighted method was used as the main analysis method. Results Our study confirmed a causal effect of fasting insulin levels (IVW-OR = 9.54, P = 0.001) and high blood pressure (IVW-OR = 3.926, P = 0.005) on NAFLD risk. And fasting insulin level was positively casually associated with high blood pressure risk (IVW-OR = 1.170, P < 0.001). However, the impact of high blood pressure on fasting insulin levels was still uncertain because of the presence of horizontal pleiotropy. Reverse MR showed NAFLD had a positive correlation with fasting insulin levels (IVW-OR = 1.010, P < 0.001) and a negative causal effect on high blood pressure risk (IVW-OR = 0.997, P = 0.037). Combined the multivariate MR result revealed high blood pressure partially mediated the contribution of fasting insulin level to NAFLD risk (proportion mediated: 9.091%). Conclusions Our study suggests there is a bidirectional causal relationship between fasting insulin levels and NAFLD. High blood pressure seems to play a mediating role in the development of NAFLD caused by changes in fasting insulin levels. However, it is uncertain whether high blood pressure is a mediator between NAFLD and the risk of fasting insulin level.
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Affiliation(s)
- Ziwen Wang
- Department of Gastroenterology, Liyuan Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Mengpei Zhu
- Department of Gastroenterology, Liyuan Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Yumei Huang
- Department of Gastroenterology, Liyuan Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Jiali Cao
- Department of Gastroenterology, Liyuan Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Zhifan Xiong
- Department of Gastroenterology, Liyuan Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
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Ren X, Wang MM, Wang G, Sun XM, Xia TT, Yao Y, Wang CC, Jiang AF, Wang H, Cao J, Wei YJ, Sun CG. A nomogram for predicting overall survival in patients with type II endometrial carcinoma: a retrospective analysis and multicenter validation study. Eur Rev Med Pharmacol Sci 2023; 27:233-247. [PMID: 36647873 DOI: 10.26355/eurrev_202301_30904] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Subscribe] [Scholar Register] [Indexed: 01/18/2023]
Abstract
OBJECTIVE Type II endometrial cancer (EC) is associated with high risk of metastasis and poor prognosis. We aimed to develop a nomogram for predicting survival probability in patients with type II EC. PATIENTS AND METHODS Data from a total of 4,117 patients with confirmed type II EC were pulled from the Surveillance, Epidemiology, and End Results (SEER) database, and were randomly divided into a training set and an internal verification set. A nomogram was constructed based on the training set. The concordance index (C-index), area under the ROC curve, and calibration plots were used to evaluate the identification and calibration of the nomogram. The SEER internal validation set and the Chinese multicenter data set (74 patients) were used to verify discriminations and corrections of the model. RESULTS Multivariate analysis indicated that age, marital status, tumor size, T stage, N stage, M stage, surgery, radiotherapy, and chemotherapy were independent factors affecting the prognosis of type II EC patients (p<0.001). The corresponding nomogram has showed excellent calibration and discrimination (C-index [95% CI], 0.752 [0.738-0.766]). The model was verified in the internal verification set (0.760 [0.739-0.781]) and the Chinese multicenter set (0.784 [0.607-0.961]). In addition, the AUC further confirmed the accuracy of the nomogram in predicting survival. The calibration curve of OS within 5 years confirmed good calibration of the nomogram. CONCLUSIONS This model and the corresponding risk classification system may provide useful tools for clinicians to evaluate the long-term prognosis of patients and carry out personalized clinical evaluation.
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Affiliation(s)
- X Ren
- Clinical Medical Colleges, Weifang Medical University, Weifang, China.
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