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Wang H, Zhang L, Li X, Sun M, Jiang M, Shi X, Xu X, Ding M, Chen B, Yu H, Li Z, Guo D, Yang W. Machine learning prediction for constructing a universal multidimensional information library of Panax saponins (ginsenosides). Food Chem 2024; 439:138106. [PMID: 38056336 DOI: 10.1016/j.foodchem.2023.138106] [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: 12/22/2022] [Revised: 11/22/2023] [Accepted: 11/26/2023] [Indexed: 12/08/2023]
Abstract
Accurate characterization of Panax herb ginsenosides is challenging because of the isomers and lack of sufficient reference compounds. More structural information could help differentiate ginsenosides and their isomers, enabling more accurate identification. Based on the VionTM ion-mobility high-resolution LC-MS platform, a multidimensional information library for ginsenosides, namely GinMIL, was established by predicting retention time (tR) and collision cross section (CCS) through machine learning. Robustness validation experiments proved tR and CCS were suitable for database construction. Among three machine learning models we attempted, gradient boosting machine (GBM) exhibited the best prediction performance. GinMIL included the multidimensional information (m/z, molecular formula, tR, CCS, and some MS/MS fragments) for 579 known ginsenosides. Accuracy in identifying ginsenosides from diverse ginseng products was greatly improved by a unique LC-MS approach and searching GinMIL, demonstrating a universal Panax saponins library constructed based on hierarchical design. GinMIL could improve the accuracy of isomers identification by approximately 88%.
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Affiliation(s)
- Hongda Wang
- National Key Laboratory of Chinese Medicine Modernization, State Key Laboratory of Component-based Chinese Medicine, Tianjin University of Traditional Chinese Medicine, 10 Poyanghu Road, Tianjin 301617, China; Haihe Laboratory of Modern Chinese Medicine, Tianjin University of Traditional Chinese Medicine, 10 Poyanghu Road, Tianjin 301617, China
| | - Lin Zhang
- National Key Laboratory of Chinese Medicine Modernization, State Key Laboratory of Component-based Chinese Medicine, Tianjin University of Traditional Chinese Medicine, 10 Poyanghu Road, Tianjin 301617, China; Haihe Laboratory of Modern Chinese Medicine, Tianjin University of Traditional Chinese Medicine, 10 Poyanghu Road, Tianjin 301617, China
| | - Xiaohang Li
- National Key Laboratory of Chinese Medicine Modernization, State Key Laboratory of Component-based Chinese Medicine, Tianjin University of Traditional Chinese Medicine, 10 Poyanghu Road, Tianjin 301617, China; Haihe Laboratory of Modern Chinese Medicine, Tianjin University of Traditional Chinese Medicine, 10 Poyanghu Road, Tianjin 301617, China
| | - Mengxiao Sun
- National Key Laboratory of Chinese Medicine Modernization, State Key Laboratory of Component-based Chinese Medicine, Tianjin University of Traditional Chinese Medicine, 10 Poyanghu Road, Tianjin 301617, China; Haihe Laboratory of Modern Chinese Medicine, Tianjin University of Traditional Chinese Medicine, 10 Poyanghu Road, Tianjin 301617, China
| | - Meiting Jiang
- National Key Laboratory of Chinese Medicine Modernization, State Key Laboratory of Component-based Chinese Medicine, Tianjin University of Traditional Chinese Medicine, 10 Poyanghu Road, Tianjin 301617, China; Haihe Laboratory of Modern Chinese Medicine, Tianjin University of Traditional Chinese Medicine, 10 Poyanghu Road, Tianjin 301617, China
| | - Xiaojian Shi
- Cellular & Molecular Physiology, Yale School of Medicine, 850 Yale West Campus, West Haven CT 06516, USA
| | - Xiaoyan Xu
- National Key Laboratory of Chinese Medicine Modernization, State Key Laboratory of Component-based Chinese Medicine, Tianjin University of Traditional Chinese Medicine, 10 Poyanghu Road, Tianjin 301617, China; Haihe Laboratory of Modern Chinese Medicine, Tianjin University of Traditional Chinese Medicine, 10 Poyanghu Road, Tianjin 301617, China
| | - Mengxiang Ding
- National Key Laboratory of Chinese Medicine Modernization, State Key Laboratory of Component-based Chinese Medicine, Tianjin University of Traditional Chinese Medicine, 10 Poyanghu Road, Tianjin 301617, China; Haihe Laboratory of Modern Chinese Medicine, Tianjin University of Traditional Chinese Medicine, 10 Poyanghu Road, Tianjin 301617, China
| | - Boxue Chen
- National Key Laboratory of Chinese Medicine Modernization, State Key Laboratory of Component-based Chinese Medicine, Tianjin University of Traditional Chinese Medicine, 10 Poyanghu Road, Tianjin 301617, China; Haihe Laboratory of Modern Chinese Medicine, Tianjin University of Traditional Chinese Medicine, 10 Poyanghu Road, Tianjin 301617, China
| | - Heshui Yu
- National Key Laboratory of Chinese Medicine Modernization, State Key Laboratory of Component-based Chinese Medicine, Tianjin University of Traditional Chinese Medicine, 10 Poyanghu Road, Tianjin 301617, China; Haihe Laboratory of Modern Chinese Medicine, Tianjin University of Traditional Chinese Medicine, 10 Poyanghu Road, Tianjin 301617, China
| | - Zheng Li
- National Key Laboratory of Chinese Medicine Modernization, State Key Laboratory of Component-based Chinese Medicine, Tianjin University of Traditional Chinese Medicine, 10 Poyanghu Road, Tianjin 301617, China; Haihe Laboratory of Modern Chinese Medicine, Tianjin University of Traditional Chinese Medicine, 10 Poyanghu Road, Tianjin 301617, China
| | - Dean Guo
- National Key Laboratory of Chinese Medicine Modernization, State Key Laboratory of Component-based Chinese Medicine, Tianjin University of Traditional Chinese Medicine, 10 Poyanghu Road, Tianjin 301617, China; Haihe Laboratory of Modern Chinese Medicine, Tianjin University of Traditional Chinese Medicine, 10 Poyanghu Road, Tianjin 301617, China; Shanghai Research Center for Modernization of Traditional Chinese Medicine, National Engineering Laboratory for TCM Standardization Technology, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, 501 Haike Road, Shanghai 201203, China.
| | - Wenzhi Yang
- National Key Laboratory of Chinese Medicine Modernization, State Key Laboratory of Component-based Chinese Medicine, Tianjin University of Traditional Chinese Medicine, 10 Poyanghu Road, Tianjin 301617, China; Haihe Laboratory of Modern Chinese Medicine, Tianjin University of Traditional Chinese Medicine, 10 Poyanghu Road, Tianjin 301617, China.
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Shao S, Liao H, Zhou S, Li Y, Yu H, Dai X, Zhu Q, Hua Y, Wang C, Zhou K. Isolated non-immune mediated second-degree atrioventricular block in fetus: natural history and predictive factors for spontaneous recovery. Ultrasound Obstet Gynecol 2024. [PMID: 38642334 DOI: 10.1002/uog.27662] [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] [Subscribe] [Scholar Register] [Received: 09/02/2023] [Revised: 03/29/2024] [Accepted: 04/03/2024] [Indexed: 04/22/2024]
Abstract
OBJECTIVES To uncover the clinical course of fetal isolated non-immune mediated second-degree AVB and determine the factors associated with the spontaneous recovery for fetal non-immune second-degree atrioventricular block (AVB). METHODS A total of 20 fetuses with isolated, non-immune mediated second-degree AVB were prospectively recruited between 2014 and 2022. These fetuses were divided into the spontaneous recovery group (n=12) and the non-spontaneous recovery group (n=8). Maternal and fetal basic characteristics, intrauterine and postnatal outcomes were compared between groups. RESULTS Twelve fetuses restored 1:1 atrioventricular conduction in utero and did not recur during the postnatal follow-up period. The residual eight fetuses maintained as second-degree AVB and six of them were aborted due to parental request in utero. Of the two live children with second-degree AVB, one of them progressed to complete AVB at the latest follow up at the age of 34 months, but without any symptoms, heart enlargement or dysfunction. The residual one progressed to complete AVB and was finally diagnosed with type 2 long-QT syndrome. Fetuses in the spontaneous recovery group presented with earlier gestational age at diagnosis (20.0[17.0-26.0] vs. 24.5[18.0-35.0] weeks, p=0.004) and higher atrial rate (147[130-160] vs 138.00[125.00-149.00] bpm, p=0.006) in comparison with the non-spontaneous recovery group. A cut-off value of 22.5 weeks of gestational age and 144 bpm of atrial rate at diagnosis could predict the failure of spontaneous recovery, with sensitivities of 87.5%, 75%, and specificities of 92.0%, 87.5%, respectively. CONCLUSIONS The outcome of fetal non-immune second-degree AVB was favorable. Earlier gestational age at diagnosis and higher atrial rate were related to spontaneous reversion for isolated non-immune-mediated second-degree AVB. However, prenatal gene test should be performed for those with persistent AVB to exclude the heritable disorders including LQTS. These findings may provide important references for clinical management and prenatal counseling. This article is protected by copyright. All rights reserved.
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Affiliation(s)
- S Shao
- Key Laboratory of Birth Defects and Related Diseases of Women and Children of MOE, Department of Pediatrics, West China Second University Hospital, Sichuan University, Chengdu, China
| | - H Liao
- Key Laboratory of Birth Defects and Related Diseases of Women and Children of MOE, Department of Pediatrics, West China Second University Hospital, Sichuan University, Chengdu, China
| | - S Zhou
- Department of Obstetrics, West China Second University Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Y Li
- Key Laboratory of Birth Defects and Related Diseases of Women and Children of MOE, Department of Pediatrics, West China Second University Hospital, Sichuan University, Chengdu, China
| | - H Yu
- Department of Obstetrics, West China Second University Hospital, Sichuan University, Chengdu, Sichuan, China
| | - X Dai
- Department of Ultrasound, West China Second University Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Q Zhu
- Department of Ultrasound, West China Second University Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Y Hua
- Key Laboratory of Birth Defects and Related Diseases of Women and Children of MOE, Department of Pediatrics, West China Second University Hospital, Sichuan University, Chengdu, China
| | - C Wang
- Key Laboratory of Birth Defects and Related Diseases of Women and Children of MOE, Department of Pediatrics, West China Second University Hospital, Sichuan University, Chengdu, China
| | - K Zhou
- Key Laboratory of Birth Defects and Related Diseases of Women and Children of MOE, Department of Pediatrics, West China Second University Hospital, Sichuan University, Chengdu, China
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Dong J, Zhang H, Ai X, Dong Q, Shi X, Zhao X, Zhong C, Yu H. Improving chilling tolerance of peanut seedlings by enhancing antioxidant-modulated ROS scavenging ability, alleviating photosynthetic inhibition, and mobilizing nutrient absorption. Plant Biol (Stuttg) 2024. [PMID: 38597809 DOI: 10.1111/plb.13643] [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] [Subscribe] [Scholar Register] [Received: 11/18/2023] [Accepted: 02/21/2024] [Indexed: 04/11/2024]
Abstract
Peanut production is threatened by climate change. Damage to seedlings from low temperatures in early spring can limit yield. Plant adaptations to chilling stress remain unclear in peanut seedlings. It is essential to understand how peanut acquires chilling tolerance. We evaluated effects of chilling stress on growth and recovery of peanut seedlings. We compared and analysed biological characteristics, antioxidants, photosynthesis, biochemical and physiological responses, and nutrient absorption at varying levels of chilling. Compared with chilling-sensitive FH18, the reduced impact of chilling stress on chilling-tolerant NH5 was associated with reduced ROS accumulation, higher ascorbate peroxidase activity and soluble sugar content, lower soluble protein content, and smaller reductions in nutrient content during stress. After removal of chilling stress, FH18 had significant accumulation of O2 •- and H2O2, which decreased photosynthesis, nutrient absorption, and transport. ROS-scavenging reduced damage from chilling stress, allowed remobilization of nutrients, improved chilling tolerance, and restored plant functioning after chilling stress removal. These findings provide a reference for targeted research on peanut seedling tolerance to chilling and lay the foundation for bioinformatics-based research on peanut chilling tolerance mechanisms.
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Affiliation(s)
- J Dong
- College of Agronomy, Peanut Research Institute, Shenyang Agricultural University, Shenyang, Liaoning Province, China
| | - H Zhang
- College of Agronomy, Peanut Research Institute, Shenyang Agricultural University, Shenyang, Liaoning Province, China
| | - X Ai
- College of Agronomy, Peanut Research Institute, Shenyang Agricultural University, Shenyang, Liaoning Province, China
| | - Q Dong
- College of Agronomy, Peanut Research Institute, Shenyang Agricultural University, Shenyang, Liaoning Province, China
| | - X Shi
- College of Agronomy, Peanut Research Institute, Shenyang Agricultural University, Shenyang, Liaoning Province, China
| | - X Zhao
- College of Agronomy, Peanut Research Institute, Shenyang Agricultural University, Shenyang, Liaoning Province, China
| | - C Zhong
- College of Agronomy, Peanut Research Institute, Shenyang Agricultural University, Shenyang, Liaoning Province, China
| | - H Yu
- College of Agronomy, Peanut Research Institute, Shenyang Agricultural University, Shenyang, Liaoning Province, China
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Wu T, Yin J, Wu X, Li W, Bie S, Zhao J, Song X, Yu H, Li Z. Discrimination and characterization of volatile organic compounds in Lonicerae Japonicae flos and Lonicerae flos using multivariate statistics combined with headspace gas chromatography-ion mobility spectrometry and headspace solid-phase microextraction gas chromatography-mass spectrometry techniques. Rapid Commun Mass Spectrom 2024; 38:e9693. [PMID: 38356085 DOI: 10.1002/rcm.9693] [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] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/20/2023] [Revised: 11/30/2023] [Accepted: 12/05/2023] [Indexed: 02/16/2024]
Abstract
RATIONALE The volatile organic compounds (VOCs) of Lonicerae Japonicae flos (LJF) and Lonicera flos (LF) play a pivotal role in determining their sensory characteristics, medicinal properties, and subsequent impact on market pricing and consumer preferences. However, the differences and specificity of these VOCs remain obscure. Hence, it is crucial to conduct a comprehensive characterization of the VOCs in LJF and LF and pinpoint their potential differential VOCs. METHODS In this study, headspace gas chromatography-ion mobility spectrometry (HS-GC/IMS) and headspace solid-phase microextraction gas chromatography-mass spectrometry (HS-SPME-GC/MS) were employed to comprehensively investigate the compositional characteristics and distinctions in VOCs between LJF and LF. Multivariate statistical analysis was used to identify candidate differential VOCs of LJF and LF samples. RESULTS A total of 54 and 88 VOCs were identified using HS-GC/IMS and HS-SPME-GC/MS analysis, respectively. Primary VOCs detected in LJF include leaf alcohol, (E)-2-hexen-1-ol dimer, 2-octyn-1-ol, and (E)-3-hexen-1-ol. Key VOCs prevalent in LF encompass farnesol, heptanoic acid, octanoic acid, and valeric acid. Multivariate statistical analysis indicates that compounds such as phenethyl alcohol and leaf alcohol were selected as potential VOCs for distinguishing between LJF and LF. CONCLUSION This research conducted a comprehensive analysis of the fundamental volatile components in both LJF and LF. It subsequently elucidated the distinctions and specificities within their respective VOC profiles. And this study enables differentiation between LJF and LF through the analysis of VOCs, offering valuable insights for enhancing the quality control of both LJF and LF.
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Affiliation(s)
- Tong Wu
- College of Pharmaceutical Engineering of Traditional Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin, China
- State Key Laboratory of Component-based Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin, China
- Haihe Laboratory of Modern Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin, China
| | - Jiaxin Yin
- College of Pharmaceutical Engineering of Traditional Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin, China
- State Key Laboratory of Component-based Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin, China
- Haihe Laboratory of Modern Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin, China
| | - Xinlong Wu
- College of Pharmaceutical Engineering of Traditional Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin, China
- State Key Laboratory of Component-based Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin, China
- Haihe Laboratory of Modern Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin, China
| | - Wei Li
- College of Pharmaceutical Engineering of Traditional Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin, China
- State Key Laboratory of Component-based Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin, China
- Haihe Laboratory of Modern Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin, China
| | - Songtao Bie
- College of Pharmaceutical Engineering of Traditional Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin, China
- State Key Laboratory of Component-based Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin, China
- Haihe Laboratory of Modern Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin, China
| | - Jing Zhao
- College of Pharmaceutical Engineering of Traditional Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin, China
- State Key Laboratory of Component-based Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin, China
- Haihe Laboratory of Modern Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin, China
| | - Xinbo Song
- College of Pharmaceutical Engineering of Traditional Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin, China
- State Key Laboratory of Component-based Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin, China
- Haihe Laboratory of Modern Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin, China
| | - Heshui Yu
- College of Pharmaceutical Engineering of Traditional Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin, China
- State Key Laboratory of Component-based Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin, China
- Haihe Laboratory of Modern Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin, China
| | - Zheng Li
- College of Pharmaceutical Engineering of Traditional Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin, China
- State Key Laboratory of Component-based Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin, China
- Haihe Laboratory of Modern Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin, China
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Yang MS, Fan XK, Su J, Wan XL, Yu H, Lu Y, Hua YJ, Jin JR, Pei P, Yu CQ, Sun DJY, Lyu J, Tao R, Zhou JY. [A prospective study on association between sleep duration and the risk of chronic obstructive pulmonary disease in adults in Suzhou]. Zhonghua Liu Xing Bing Xue Za Zhi 2024; 45:331-338. [PMID: 38514308 DOI: 10.3760/cma.j.cn112338-20230918-00164] [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: 03/23/2024]
Abstract
Objective: To investigate the prospective association of sleep duration with the development of chronic obstructive pulmonary disease (COPD) in adults in Suzhou. Methods: The study used the data of 53 269 participants aged 30-79 years recruited in the baseline survey from 2004 to 2008 and the follow-up until December 31, 2017 of China Kadoorie Biobank (CKB) conducted in Wuzhong District, Suzhou. After excluding participants with airflow limitation, self-reported chronic bronchitis/emphysema/coronary heart disease history at the baseline survey and abnormal or incomplete data, a total of 45 336 participants were included in the final analysis. The association between daily sleep duration and the risk for developing COPD was analyzed by using a Cox proportional hazard regression model, and the hazard ratio (HR) values and their 95%CI were calculated. The analysis was stratified by age, gender and lifestyle factors, and cross-analysis was conducted according to smoking status and daily sleep duration. Results: The median follow-up time was 11.12 years, with a total of 515 COPD diagnoses in the follow-up. After adjusting for potential confounders, multifactorial Cox proportional hazard regression analysis showed that daily sleep duration ≥10 hours was associated with higher risk for developing COPD (HR=1.42, 95%CI: 1.03-1.97). The cross analysis showed that excessive daily sleep duration increased the risk for COPD in smokers (HR=2.49, 95%CI: 1.35-4.59, interaction P<0.001). Conclusion: Longer daily sleep duration (≥10 hours) might increase the risk for COPD in adults in Suzhou, especially in smokers.
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Affiliation(s)
- M S Yang
- Department of Epidemiology and Biostatistics, School of Public Health, Southeast University, Nanjing 210009, China
| | - X K Fan
- Jiangsu Provincial Center for Disease Control and Prevention, Nanjing 210009, China
| | - J Su
- Jiangsu Provincial Center for Disease Control and Prevention, Nanjing 210009, China School of Public Health, Nanjing Medical University, Nanjing 211166, China
| | - X L Wan
- School of Public Health, Nanjing Medical University, Nanjing 211166, China
| | - H Yu
- Jiangsu Provincial Center for Disease Control and Prevention, Nanjing 210009, China
| | - Y Lu
- Suzhou Prefectural Center for Disease Control and Prevention, Suzhou 215003, China
| | - Y J Hua
- Suzhou Prefectural Center for Disease Control and Prevention, Suzhou 215003, China
| | - J R Jin
- Wuzhong District Center for Disease Control and Prevention of Suzhou, Suzhou 215128, China
| | - P Pei
- Peking University Center for Public Health and Epidemic Preparedness & Response, Beijing 100191, China
| | - C Q Yu
- Peking University Center for Public Health and Epidemic Preparedness & Response, Beijing 100191, China Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing 100191, China Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing 100191, China
| | - D J Y Sun
- Peking University Center for Public Health and Epidemic Preparedness & Response, Beijing 100191, China Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing 100191, China Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing 100191, China
| | - J Lyu
- Peking University Center for Public Health and Epidemic Preparedness & Response, Beijing 100191, China Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing 100191, China Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing 100191, China
| | - R Tao
- Jiangsu Provincial Center for Disease Control and Prevention, Nanjing 210009, China School of Public Health, Nanjing Medical University, Nanjing 211166, China
| | - J Y Zhou
- Jiangsu Provincial Center for Disease Control and Prevention, Nanjing 210009, China School of Public Health, Nanjing Medical University, Nanjing 211166, China
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Yin J, Wu T, Zhu B, Cui P, Zhang Y, Chen X, Ding H, Han L, Bie S, Li F, Song X, Yu H, Li Z. Comprehensive multicomponent characterization and quality assessment of Xiaoyao Wan by UPLC-Q-Orbitrap-MS, HS-SPME-GC-MS and HS-GC-IMS. J Pharm Biomed Anal 2024; 239:115910. [PMID: 38101240 DOI: 10.1016/j.jpba.2023.115910] [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: 09/26/2023] [Revised: 12/02/2023] [Accepted: 12/05/2023] [Indexed: 12/17/2023]
Abstract
Xiaoyao Wan (XYW) is a prescription medicine of traditional Chinese medicine (TCM) with the effects of "soothing the liver and relieving depression," and "strengthening spleen and nourishing blood". XYW has been widely concerned in the treatment of depression and has become one of the commonly used classic formulas in clinical practice. However, the pharmacodynamic substance basis and the quality control studies of XYW are hitherto quite limited. Here, we aim to fully utilize an advanced ultra - performance liquid chromatography-quadrupole - Orbitrap mass spectrometry (UPLC-Q-Orbitrap-MS), headspace-solid phase microextraction-gas chromatography-mass spectrometry (HS-SPME-GC-MS) and headspace-gas chromatography-ion mobility spectrometry (HS-GC-IMS) technique to deep characterization of the pharmacological substance basis and quantitatively evaluate the quality of XYW. Firstly, 299 compounds were identified or tentatively characterized, including 198 non-volatile organic compounds (n-VOCs) and 101 volatile organic compounds (VOCs). Secondly, principal component analysis (PCA) and hierarchical cluster analysis (HCA) was used to analyze quality differences in XYW at different manufacturers. Thirdly, a parallel reaction monitoring (PRM) method was established and validated to quantify the fourteen major effective substances in different manufacturers of XYW, which were chosen as the benchmarked substances to evaluate the quality of XYW. In conclusion, this study shows that the strategy provides a useful method for quality control of TCM and offers a practical workflow for exploring the quality consistency of TCM.
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Affiliation(s)
- Jiaxin Yin
- College of Pharmaceutical Engineering of Traditional Chinese Medicine, Tianjin University of Traditional Chinese Medicine, No.10 Poyanghu Road, West Tuanbo New Town Jinghai, Tianjin 301617, PR China; Haihe Laboratory of Modern Chinese Medicine, Tianjin 301617, PR China
| | - Tong Wu
- College of Pharmaceutical Engineering of Traditional Chinese Medicine, Tianjin University of Traditional Chinese Medicine, No.10 Poyanghu Road, West Tuanbo New Town Jinghai, Tianjin 301617, PR China; Haihe Laboratory of Modern Chinese Medicine, Tianjin 301617, PR China
| | - Beibei Zhu
- College of Pharmaceutical Engineering of Traditional Chinese Medicine, Tianjin University of Traditional Chinese Medicine, No.10 Poyanghu Road, West Tuanbo New Town Jinghai, Tianjin 301617, PR China; State Key Laboratory of Component-based Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin 301617, PR China; Haihe Laboratory of Modern Chinese Medicine, Tianjin 301617, PR China
| | - Pengdi Cui
- College of Pharmaceutical Engineering of Traditional Chinese Medicine, Tianjin University of Traditional Chinese Medicine, No.10 Poyanghu Road, West Tuanbo New Town Jinghai, Tianjin 301617, PR China; Haihe Laboratory of Modern Chinese Medicine, Tianjin 301617, PR China
| | - Yang Zhang
- College of Pharmaceutical Engineering of Traditional Chinese Medicine, Tianjin University of Traditional Chinese Medicine, No.10 Poyanghu Road, West Tuanbo New Town Jinghai, Tianjin 301617, PR China; Haihe Laboratory of Modern Chinese Medicine, Tianjin 301617, PR China
| | - Xue Chen
- College of Pharmaceutical Engineering of Traditional Chinese Medicine, Tianjin University of Traditional Chinese Medicine, No.10 Poyanghu Road, West Tuanbo New Town Jinghai, Tianjin 301617, PR China; Haihe Laboratory of Modern Chinese Medicine, Tianjin 301617, PR China
| | - Hui Ding
- College of Pharmaceutical Engineering of Traditional Chinese Medicine, Tianjin University of Traditional Chinese Medicine, No.10 Poyanghu Road, West Tuanbo New Town Jinghai, Tianjin 301617, PR China; Haihe Laboratory of Modern Chinese Medicine, Tianjin 301617, PR China
| | - Lifeng Han
- College of Pharmaceutical Engineering of Traditional Chinese Medicine, Tianjin University of Traditional Chinese Medicine, No.10 Poyanghu Road, West Tuanbo New Town Jinghai, Tianjin 301617, PR China; State Key Laboratory of Component-based Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin 301617, PR China
| | - Songtao Bie
- College of Pharmaceutical Engineering of Traditional Chinese Medicine, Tianjin University of Traditional Chinese Medicine, No.10 Poyanghu Road, West Tuanbo New Town Jinghai, Tianjin 301617, PR China; State Key Laboratory of Component-based Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin 301617, PR China; Haihe Laboratory of Modern Chinese Medicine, Tianjin 301617, PR China
| | - Fangyi Li
- College of Pharmaceutical Engineering of Traditional Chinese Medicine, Tianjin University of Traditional Chinese Medicine, No.10 Poyanghu Road, West Tuanbo New Town Jinghai, Tianjin 301617, PR China; State Key Laboratory of Component-based Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin 301617, PR China; Haihe Laboratory of Modern Chinese Medicine, Tianjin 301617, PR China
| | - Xinbo Song
- College of Pharmaceutical Engineering of Traditional Chinese Medicine, Tianjin University of Traditional Chinese Medicine, No.10 Poyanghu Road, West Tuanbo New Town Jinghai, Tianjin 301617, PR China; State Key Laboratory of Component-based Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin 301617, PR China; Haihe Laboratory of Modern Chinese Medicine, Tianjin 301617, PR China
| | - Heshui Yu
- College of Pharmaceutical Engineering of Traditional Chinese Medicine, Tianjin University of Traditional Chinese Medicine, No.10 Poyanghu Road, West Tuanbo New Town Jinghai, Tianjin 301617, PR China; State Key Laboratory of Component-based Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin 301617, PR China; Haihe Laboratory of Modern Chinese Medicine, Tianjin 301617, PR China.
| | - Zheng Li
- College of Pharmaceutical Engineering of Traditional Chinese Medicine, Tianjin University of Traditional Chinese Medicine, No.10 Poyanghu Road, West Tuanbo New Town Jinghai, Tianjin 301617, PR China; State Key Laboratory of Component-based Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin 301617, PR China; Haihe Laboratory of Modern Chinese Medicine, Tianjin 301617, PR China.
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Yu K, Liang P, Yu H, Liu H, Guo J, Yan X, Li Z, Li G, Wang Y, Wang C. Integrating Transcriptome and Chemical Analyses to Provide Insights into Biosynthesis of Terpenoids and Flavonoids in the Medicinal Industrial Crop Andrographis paniculate and Its Antiviral Medicinal Parts. Molecules 2024; 29:852. [PMID: 38398604 PMCID: PMC10893308 DOI: 10.3390/molecules29040852] [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: 01/14/2024] [Revised: 02/09/2024] [Accepted: 02/09/2024] [Indexed: 02/25/2024] Open
Abstract
Andrographis paniculata is a medicinal plant traditionally used to produce diterpene lactones and flavonoids, which possess various biological activities. Widely distributed in China, India, and other Southeast Asia countries, A. paniculata has become an important economic crop, significantly treating SARS-CoV-2, and is being cultivated on a large scale in southern China. The biosynthesis of active ingredients in A. paniculata are regulated and controlled by genes, but their specific roles are still not fully understood. To further explore the growth regulation factors and utilization of its medicinal parts of this industrial crop, chemical and transcriptome analyses were conducted on the roots, stems, and leaves of A. paniculata to identify the biosynthesis pathways and related candidate genes of the active ingredients. The chemical analysis revealed that the main components of A. paniculata were diterpene lactones and flavonoids, which displayed potential ability to treat SARS-CoV-2 through molecular docking. Moreover, the transcriptome sequencing annotated a total of 40,850 unigenes, including 7962 differentially expressed genes. Among these, 120 genes were involved in diterpene lactone biosynthesis and 60 genes were involved in flavonoid biosynthesis. The expression of diterpene lactone-related genes was the highest in leaves and the lowest in roots, consistent with our content determination results. It is speculated that these highly expressed genes in leaves may be involved in the biosynthesis pathway of diterpenes. Furthermore, two class Ⅰ terpene synthases in A. paniculata transcriptome were also annotated, providing reference for the downstream pathway of the diterpene lactone biosynthesis. With their excellent market value, our experiments will promote the study of the biosynthetic genes for active ingredients in A. paniculata and provide insights for subsequent in vitro biosynthesis.
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Affiliation(s)
- Kuo Yu
- School of Medicine, Foshan University, Foshan 528225, China; (K.Y.); (P.L.); (H.L.); (J.G.); (G.L.)
- College of Pharmaceutical Engineering of Traditional Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin 301617, China; (H.Y.); (X.Y.); (Z.L.)
| | - Pengjie Liang
- School of Medicine, Foshan University, Foshan 528225, China; (K.Y.); (P.L.); (H.L.); (J.G.); (G.L.)
| | - Heshui Yu
- College of Pharmaceutical Engineering of Traditional Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin 301617, China; (H.Y.); (X.Y.); (Z.L.)
| | - Hui Liu
- School of Medicine, Foshan University, Foshan 528225, China; (K.Y.); (P.L.); (H.L.); (J.G.); (G.L.)
| | - Jialiang Guo
- School of Medicine, Foshan University, Foshan 528225, China; (K.Y.); (P.L.); (H.L.); (J.G.); (G.L.)
| | - Xiaohui Yan
- College of Pharmaceutical Engineering of Traditional Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin 301617, China; (H.Y.); (X.Y.); (Z.L.)
| | - Zheng Li
- College of Pharmaceutical Engineering of Traditional Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin 301617, China; (H.Y.); (X.Y.); (Z.L.)
| | - Guoqiang Li
- School of Medicine, Foshan University, Foshan 528225, China; (K.Y.); (P.L.); (H.L.); (J.G.); (G.L.)
| | - Ying Wang
- Institute of Traditional Chinese Medicine & Natural Products, College of Pharmacy, Jinan University, Guangzhou 510632, China
| | - Chunhua Wang
- School of Medicine, Foshan University, Foshan 528225, China; (K.Y.); (P.L.); (H.L.); (J.G.); (G.L.)
- College of Pharmaceutical Engineering of Traditional Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin 301617, China; (H.Y.); (X.Y.); (Z.L.)
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Hu M, Shen Y, Yu H, Song Y, Zheng T, Hong D, Gong L. Prognostic value of cardiac magnetic resonance imaging feature tracking technology in patients with light chain amyloidosis. Clin Radiol 2024; 79:e239-e246. [PMID: 37953095 DOI: 10.1016/j.crad.2023.10.016] [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: 09/26/2022] [Revised: 06/27/2023] [Accepted: 10/12/2023] [Indexed: 11/14/2023]
Abstract
AIM To undertake a meta-analysis of the prognostic value of cardiac magnetic resonance imaging feature tracking (CMR-FT) in patients with light-chain cardiac amyloidosis (LCA). MATERIALS AND METHODS A systematic search was conducted in PubMed, EMBASE, Web of Science, and the Cochrane Library. All analyses were conducted using RevMan 5.3 software. RESULTS Eight studies were included with 663 patients. For the left ventricle, the results showed that CMR-FT was statistically significant in predicting death, with less impaired global circumferential (GCS), radial (GRS) and longitudinal (GLS) strain in survivors of LCA (odds ratio [OR] 1.17, 95% confidence interval [CI] 1.09-1.25; 0.95, 0.93-0.96; 1.12, 1.05-1.20, all p<0.001). For ejection fraction (EF) and mass index, surviving patients had higher EFs and mass index (OR 0.96, 95% CI 0.96-0.97; 1.01, 1.01-1.02). For the right ventricle, the results showed that CMR-FT was statistically significant in predicting death, with less impaired GLS and GRS in survivors of LCA (OR 1.11, 95% CI 1.08-1.15; 0.93, 0.90-0.96, all p<0.001). Surviving patients had higher EFs (OR 0.97, 95% CI 0.96-0.98, p<0.001). Upon removing the studies one by one, there was no significant change in the results of the study. Both analyses showed no apparent publication deviation on funnel plots. CONCLUSION Parameters derived from CMR-FT technology are promising new predictors for LCA, and are easily available and reliable. Patients with poor myocardial deformability are at highest risk of death.
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Affiliation(s)
- M Hu
- Medical Imaging Center, The Second Affiliated Hospital of Nanchang University, No. 1 Min-de Road, Donghu District, Nanchang, 33000, Jiangxi Province, People's Republic of China
| | - Y Shen
- Department of Neurology, The First Affiliated Hospital of Nanchang University, No. 17, Yongwai Zheng Street, Donghu District, Nanchang 330006, Jiangxi Province, People's Republic of China
| | - H Yu
- Department of Radiology, Jiangxi Province Medical Imaging Research Institute, The First Affiliated Hospital of Nanchang University, No. 17, Yongwai Zheng Street, Donghu District, Nanchang 330006, Jiangxi Province, People's Republic of China
| | - Y Song
- Medical Imaging Center, The Second Affiliated Hospital of Nanchang University, No. 1 Min-de Road, Donghu District, Nanchang, 33000, Jiangxi Province, People's Republic of China
| | - T Zheng
- Medical Imaging Center, The Second Affiliated Hospital of Nanchang University, No. 1 Min-de Road, Donghu District, Nanchang, 33000, Jiangxi Province, People's Republic of China
| | - D Hong
- Department of Neurology, The First Affiliated Hospital of Nanchang University, No. 17, Yongwai Zheng Street, Donghu District, Nanchang 330006, Jiangxi Province, People's Republic of China.
| | - L Gong
- Medical Imaging Center, The Second Affiliated Hospital of Nanchang University, No. 1 Min-de Road, Donghu District, Nanchang, 33000, Jiangxi Province, People's Republic of China.
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Yue Y, Yin J, Xie J, Wu S, Ding H, Han L, Bie S, Song W, Zhang Y, Song X, Yu H, Li Z. Comparative Analysis of Volatile Compounds in the Flower Buds of Three Panax Species Using Fast Gas Chromatography Electronic Nose, Headspace-Gas Chromatography-Ion Mobility Spectrometry, and Headspace Solid Phase Microextraction-Gas Chromatography-Mass Spectrometry Coupled with Multivariate Statistical Analysis. Molecules 2024; 29:602. [PMID: 38338347 PMCID: PMC10856343 DOI: 10.3390/molecules29030602] [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: 11/27/2023] [Revised: 12/09/2023] [Accepted: 01/11/2024] [Indexed: 02/12/2024] Open
Abstract
The flower buds of three Panax species (PGF: P. ginseng; PQF: P. quinquefolius; PNF: P. notoginseng) widely consumed as health tea are easily confused in market circulation. We aimed to develop a green, fast, and easy analysis strategy to distinguish PGF, PQF, and PNF. In this work, fast gas chromatography electronic nose (fast GC e-nose), headspace-gas chromatography-ion mobility spectrometry (HS-GC-IMS), and headspace solid phase microextraction-gas chromatography-mass spectrometry (HS-SPME-GC-MS) were utilized to comprehensively analyze the volatile organic components (VOCs) of three flowers. Meanwhile, a principal component analysis (PCA) and heatmap were applied to distinguish the VOCs identified in PGF, PQF, and PNF. A random forest (RF) analysis was used to screen key factors affecting the discrimination. As a result, 39, 68, and 78 VOCs were identified in three flowers using fast GC e-nose, HS-GC-IMS, and HS-SPME-GC-MS. Nine VOCs were selected as potential chemical markers based on a model of RF for distinguishing these three species. Conclusively, a complete VOC analysis strategy was created to provide a methodological reference for the rapid, simple, and environmentally friendly detection and identification of food products (tea, oil, honey, etc.) and herbs with flavor characteristics and to provide a basis for further specification of their quality and base sources.
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Affiliation(s)
- Yang Yue
- College of Pharmaceutical Engineering of Traditional Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin 301617, China; (Y.Y.); (J.Y.); (J.X.); (S.W.); (H.D.); (L.H.); (S.B.); (X.S.)
- Haihe Laboratory of Modern Chinese Medicine, Tianjin 301617, China
| | - Jiaxin Yin
- College of Pharmaceutical Engineering of Traditional Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin 301617, China; (Y.Y.); (J.Y.); (J.X.); (S.W.); (H.D.); (L.H.); (S.B.); (X.S.)
- Haihe Laboratory of Modern Chinese Medicine, Tianjin 301617, China
| | - Jingyi Xie
- College of Pharmaceutical Engineering of Traditional Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin 301617, China; (Y.Y.); (J.Y.); (J.X.); (S.W.); (H.D.); (L.H.); (S.B.); (X.S.)
- Haihe Laboratory of Modern Chinese Medicine, Tianjin 301617, China
| | - Shufang Wu
- College of Pharmaceutical Engineering of Traditional Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin 301617, China; (Y.Y.); (J.Y.); (J.X.); (S.W.); (H.D.); (L.H.); (S.B.); (X.S.)
- Haihe Laboratory of Modern Chinese Medicine, Tianjin 301617, China
| | - Hui Ding
- College of Pharmaceutical Engineering of Traditional Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin 301617, China; (Y.Y.); (J.Y.); (J.X.); (S.W.); (H.D.); (L.H.); (S.B.); (X.S.)
- Haihe Laboratory of Modern Chinese Medicine, Tianjin 301617, China
| | - Lifeng Han
- College of Pharmaceutical Engineering of Traditional Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin 301617, China; (Y.Y.); (J.Y.); (J.X.); (S.W.); (H.D.); (L.H.); (S.B.); (X.S.)
- State Key Laboratory of Component-Based Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin 301617, China
| | - Songtao Bie
- College of Pharmaceutical Engineering of Traditional Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin 301617, China; (Y.Y.); (J.Y.); (J.X.); (S.W.); (H.D.); (L.H.); (S.B.); (X.S.)
- Haihe Laboratory of Modern Chinese Medicine, Tianjin 301617, China
- State Key Laboratory of Component-Based Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin 301617, China
| | - Wen Song
- Tianjin HongRenTang Pharmaceutical Co., Ltd., Tianjin 300385, China; (W.S.); (Y.Z.)
| | - Ying Zhang
- Tianjin HongRenTang Pharmaceutical Co., Ltd., Tianjin 300385, China; (W.S.); (Y.Z.)
| | - Xinbo Song
- College of Pharmaceutical Engineering of Traditional Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin 301617, China; (Y.Y.); (J.Y.); (J.X.); (S.W.); (H.D.); (L.H.); (S.B.); (X.S.)
- Haihe Laboratory of Modern Chinese Medicine, Tianjin 301617, China
- State Key Laboratory of Component-Based Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin 301617, China
| | - Heshui Yu
- College of Pharmaceutical Engineering of Traditional Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin 301617, China; (Y.Y.); (J.Y.); (J.X.); (S.W.); (H.D.); (L.H.); (S.B.); (X.S.)
- Haihe Laboratory of Modern Chinese Medicine, Tianjin 301617, China
- State Key Laboratory of Component-Based Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin 301617, China
| | - Zheng Li
- College of Pharmaceutical Engineering of Traditional Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin 301617, China; (Y.Y.); (J.Y.); (J.X.); (S.W.); (H.D.); (L.H.); (S.B.); (X.S.)
- Haihe Laboratory of Modern Chinese Medicine, Tianjin 301617, China
- State Key Laboratory of Component-Based Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin 301617, China
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Bie S, Mo Q, Shi C, Yuan H, Li C, Wu T, Li W, Yu H. Interactions of plumbagin with five common antibiotics against Staphylococcus aureus in vitro. PLoS One 2024; 19:e0297493. [PMID: 38277418 PMCID: PMC10817181 DOI: 10.1371/journal.pone.0297493] [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: 10/10/2023] [Accepted: 01/06/2024] [Indexed: 01/28/2024] Open
Abstract
Staphylococcus aureus is the main culprit, causing a variety of severe clinical infections. At the same time, clinics are also facing the severe situation of antibiotic resistance. Therefore, effective strategies to address this problem may include expanding the antimicrobial spectrum by exploring alternative sources of drugs or delaying the development of antibiotic resistance through combination therapy so that existing antibiotics can continue to be used. Plumbagin (PLU) is a phytochemical that exhibits antibacterial activity. In the present study, we investigated the in vitro antibacterial activity of PLU. We selected five antibiotics with different mechanisms and inhibitory activities against S. aureus to explore their interaction with the combination of PLU. The interaction of combinations was evaluated by the Bliss independent model and visualized through response surface analysis. PLU exhibited potent antibacterial activity, with half maximal inhibitory concentration (IC50) and minimum inhibitory concentration (MIC) values against S. aureus of 1.73 μg/mL and 4 μg/mL, respectively. Synergism was observed when PLU was combined with nitrofurantoin (NIT), ciprofloxacin (CPR), mecillinam (MEC), and chloramphenicol (CHL). The indifference of the trimethoprim (TMP)-PLU pairing was demonstrated across the entire dose-response matrix, but significant synergy was observed within a specific dose region. In addition, no antagonistic interactions were indicated. Overall, PLU is not only a promising antimicrobial agent but also has the potential to enhance the growth-inhibitory activity of some antibiotics against S. aureus, and the use of the interaction landscape, along with the dose-response matrix, for analyzing and quantifying combination results represents an improved approach to comprehending antibacterial combinations.
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Affiliation(s)
- Songtao Bie
- College of Pharmaceutical Engineering of Traditional Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin, China
- State Key Laboratory of Component-based Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin, China
- Haihe Laboratory of Modern Chinese Medicine, Tianjin, China
| | - Qiuyue Mo
- College of Pharmaceutical Engineering of Traditional Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin, China
- Haihe Laboratory of Modern Chinese Medicine, Tianjin, China
| | - Chen Shi
- College of Pharmaceutical Engineering of Traditional Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin, China
- Haihe Laboratory of Modern Chinese Medicine, Tianjin, China
| | - Hui Yuan
- College of Pharmaceutical Engineering of Traditional Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin, China
- Haihe Laboratory of Modern Chinese Medicine, Tianjin, China
| | - Chunshuang Li
- College of Pharmaceutical Engineering of Traditional Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin, China
- Haihe Laboratory of Modern Chinese Medicine, Tianjin, China
| | - Tong Wu
- College of Pharmaceutical Engineering of Traditional Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin, China
- Haihe Laboratory of Modern Chinese Medicine, Tianjin, China
| | - Wenlong Li
- College of Pharmaceutical Engineering of Traditional Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin, China
- State Key Laboratory of Component-based Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin, China
- Haihe Laboratory of Modern Chinese Medicine, Tianjin, China
| | - Heshui Yu
- College of Pharmaceutical Engineering of Traditional Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin, China
- State Key Laboratory of Component-based Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin, China
- Haihe Laboratory of Modern Chinese Medicine, Tianjin, China
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Jia LH, Lin HL, Zheng SW, Lin XJ, Zhang D, Yu H. [Mitigating metal artifacts from cobalt-chromium alloy crowns in cone-beam CT images through deep learning techniques]. Zhonghua Kou Qiang Yi Xue Za Zhi 2024; 59:71-79. [PMID: 38228542 DOI: 10.3760/cma.j.cn112144-20231030-00228] [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: 01/18/2024]
Abstract
Objective: To develop and evaluate metal artifact removal systems (MARS) based on deep learning to assess their effectiveness in removing artifacts caused by different thicknesses of metals in cone-beam CT (CBCT) images. Methods: A full-mouth standard model (60 mm×75 mm×110 mm) was three-dimensional (3D) printed using photosensitive resin. The model included a removable and replaceable target tooth position where cobalt-chromium alloy crowns with varying thicknesses were inserted to generate matched CBCT images. The artifacts resulting from cobalt-chromium alloys with different thicknesses were evaluated using the structural similarity index measure (SSIM) and peak signal-to-noise ratio (PSNR). CNN-MARS and U-net-MARS were developed using a convolutional neural network and U-net architecture, respectively. The effectiveness of both MARSs were assessed through visualization and by measuring SSIM and PSNR values. The SSIM and PSNR values were statistically analyzed using one-way analysis of variance (α=0.05). Results: Significant differences were observed in the range of artifacts produced by different thicknesses of cobalt-chromium alloys (all P<0.05), with 1 mm resulting in the least artifacts. The SSIM values for specimens with thicknesses of 1.0, 1.5, and 2.0 mm were 0.916±0.019, 0.873±0.010, and 0.833±0.010, respectively (F=447.89, P<0.001). The corresponding PSNR values were 20.834±1.176, 17.002±0.427, and 14.673±0.429, respectively (F=796.51, P<0.001). After applying CNN-MARS and U-net-MARS to artifact removal, the SSIM and PSNR values significantly increased for images with the same thickness of metal (both P<0.05). When using the CNN-MARS for artifact removal, the SSIM values for 1.0, 1.5 and 2.0 mm were 0.938±0.023, 0.930±0.029, and 0.928±0.020 (F=2.22, P=0.112), while the PSNR values were 30.938±1.495, 30.578±2.154 and 30.553±2.355 (F=0.54, P=0.585). When using the U-net-MARS for artifact removal, the SSIM values for 1.0, 1.5 and 2.0 mm were 0.930±0.024, 0.932±0.017 and 0.930±0.012 (F=0.24, P=0.788), and the PSNR values were 30.291±0.934, 30.351±1.002 and 30.271±1.143 (F=0.07, P=0.929). No significant differences were found in SSIM and PSNR values after artifact removal using CNN-MARS and U-net-MARS for different thicknesses of cobalt-chromium alloys (all P>0.05). Visualization demonstrated a high degree of similarity between the images before and after artifact removal using both MARS. However, CNN-MARS displayed clearer metal edges and preserved more tissue details when compared with U-net-MARS. Conclusions: Both the CNN-MARS and U-net-MARS models developed in this study effectively remove the metal artifacts and enhance the image quality. CNN-MARS exhibited an advantage in restoring tissue structure information around the artifacts compared to U-net-MARS.
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Affiliation(s)
- L H Jia
- Department of Prosthodontics, School and Hospital of Stomatology, Fujian Medical University & Fujian Key Laboratory of Oral Diseases & Fujian Provincial Engineering Research Center of Oral Biomaterial & Stomatological Key Laboratory of Fujian College and University & Institute of Stomatology, Fujian Medical University & Research Center of Dental Esthetics and Biomechanics, Fujian Medical University, Fuzhou 350002, China
| | - H L Lin
- Department of Prosthodontics, School and Hospital of Stomatology, Fujian Medical University & Fujian Key Laboratory of Oral Diseases & Fujian Provincial Engineering Research Center of Oral Biomaterial & Stomatological Key Laboratory of Fujian College and University & Institute of Stomatology, Fujian Medical University & Research Center of Dental Esthetics and Biomechanics, Fujian Medical University, Fuzhou 350002, China
| | - S W Zheng
- College of Computer and Data Science, Fuzhou University, Fuzhou 350108, China
| | - X J Lin
- Department of Prosthodontics, School and Hospital of Stomatology, Fujian Medical University & Fujian Key Laboratory of Oral Diseases & Fujian Provincial Engineering Research Center of Oral Biomaterial & Stomatological Key Laboratory of Fujian College and University & Institute of Stomatology, Fujian Medical University & Research Center of Dental Esthetics and Biomechanics, Fujian Medical University, Fuzhou 350002, China
| | - D Zhang
- College of Computer and Data Science, Fuzhou University, Fuzhou 350108, China
| | - H Yu
- Department of Prosthodontics, School and Hospital of Stomatology, Fujian Medical University & Fujian Key Laboratory of Oral Diseases & Fujian Provincial Engineering Research Center of Oral Biomaterial & Stomatological Key Laboratory of Fujian College and University & Institute of Stomatology, Fujian Medical University & Research Center of Dental Esthetics and Biomechanics, Fujian Medical University, Fuzhou 350002, China
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Zheng C, Zeng R, Wu G, Hu Y, Yu H. Beyond Vision: A View from Eye to Alzheimer's Disease and Dementia. J Prev Alzheimers Dis 2024; 11:469-483. [PMID: 38374754 DOI: 10.14283/jpad.2023.118] [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] [Indexed: 02/21/2024]
Abstract
With the aging of the global population, the health care burden of Alzheimer's disease (AD) and dementia is considered to increase dramatically in the coming decades. Given the insufficiency of effective interventions for AD and dementia, clinical research on identifying potentially modifiable risk factors and early diagnostic biomarkers becomes a public health priority. Currently, extracerebral manifestations with a large proportion of ocular involvement are usually recognized to precede the symptoms of AD and dementia. Growing epidemiologic evidence also suggests that eye disorders, such as cataracts, age-related macular degeneration, glaucoma, diabetic retinopathy, and so on, are closely associated with and even have a higher incidence of AD and dementia. The eye, as an extension of the central nervous system, therefore has the potential to provide a feasible approach to detecting structural and functional abnormalities of the brain. Numerous new imaging modalities are developed and give novel insights into the detection of several neurodegenerative, vascular, neuropathological, and other ocular abnormalities of AD and dementia in scientific research and clinical application. This review provides an overview of the epidemiologic associations between eye disorders and AD or dementia and summarizes the recent advances in ocular examinations and techniques employed for the detection of AD and dementia. With more brain-and-eye interconnections being identified, the eye is becoming a noninvasive and easily accessible window for the early diagnosis and prevention of AD and dementia.
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Affiliation(s)
- C Zheng
- Prof. Honghua Yu, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou 510080, China. Tel: 86-186-8888-8422.Fax: 86-8382-7812, E-mail: ; Prof. Yijun Hu, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou 510080, China. Tel: 86-137-1052-6990. Fax: 86-8382-7812; E-mail:
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Staplin N, Haynes R, Judge PK, Wanner C, Green JB, Emberson J, Preiss D, Mayne KJ, Ng SYA, Sammons E, Zhu D, Hill M, Stevens W, Wallendszus K, Brenner S, Cheung AK, Liu ZH, Li J, Hooi LS, Liu WJ, Kadowaki T, Nangaku M, Levin A, Cherney D, Maggioni AP, Pontremoli R, Deo R, Goto S, Rossello X, Tuttle KR, Steubl D, Petrini M, Seidi S, Landray MJ, Baigent C, Herrington WG, Abat S, Abd Rahman R, Abdul Cader R, Abdul Hafidz MI, Abdul Wahab MZ, Abdullah NK, Abdul-Samad T, Abe M, Abraham N, Acheampong S, Achiri P, Acosta JA, Adeleke A, Adell V, Adewuyi-Dalton R, Adnan N, Africano A, Agharazii M, Aguilar F, Aguilera A, Ahmad M, Ahmad MK, Ahmad NA, Ahmad NH, Ahmad NI, Ahmad Miswan N, Ahmad Rosdi H, Ahmed I, Ahmed S, Ahmed S, Aiello J, Aitken A, AitSadi R, Aker S, Akimoto S, Akinfolarin A, Akram S, Alberici F, Albert C, Aldrich L, Alegata M, Alexander L, Alfaress S, Alhadj Ali M, Ali A, Ali A, Alicic R, Aliu A, Almaraz R, Almasarwah R, Almeida J, Aloisi A, Al-Rabadi L, Alscher D, Alvarez P, Al-Zeer B, Amat M, Ambrose C, Ammar H, An Y, Andriaccio L, Ansu K, Apostolidi A, Arai N, Araki H, Araki S, Arbi A, Arechiga O, Armstrong S, Arnold T, Aronoff S, Arriaga W, Arroyo J, Arteaga D, Asahara S, Asai A, Asai N, Asano S, Asawa M, Asmee MF, Aucella F, Augustin M, Avery A, Awad A, Awang IY, Awazawa M, Axler A, Ayub W, Azhari Z, Baccaro R, Badin C, Bagwell B, Bahlmann-Kroll E, Bahtar AZ, Baigent C, Bains D, Bajaj H, Baker R, Baldini E, Banas B, Banerjee D, Banno S, Bansal S, Barberi S, Barnes S, Barnini C, Barot C, Barrett K, Barrios R, Bartolomei Mecatti B, Barton I, Barton J, Basily W, Bavanandan S, Baxter A, Becker L, Beddhu S, Beige J, Beigh S, Bell S, Benck U, Beneat A, Bennett A, Bennett D, Benyon S, Berdeprado J, Bergler T, Bergner A, Berry M, Bevilacqua M, Bhairoo J, Bhandari S, Bhandary N, Bhatt A, Bhattarai M, Bhavsar M, Bian W, Bianchini F, Bianco S, Bilous R, Bilton J, Bilucaglia D, Bird C, Birudaraju D, Biscoveanu M, Blake C, Bleakley N, Bocchicchia K, Bodine S, 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Effects of empagliflozin on progression of chronic kidney disease: a prespecified secondary analysis from the empa-kidney trial. Lancet Diabetes Endocrinol 2024; 12:39-50. [PMID: 38061371 PMCID: PMC7615591 DOI: 10.1016/s2213-8587(23)00321-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/05/2023] [Revised: 10/24/2023] [Accepted: 10/25/2023] [Indexed: 12/23/2023]
Abstract
BACKGROUND Sodium-glucose co-transporter-2 (SGLT2) inhibitors reduce progression of chronic kidney disease and the risk of cardiovascular morbidity and mortality in a wide range of patients. However, their effects on kidney disease progression in some patients with chronic kidney disease are unclear because few clinical kidney outcomes occurred among such patients in the completed trials. In particular, some guidelines stratify their level of recommendation about who should be treated with SGLT2 inhibitors based on diabetes status and albuminuria. We aimed to assess the effects of empagliflozin on progression of chronic kidney disease both overall and among specific types of participants in the EMPA-KIDNEY trial. METHODS EMPA-KIDNEY, a randomised, controlled, phase 3 trial, was conducted at 241 centres in eight countries (Canada, China, Germany, Italy, Japan, Malaysia, the UK, and the USA), and included individuals aged 18 years or older with an estimated glomerular filtration rate (eGFR) of 20 to less than 45 mL/min per 1·73 m2, or with an eGFR of 45 to less than 90 mL/min per 1·73 m2 with a urinary albumin-to-creatinine ratio (uACR) of 200 mg/g or higher. We explored the effects of 10 mg oral empagliflozin once daily versus placebo on the annualised rate of change in estimated glomerular filtration rate (eGFR slope), a tertiary outcome. We studied the acute slope (from randomisation to 2 months) and chronic slope (from 2 months onwards) separately, using shared parameter models to estimate the latter. Analyses were done in all randomly assigned participants by intention to treat. EMPA-KIDNEY is registered at ClinicalTrials.gov, NCT03594110. FINDINGS Between May 15, 2019, and April 16, 2021, 6609 participants were randomly assigned and then followed up for a median of 2·0 years (IQR 1·5-2·4). Prespecified subgroups of eGFR included 2282 (34·5%) participants with an eGFR of less than 30 mL/min per 1·73 m2, 2928 (44·3%) with an eGFR of 30 to less than 45 mL/min per 1·73 m2, and 1399 (21·2%) with an eGFR 45 mL/min per 1·73 m2 or higher. Prespecified subgroups of uACR included 1328 (20·1%) with a uACR of less than 30 mg/g, 1864 (28·2%) with a uACR of 30 to 300 mg/g, and 3417 (51·7%) with a uACR of more than 300 mg/g. Overall, allocation to empagliflozin caused an acute 2·12 mL/min per 1·73 m2 (95% CI 1·83-2·41) reduction in eGFR, equivalent to a 6% (5-6) dip in the first 2 months. After this, it halved the chronic slope from -2·75 to -1·37 mL/min per 1·73 m2 per year (relative difference 50%, 95% CI 42-58). The absolute and relative benefits of empagliflozin on the magnitude of the chronic slope varied significantly depending on diabetes status and baseline levels of eGFR and uACR. In particular, the absolute difference in chronic slopes was lower in patients with lower baseline uACR, but because this group progressed more slowly than those with higher uACR, this translated to a larger relative difference in chronic slopes in this group (86% [36-136] reduction in the chronic slope among those with baseline uACR <30 mg/g compared with a 29% [19-38] reduction for those with baseline uACR ≥2000 mg/g; ptrend<0·0001). INTERPRETATION Empagliflozin slowed the rate of progression of chronic kidney disease among all types of participant in the EMPA-KIDNEY trial, including those with little albuminuria. Albuminuria alone should not be used to determine whether to treat with an SGLT2 inhibitor. FUNDING Boehringer Ingelheim and Eli Lilly.
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T, Tamori Y, Tamura R, Tamura Y, Tan CHH, Tan EZZ, Tanabe A, Tanabe K, Tanaka A, Tanaka A, Tanaka N, Tang S, Tang Z, Tanigaki K, Tarlac M, Tatsuzawa A, Tay JF, Tay LL, Taylor J, Taylor K, Taylor K, Te A, Tenbusch L, Teng KS, Terakawa A, Terry J, Tham ZD, Tholl S, Thomas G, Thong KM, Tietjen D, Timadjer A, Tindall H, Tipper S, Tobin K, Toda N, Tokuyama A, Tolibas M, Tomita A, Tomita T, Tomlinson J, Tonks L, Topf J, Topping S, Torp A, Torres A, Totaro F, Toth P, Toyonaga Y, Tripodi F, Trivedi K, Tropman E, Tschope D, Tse J, Tsuji K, Tsunekawa S, Tsunoda R, Tucky B, Tufail S, Tuffaha A, Turan E, Turner H, Turner J, Turner M, Tuttle KR, Tye YL, Tyler A, Tyler J, Uchi H, Uchida H, Uchida T, Uchida T, Udagawa T, Ueda S, Ueda Y, Ueki K, Ugni S, Ugwu E, Umeno R, Unekawa C, Uozumi K, Urquia K, Valleteau A, Valletta C, van Erp R, Vanhoy C, Varad V, Varma R, Varughese A, Vasquez P, Vasseur A, Veelken R, Velagapudi C, Verdel K, Vettoretti S, Vezzoli G, Vielhauer V, Viera R, Vilar E, Villaruel S, Vinall L, Vinathan J, Visnjic M, Voigt E, von-Eynatten M, Vourvou M, Wada J, Wada J, Wada T, Wada Y, Wakayama K, Wakita Y, Wallendszus K, Walters T, Wan Mohamad WH, Wang L, Wang W, Wang X, Wang X, Wang Y, Wanner C, Wanninayake S, Watada H, Watanabe K, Watanabe K, Watanabe M, Waterfall H, Watkins D, Watson S, Weaving L, Weber B, Webley Y, Webster A, Webster M, Weetman M, Wei W, Weihprecht H, Weiland L, Weinmann-Menke J, Weinreich T, Wendt R, Weng Y, Whalen M, Whalley G, Wheatley R, Wheeler A, Wheeler J, Whelton P, White K, Whitmore B, Whittaker S, Wiebel J, Wiley J, Wilkinson L, Willett M, Williams A, Williams E, Williams K, Williams T, Wilson A, Wilson P, Wincott L, Wines E, Winkelmann B, Winkler M, Winter-Goodwin B, Witczak J, Wittes J, Wittmann M, Wolf G, Wolf L, Wolfling R, Wong C, Wong E, Wong HS, Wong LW, Wong YH, Wonnacott A, Wood A, Wood L, Woodhouse H, Wooding N, Woodman A, Wren K, Wu J, Wu P, Xia S, Xiao H, Xiao X, Xie Y, Xu C, Xu Y, Xue H, Yahaya H, Yalamanchili H, Yamada A, Yamada N, Yamagata K, Yamaguchi M, Yamaji Y, Yamamoto A, Yamamoto S, Yamamoto S, Yamamoto T, Yamanaka A, Yamano T, Yamanouchi Y, Yamasaki N, Yamasaki Y, Yamasaki Y, Yamashita C, Yamauchi T, Yan Q, Yanagisawa E, Yang F, Yang L, Yano S, Yao S, Yao Y, Yarlagadda S, Yasuda Y, Yiu V, Yokoyama T, Yoshida S, Yoshidome E, Yoshikawa H, Young A, Young T, Yousif V, Yu H, Yu Y, Yuasa K, Yusof N, Zalunardo N, Zander B, Zani R, Zappulo F, Zayed M, Zemann B, Zettergren P, Zhang H, Zhang L, Zhang L, Zhang N, Zhang X, Zhao J, Zhao L, Zhao S, Zhao Z, Zhong H, Zhou N, Zhou S, Zhu D, Zhu L, Zhu S, Zietz M, Zippo M, Zirino F, Zulkipli FH. Impact of primary kidney disease on the effects of empagliflozin in patients with chronic kidney disease: secondary analyses of the EMPA-KIDNEY trial. Lancet Diabetes Endocrinol 2024; 12:51-60. [PMID: 38061372 DOI: 10.1016/s2213-8587(23)00322-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/05/2023] [Revised: 10/24/2023] [Accepted: 10/25/2023] [Indexed: 12/23/2023]
Abstract
BACKGROUND The EMPA-KIDNEY trial showed that empagliflozin reduced the risk of the primary composite outcome of kidney disease progression or cardiovascular death in patients with chronic kidney disease mainly through slowing progression. We aimed to assess how effects of empagliflozin might differ by primary kidney disease across its broad population. METHODS EMPA-KIDNEY, a randomised, controlled, phase 3 trial, was conducted at 241 centres in eight countries (Canada, China, Germany, Italy, Japan, Malaysia, the UK, and the USA). Patients were eligible if their estimated glomerular filtration rate (eGFR) was 20 to less than 45 mL/min per 1·73 m2, or 45 to less than 90 mL/min per 1·73 m2 with a urinary albumin-to-creatinine ratio (uACR) of 200 mg/g or higher at screening. They were randomly assigned (1:1) to 10 mg oral empagliflozin once daily or matching placebo. Effects on kidney disease progression (defined as a sustained ≥40% eGFR decline from randomisation, end-stage kidney disease, a sustained eGFR below 10 mL/min per 1·73 m2, or death from kidney failure) were assessed using prespecified Cox models, and eGFR slope analyses used shared parameter models. Subgroup comparisons were performed by including relevant interaction terms in models. EMPA-KIDNEY is registered with ClinicalTrials.gov, NCT03594110. FINDINGS Between May 15, 2019, and April 16, 2021, 6609 participants were randomly assigned and followed up for a median of 2·0 years (IQR 1·5-2·4). Prespecified subgroupings by primary kidney disease included 2057 (31·1%) participants with diabetic kidney disease, 1669 (25·3%) with glomerular disease, 1445 (21·9%) with hypertensive or renovascular disease, and 1438 (21·8%) with other or unknown causes. Kidney disease progression occurred in 384 (11·6%) of 3304 patients in the empagliflozin group and 504 (15·2%) of 3305 patients in the placebo group (hazard ratio 0·71 [95% CI 0·62-0·81]), with no evidence that the relative effect size varied significantly by primary kidney disease (pheterogeneity=0·62). The between-group difference in chronic eGFR slopes (ie, from 2 months to final follow-up) was 1·37 mL/min per 1·73 m2 per year (95% CI 1·16-1·59), representing a 50% (42-58) reduction in the rate of chronic eGFR decline. This relative effect of empagliflozin on chronic eGFR slope was similar in analyses by different primary kidney diseases, including in explorations by type of glomerular disease and diabetes (p values for heterogeneity all >0·1). INTERPRETATION In a broad range of patients with chronic kidney disease at risk of progression, including a wide range of non-diabetic causes of chronic kidney disease, empagliflozin reduced risk of kidney disease progression. Relative effect sizes were broadly similar irrespective of the cause of primary kidney disease, suggesting that SGLT2 inhibitors should be part of a standard of care to minimise risk of kidney failure in chronic kidney disease. FUNDING Boehringer Ingelheim, Eli Lilly, and UK Medical Research Council.
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Jia LH, Lin HL, Zheng SW, Lin XJ, Zhang D, Yu H. [Mitigating metal artifacts in cone-beam CT images through deep learning techniques]. Zhonghua Kou Qiang Yi Xue Za Zhi 2023; 59:71-79. [PMID: 38172064 DOI: 10.3760/cma.j.cn112144-20231030-00233] [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: 01/05/2024]
Abstract
Objective: To develop and evaluate metal artifact removal systems (MARSs) based on deep learning to assess their effectiveness in removing artifacts caused by different thicknesses of metals in cone-beam CT (CBCT) images. Methods: A full-mouth standard model (60 mm×75 mm×110 mm) was three-dimensional (3D) printed using photosensitive resin. The model included a removable and replaceable target tooth position where cobalt-chromium alloy crowns with varying thicknesses were inserted to generate matched CBCT images. The artifacts resulting from cobalt-chromium alloys with different thicknesses were evaluated using the structural similarity index measure (SSIM) and peak signal-to-noise ratio (PSNR). CNN-MARS and U-net-MARS were developed using a convolutional neural network and U-net architecture, respectively. The effectiveness of both MARSs were assessed through visualization and by measuring SSIM and PSNR values. The SSIM and PSNR values were statistically analyzed using one-way analysis of variance (α=0.05). Results: Significant differences were observed in the range of artifacts produced by different thicknesses of cobalt-chromium alloys (all P<0.05), with 1 mm resulting in the least artifacts. The SSIM values for specimens with thicknesses of 1.0 mm, 1.5 mm, and 2.0 mm were 0.916±0.019, 0.873±0.010, and 0.833±0.010, respectively (F=447.89, P<0.001). The corresponding PSNR values were 20.834±1.176, 17.002±0.427, and 14.673±0.429, respectively (F=796.51, P<0.001). After applying CNN-MARS and U-net-MARS to artifact removal, the SSIM and PSNR values significantly increased for images with the same thickness of metal (both P<0.05). When using the CNN-MARS for artifact removal, the SSIM values for 1.0, 1.5 and 2.0 mm were 0.938±0.023, 0.930±0.029, and 0.928±0.020 (F=2.22, P=0.112), while the PSNR values were 30.938±1.495, 30.578±2.154 and 30.553±2.355 (F=0.54, P=0.585). When using the U-net-MARS for artifact removal, the SSIM values for 1.0, 1.5 and 2.0 mm were 0.930±0.024, 0.932±0.017 and 0.930±0.012 (F=0.24, P=0.788), and the PSNR values were 30.291±0.934, 30.351±1.002 and 30.271±1.143 (F=0.07, P=0.929). No significant differences were found in SSIM and PSNR values after artifact removal using CNN-MARS and U-net-MARS for different thicknesses of cobalt-chromium alloys (all P>0.05). Visualization demonstrated a high degree of similarity between the images before and after artifact removal using both MARSs. However, CNN-MARS displayed clearer metal edges and preserved more tissue details when compared with U-net-MARS. Conclusions: Both the CNN-MARS and U-net-MARS models developed in this study effectively remove the metal artifacts and enhance the image quality. CNN-MARS exhibited an advantage in restoring tissue structure information around the artifacts compared to U-net-MARS.
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Affiliation(s)
- L H Jia
- Department of Prosthodontics, School and Hospital of Stomatology, Fujian Medical University, Fuzhou 350002, China
| | - H L Lin
- Department of Prosthodontics, School and Hospital of Stomatology, Fujian Medical University, Fuzhou 350002, China
| | - S W Zheng
- College of Computer and Data Science, Fuzhou University, Fuzhou 350108, China
| | - X J Lin
- Department of Prosthodontics, School and Hospital of Stomatology, Fujian Medical University, Fuzhou 350002, China
| | - D Zhang
- College of Computer and Data Science, Fuzhou University, Fuzhou 350108, China
| | - H Yu
- Department of Prosthodontics, School and Hospital of Stomatology, Fujian Medical University, Fuzhou 350002, China
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Zhao Y, Jiang M, Liu M, Wang H, Wang W, Zhang T, Tian X, Hong L, Yang F, Wang Y, Zou Y, Yu H, Li Z, Yang W. Spatial Distribution and Characterization of the Small-Molecule Metabolites and In Situ Hydrolyzed Oligosaccharides in the Rhizome of Glycyrrhiza uralensis by Desorption Electrospray Ionization-Mass Spectrometry Imaging and High-Resolution Liquid Chromatography-Mass Spectrometry. J Agric Food Chem 2023; 71:20372-20385. [PMID: 38055271 DOI: 10.1021/acs.jafc.3c04996] [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] [Subscribe] [Scholar Register] [Indexed: 12/07/2023]
Abstract
Characterization and spatial distribution studies of the metabolome in plants are crucial for revealing the physiology of plants and developing functional foods. Using the rhizome of Glycyrrhiza uralensis as a case, we integrated desorption electrospray ionization-mass spectrometry imaging (DESI-MSI) and high-resolution liquid chromatography/mass spectrometry approaches aimed at characterizing and locating both the small molecules and the macromolecular polysaccharides. Under the optimal conditions, 21 flavonoids and 12 triterpenoids were detected and characterized in different tissues of the rhizome and another 19 components were characterized exclusively by DESI-MSI. Combined with hydrophilic interaction chromatography/ion mobility-quadrupole time-of-flight mass spectrometry, eight different degrees of polymerization of oligosaccharides (after in situ acid hydrolysis) were characterized from the rhizome of G. uralensis. Majority of these metabolites are located in the cortex, phloem, and medulla, which lays the foundation for understanding the physiology of G. uralensis. The useful information can benefit the sustainable utilization and further development of Glycyrrhiza resource.
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Affiliation(s)
- Yuying Zhao
- Haihe Laboratory of Modern Chinese Medicine, Tianjin University of Traditional Chinese Medicine, 10 Poyanghu Road, Tianjin 301617, China
- National Key Laboratory of Chinese Medicine Modernization, State Key Laboratory of Component-based Chinese Medicine, Tianjin University of Traditional Chinese Medicine, 10 Poyanghu Road, Tianjin 301617, China
- Tianjin Key Laboratory of Therapeutic Substance of Traditional Chinese Medicine, Tianjin University of Traditional Chinese Medicine, 10 Poyanghu Road, Tianjin 301617, China
| | - Meiting Jiang
- Haihe Laboratory of Modern Chinese Medicine, Tianjin University of Traditional Chinese Medicine, 10 Poyanghu Road, Tianjin 301617, China
- National Key Laboratory of Chinese Medicine Modernization, State Key Laboratory of Component-based Chinese Medicine, Tianjin University of Traditional Chinese Medicine, 10 Poyanghu Road, Tianjin 301617, China
| | - Meiyu Liu
- Haihe Laboratory of Modern Chinese Medicine, Tianjin University of Traditional Chinese Medicine, 10 Poyanghu Road, Tianjin 301617, China
- National Key Laboratory of Chinese Medicine Modernization, State Key Laboratory of Component-based Chinese Medicine, Tianjin University of Traditional Chinese Medicine, 10 Poyanghu Road, Tianjin 301617, China
| | - Hongda Wang
- Haihe Laboratory of Modern Chinese Medicine, Tianjin University of Traditional Chinese Medicine, 10 Poyanghu Road, Tianjin 301617, China
- National Key Laboratory of Chinese Medicine Modernization, State Key Laboratory of Component-based Chinese Medicine, Tianjin University of Traditional Chinese Medicine, 10 Poyanghu Road, Tianjin 301617, China
| | - Wei Wang
- Haihe Laboratory of Modern Chinese Medicine, Tianjin University of Traditional Chinese Medicine, 10 Poyanghu Road, Tianjin 301617, China
- National Key Laboratory of Chinese Medicine Modernization, State Key Laboratory of Component-based Chinese Medicine, Tianjin University of Traditional Chinese Medicine, 10 Poyanghu Road, Tianjin 301617, China
| | - Tingting Zhang
- Haihe Laboratory of Modern Chinese Medicine, Tianjin University of Traditional Chinese Medicine, 10 Poyanghu Road, Tianjin 301617, China
- National Key Laboratory of Chinese Medicine Modernization, State Key Laboratory of Component-based Chinese Medicine, Tianjin University of Traditional Chinese Medicine, 10 Poyanghu Road, Tianjin 301617, China
| | - Xiaoxuan Tian
- Haihe Laboratory of Modern Chinese Medicine, Tianjin University of Traditional Chinese Medicine, 10 Poyanghu Road, Tianjin 301617, China
- National Key Laboratory of Chinese Medicine Modernization, State Key Laboratory of Component-based Chinese Medicine, Tianjin University of Traditional Chinese Medicine, 10 Poyanghu Road, Tianjin 301617, China
| | - Lili Hong
- Haihe Laboratory of Modern Chinese Medicine, Tianjin University of Traditional Chinese Medicine, 10 Poyanghu Road, Tianjin 301617, China
- National Key Laboratory of Chinese Medicine Modernization, State Key Laboratory of Component-based Chinese Medicine, Tianjin University of Traditional Chinese Medicine, 10 Poyanghu Road, Tianjin 301617, China
| | - Feifei Yang
- Haihe Laboratory of Modern Chinese Medicine, Tianjin University of Traditional Chinese Medicine, 10 Poyanghu Road, Tianjin 301617, China
- National Key Laboratory of Chinese Medicine Modernization, State Key Laboratory of Component-based Chinese Medicine, Tianjin University of Traditional Chinese Medicine, 10 Poyanghu Road, Tianjin 301617, China
| | - Yu Wang
- Haihe Laboratory of Modern Chinese Medicine, Tianjin University of Traditional Chinese Medicine, 10 Poyanghu Road, Tianjin 301617, China
- National Key Laboratory of Chinese Medicine Modernization, State Key Laboratory of Component-based Chinese Medicine, Tianjin University of Traditional Chinese Medicine, 10 Poyanghu Road, Tianjin 301617, China
| | - Yadan Zou
- Haihe Laboratory of Modern Chinese Medicine, Tianjin University of Traditional Chinese Medicine, 10 Poyanghu Road, Tianjin 301617, China
- National Key Laboratory of Chinese Medicine Modernization, State Key Laboratory of Component-based Chinese Medicine, Tianjin University of Traditional Chinese Medicine, 10 Poyanghu Road, Tianjin 301617, China
| | - Heshui Yu
- Haihe Laboratory of Modern Chinese Medicine, Tianjin University of Traditional Chinese Medicine, 10 Poyanghu Road, Tianjin 301617, China
- National Key Laboratory of Chinese Medicine Modernization, State Key Laboratory of Component-based Chinese Medicine, Tianjin University of Traditional Chinese Medicine, 10 Poyanghu Road, Tianjin 301617, China
- College of Pharmaceutical Engineering of Traditional Chinese Medicine, Tianjin University of Traditional Chinese Medicine, 10 Poyanghu Road, Tianjin 301617, China
| | - Zheng Li
- Haihe Laboratory of Modern Chinese Medicine, Tianjin University of Traditional Chinese Medicine, 10 Poyanghu Road, Tianjin 301617, China
- National Key Laboratory of Chinese Medicine Modernization, State Key Laboratory of Component-based Chinese Medicine, Tianjin University of Traditional Chinese Medicine, 10 Poyanghu Road, Tianjin 301617, China
- College of Pharmaceutical Engineering of Traditional Chinese Medicine, Tianjin University of Traditional Chinese Medicine, 10 Poyanghu Road, Tianjin 301617, China
| | - Wenzhi Yang
- Haihe Laboratory of Modern Chinese Medicine, Tianjin University of Traditional Chinese Medicine, 10 Poyanghu Road, Tianjin 301617, China
- National Key Laboratory of Chinese Medicine Modernization, State Key Laboratory of Component-based Chinese Medicine, Tianjin University of Traditional Chinese Medicine, 10 Poyanghu Road, Tianjin 301617, China
- Tianjin Key Laboratory of Therapeutic Substance of Traditional Chinese Medicine, Tianjin University of Traditional Chinese Medicine, 10 Poyanghu Road, Tianjin 301617, China
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Su C, Lu ZC, Yu H. [10-Methacryloyloxydecyl dihydrogen phosphate in resin-to-zirconia bonding durability: a systematic review and meta-analysis]. Zhonghua Kou Qiang Yi Xue Za Zhi 2023; 58:1281-1290. [PMID: 38061871 DOI: 10.3760/cma.j.cn112144-20230915-00158] [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 systematically assess the durability of the 10-methacryloyloxydecyl dihydrogen phosphate (10-MDP) pretreated resin-to-zirconia bonding and conducted a meta-analysis to provide clinical guidance on zirconia bonding strategies. Methods: A comprehensive search was conducted on PubMed, Scopus, Web of Science, CNKI, and Wanfang database to identify relevant studies on the resin-to-zirconia bonding after surface pretreatment with 10-MDP. Strict inclusion and exclusion criteria were applied to select appropriate literature and extract essential information and data. The included studies were categorized based on aging methods (water storage, thermocycling, or both), 10-MDP application methods (within primer, adhesive, resin cement, or both), and additional surface treatments (alumina sandblasting, tribochemical silica coating, acid etching, laser etching, and plasma treatment) and were analyzed by Review Manager 5.4. The evaluation indicator was the bonding strength of zirconia after surface pretreatment with 10-MDP. Results: A total of 72 studies were included in the systematic review, with 68 studies eligible for the meta-analysis. The bonding strength of zirconia decreased significantly after aging [P<0.001; mean difference (MD): 5.58; 95%CI: 5.11-6.05]. No significant differences in bonding strength of zirconia were observed after aging when employing various application methods of 10-MDP (all P>0.05). The bonding strength of zirconia was significantly enhanced after aging when 10-MDP was applied in conjunction with additional surface treatments, as compared to the application of 10-MDP alone (P<0.001; MD: 10.17; 95%CI: 8.20-12.14). Conclusions: The bonding strength of zirconia pretreated with 10-MDP exhibited a reduction after undergoing water storage or thermocycling. The application of 10-MDP with additional surface treatments enhanced the bonding strength of zirconia after aging, while the application methods of 10-MDP did not exert an influence.
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Affiliation(s)
- C Su
- Department of Prosthodontics, School and Hospital of Stomatology, Fujian Medical University & Fujian Key Laboratory of Oral Diseases & Fujian Provincial Engineering Research Center of Oral Biomaterial & Stomatological Key Laboratory of Fujian College and University & Institute of Stomatology, Fujian Medical University & Research Center of Dental Esthetics and Biomechanics, Fujian Medical University, Fuzhou 350002, China
| | - Z C Lu
- Department of Prosthodontics, School and Hospital of Stomatology, Fujian Medical University & Fujian Key Laboratory of Oral Diseases & Fujian Provincial Engineering Research Center of Oral Biomaterial & Stomatological Key Laboratory of Fujian College and University & Institute of Stomatology, Fujian Medical University & Research Center of Dental Esthetics and Biomechanics, Fujian Medical University, Fuzhou 350002, China
| | - H Yu
- Department of Prosthodontics, School and Hospital of Stomatology, Fujian Medical University & Fujian Key Laboratory of Oral Diseases & Fujian Provincial Engineering Research Center of Oral Biomaterial & Stomatological Key Laboratory of Fujian College and University & Institute of Stomatology, Fujian Medical University & Research Center of Dental Esthetics and Biomechanics, Fujian Medical University, Fuzhou 350002, China
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Li X, Wu M, Ding H, Li W, Yin J, Lin R, Wu X, Han L, Yang W, Bie S, Li F, Song X, Yu H, Dong Z, Li Z. Integration of non-targeted multicomponent profiling, targeted characteristic chromatograms and quantitative to accomplish systematic quality evaluation strategy of Huo-Xiang-Zheng-Qi oral liquid. J Pharm Biomed Anal 2023; 236:115715. [PMID: 37769526 DOI: 10.1016/j.jpba.2023.115715] [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: 07/21/2023] [Revised: 09/06/2023] [Accepted: 09/09/2023] [Indexed: 10/03/2023]
Abstract
Huo-Xiang-Zheng-Qi oral liquid (HXZQOL) is a well-known traditional Chinese medicine formula for the treatment of gastrointestinal diseases, with the pharmacologic effects of antiinflammatory, immune protection and gastrointestinal motility regulation. More significantly, HXZQOL is recommended for the treatment of COVID-19 patients with gastrointestinal symptoms, and it has been clinically proven to reduce the inflammatory response in patients with COVID-19. However, the effective and overall quality control of HXZQOL is currently limited due to its complex composition, especially the large amount of volatile and non-volatile active components involved. In this study, aimed to fully develop a comprehensive strategy based on non-targeted multicomponent identification, targeted authentication and quantitative analysis for quality evaluation of HXZQOL from different batches. Firstly, the non-targeted high-definition MSE (HDMSE) approach is established based on UHPLC/IM-QTOF-MS, utilized for multicomponent comprehensive characterization of HXZQOL. Combined with in house library-driven automated peak annotation and comparison of 47 reference compounds, 195 components were initially identified. In addition, HS-SPME-GC-MS was employed to analyze the volatile organic compounds (VOCs) in HXZQOL, and a total of 61 components were identified by comparison to the NIST database, reference compounds as well as retention indices. Secondly, based on the selective ion monitoring (SIM) of 24 "identity markers" (involving each herbal medicine), characteristic chromatograms (CCs) were established on LC-MS and GC-MS respectively, to authenticate 15 batches of HXZQOL samples. The targeted-SIM CCs showed that all marker compounds in 15 batches of samples could be accurately monitored, which could indicate preparations authenticity. Finally, a parallel reaction monitoring (PRM) method was established and validated to quantify the nine compounds in 15 batches of HXZQOL. Conclusively, this study first reports chemical-material basis, SIM CCs and quality evaluation of HXZQOL, which is of great implication to quality control and ensuring the authenticity of the preparation.
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Affiliation(s)
- Xuejuan Li
- College of Pharmaceutical Engineering of Traditional Chinese Medicine, Tianjin University of Traditional Chinese Medicine, 301617, China; Haihe Laboratory of Modern Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin 301617, China
| | - Mengfan Wu
- College of Pharmaceutical Engineering of Traditional Chinese Medicine, Tianjin University of Traditional Chinese Medicine, 301617, China; Haihe Laboratory of Modern Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin 301617, China
| | - Hui Ding
- College of Pharmaceutical Engineering of Traditional Chinese Medicine, Tianjin University of Traditional Chinese Medicine, 301617, China; Haihe Laboratory of Modern Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin 301617, China
| | - Wei Li
- College of Pharmaceutical Engineering of Traditional Chinese Medicine, Tianjin University of Traditional Chinese Medicine, 301617, China; Haihe Laboratory of Modern Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin 301617, China
| | - Jiaxin Yin
- College of Pharmaceutical Engineering of Traditional Chinese Medicine, Tianjin University of Traditional Chinese Medicine, 301617, China; Haihe Laboratory of Modern Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin 301617, China
| | - Ruimei Lin
- College of Pharmaceutical Engineering of Traditional Chinese Medicine, Tianjin University of Traditional Chinese Medicine, 301617, China; Haihe Laboratory of Modern Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin 301617, China
| | - Xinlong Wu
- College of Pharmaceutical Engineering of Traditional Chinese Medicine, Tianjin University of Traditional Chinese Medicine, 301617, China; Haihe Laboratory of Modern Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin 301617, China
| | - Lifeng Han
- State Key Laboratory of Component-based Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin 301617, China; Haihe Laboratory of Modern Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin 301617, China
| | - Wenzhi Yang
- State Key Laboratory of Component-based Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin 301617, China; Haihe Laboratory of Modern Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin 301617, China
| | - Songtao Bie
- College of Pharmaceutical Engineering of Traditional Chinese Medicine, Tianjin University of Traditional Chinese Medicine, 301617, China; State Key Laboratory of Component-based Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin 301617, China; Haihe Laboratory of Modern Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin 301617, China
| | - Fangyi Li
- College of Pharmaceutical Engineering of Traditional Chinese Medicine, Tianjin University of Traditional Chinese Medicine, 301617, China; State Key Laboratory of Component-based Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin 301617, China; Haihe Laboratory of Modern Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin 301617, China
| | - Xinbo Song
- College of Pharmaceutical Engineering of Traditional Chinese Medicine, Tianjin University of Traditional Chinese Medicine, 301617, China; State Key Laboratory of Component-based Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin 301617, China; Haihe Laboratory of Modern Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin 301617, China
| | - Heshui Yu
- College of Pharmaceutical Engineering of Traditional Chinese Medicine, Tianjin University of Traditional Chinese Medicine, 301617, China; State Key Laboratory of Component-based Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin 301617, China; Haihe Laboratory of Modern Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin 301617, China.
| | - Ziliang Dong
- Chongqing Taiji Industry (Group) Co.,Ltd., 408000, China.
| | - Zheng Li
- College of Pharmaceutical Engineering of Traditional Chinese Medicine, Tianjin University of Traditional Chinese Medicine, 301617, China; State Key Laboratory of Component-based Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin 301617, China; Haihe Laboratory of Modern Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin 301617, China
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Liu K, Chen YJ, Su J, Fan XK, Yu H, Qin Y, Yang J, Zhu Z, Guan HY, Shen C, Pan EC, Lu Y, Zhou JY, Wu M. [Association of category of dietary intake and physical activity with the risk of mortality in patients with type 2 diabetes mellitus: a prospective cohort study]. Zhonghua Liu Xing Bing Xue Za Zhi 2023; 44:1591-1598. [PMID: 37875446 DOI: 10.3760/cma.j.cn112338-20230328-00188] [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: 10/26/2023]
Abstract
Objective: To investigate the association between dietary intake and physical activity category and their combined effects on all-cause and cause-specific mortality risk in patients with type 2 diabetes mellitus (T2DM). Methods: Between December 2013 and December 2021, a prospective cohort study was conducted on 19 863 T2DM patients in Changshu City, Qingjiangpu District (formerly Qinghe District), and Huai'an District, included in the national basic health service management. Information on deaths and underlying causes of death was obtained from the Jiangsu Provincial CDC and Prevention Death Surveillance System. Cox proportional hazards models were used to estimate the intensity of associations between dietary intake, physical activity, and their combined effects with all-cause and cause-specific mortality in patients with T2DM. Results: As of December 31, 2021, the research subjects had been followed up for 150 283 person-years, with a median follow-up time of 8.15 years. During the follow-up period, 3 293 people died, including 1 124 deaths from cardiovascular disease (CVD) and 875 deaths from cancer. Cox regression analysis showed that compared with the population of 0-1 recommended food group, those having more than five recommended food groups had a 19% lower risk of all-cause mortality [hazard ratio (HR)=0.81, 95%CI: 0.70-0.94] and a 33% lower risk of all-cause mortality (HR=0.67, 95%CI: 0.52-0.87). Compared with the T2DM population in the physical activity Q1 group, the risk of all-cause mortality, CVD mortality, and cancer mortality among the physical activity Q4 group reduced by 50% (HR=0.50, 95%CI: 0.45-0.56), 50% (HR=0.50, 95%CI: 0.41-0.61), and 27% (HR=0.73, 95%CI: 0.60-0.88), respectively. The combined effect showed that compared with the population in the intake of food categories 0-2 and low physical activity groups, the risk of all-cause, CVD mortality, and cancer mortality in the intake of food categories 4-9 and high physical activity groups reduced by 55% (HR=0.45, 95%CI: 0.38-0.53), 56% (HR=0.44, 95%CI: 0.32-0.59), and 40% (HR=0.60, 95%CI: 0.44-0.82), respectively. Conclusion: Type of dietary intake, physical activity, and their combined effects are associated with a reduced mortality risk in patients with T2DM.
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Affiliation(s)
- K Liu
- School of Public Health, Southeast University, Nanjing 210009, China
| | - Y J Chen
- Department of Non-communicable Chronic Disease Control and Prevention, Nanjing Center for Disease Control and Prevention, Nanjing 210003, China
| | - J Su
- Department of Non-communicable Chronic Disease Control and Prevention, Jiangsu Provincial Center for Disease Control and Prevention, Nanjing 210009, China
| | - X K Fan
- Department of Non-communicable Chronic Disease Control and Prevention, Jiangsu Provincial Center for Disease Control and Prevention, Nanjing 210009, China
| | - H Yu
- Department of Non-communicable Chronic Disease Control and Prevention, Jiangsu Provincial Center for Disease Control and Prevention, Nanjing 210009, China
| | - Y Qin
- Department of Non-communicable Chronic Disease Control and Prevention, Jiangsu Provincial Center for Disease Control and Prevention, Nanjing 210009, China
| | - J Yang
- Department of Non-communicable Chronic Disease Control and Prevention, Jiangsu Provincial Center for Disease Control and Prevention, Nanjing 210009, China
| | - Z Zhu
- Department of Non-communicable Chronic Disease Control and Prevention, Jiangsu Provincial Center for Disease Control and Prevention, Nanjing 210009, China
| | - H Y Guan
- School of Public Health, Nanjing Medical University, Nanjing 211166, China
| | - C Shen
- School of Public Health, Nanjing Medical University, Nanjing 211166, China
| | - E C Pan
- Department of Chronic Disease Prevention and Control, Huai'an City Center for Disease Control and Prevention, Huai'an 223001, China
| | - Y Lu
- Department of Chronic Disease Prevention and Control, Suzhou City Center for Disease Control and Prevention, Suzhou 215004, China
| | - J Y Zhou
- Department of Non-communicable Chronic Disease Control and Prevention, Jiangsu Provincial Center for Disease Control and Prevention, Nanjing 210009, China
| | - M Wu
- School of Public Health, Southeast University, Nanjing 210009, China Department of Non-communicable Chronic Disease Control and Prevention, Jiangsu Provincial Center for Disease Control and Prevention, Nanjing 210009, China
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Wang H, Yu H, Zhou YY, Cong WM, Dong H. [Combined hepatocellular-cholangiocarcinoma containing both large and small duct type cholangiocarcinoma: report of a case]. Zhonghua Bing Li Xue Za Zhi 2023; 52:1047-1049. [PMID: 37805401 DOI: 10.3760/cma.j.cn112151-20230110-00020] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 10/09/2023]
Affiliation(s)
- H Wang
- Department of Pathology, Eastern Hepatobiliary Surgery Hospital, Naval Medical University, Shanghai 200438, China
| | - H Yu
- Department of Pathology, Eastern Hepatobiliary Surgery Hospital, Naval Medical University, Shanghai 200438, China
| | - Y Y Zhou
- Department of Pathology, Eastern Hepatobiliary Surgery Hospital, Naval Medical University, Shanghai 200438, China
| | - W M Cong
- Department of Pathology, Eastern Hepatobiliary Surgery Hospital, Naval Medical University, Shanghai 200438, China
| | - H Dong
- Department of Pathology, Eastern Hepatobiliary Surgery Hospital, Naval Medical University, Shanghai 200438, China
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Yu J, Jiang L, Zhao L, Wang X, Yang X, Yang D, Zhuo M, Chen H, Zhao YD, Zhou F, Li Q, Zhu Z, Chu L, Ma Z, Wang Q, Qu Y, Huang W, Zhang M, Gu T, Liu S, Yang Y, Yang J, Yu H, Yu R, Zhao J, Shi A. High Dose Hyperfractionated Thoracic Radiotherapy vs. Standard Dose for Limited Stage Small-Cell Lung Cancer: A Multicenter, Open-Label Randomized, Phase 3 Trial. Int J Radiat Oncol Biol Phys 2023; 117:S1. [PMID: 37784261 DOI: 10.1016/j.ijrobp.2023.06.205] [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) Limited stage small-cell lung cancer (LS-SCLC) is associated with poor prognosis. We aimed to assess the efficacy and safety of high-dose, hyperfractionated thoracic radiotherapy of 54 Gy in 30 fractions compared with standard dose (45 Gy in 30 fractions) as a first-line treatment for LS-SCLC. MATERIALS/METHODS The study was an open-label, randomized, phase 3 trial, done at 16 public hospitals in China. Key inclusion criteria were patients aged 18-70 years, with previously histologically or cytologically confirmed LS-SCLC, previously untreated or received 1-2 courses of intravenous cisplatin (75 mg/m²of body-surface area, on day 1 or divided into two days of each cycle) or carboplatin (area under the curve of 5 mg/mL per min, day 1 of each cycle)and intravenous etoposide (100 mg/m²of body-surface area, on days 1-3 of each cycle), and an Eastern Cooperative Oncology Group (ECOG) performance status of 0-1.Eligible patients were randomly assigned (1:1) to receive volumetric-modulated arc radiotherapy (VMAT) of 45 Gy in 30 fractions or the simultaneous integrated boost VMAT (SIB-VMAT) of 54 Gy in 30 fractions to the primary lung tumor and lymph node metastases starting 0-42 days after the first chemotherapy course. Both groups of patients received thoracic radiotherapy twice per day and 10 fractions per week. Prophylactic cranial radiation (PCI, 25 Gy in 10 fractions) was implemented to patients with responsive disease. The primary endpoint was overall survival. Safety was analyzed in the as-treated population. RESULTS Between June 30, 2017, and April 6, 2021, 224 eligible patients were enrolled and randomly assigned to 54 Gy (n = 108) or 45 Gy (n = 116). Median follow-up for the primary analysis was 45 months (IQR 41-48). Median overall survival was significantly improved in the 54 Gy group (62.4 months) compared with the 45 Gy group (43.1 months; p = 0.001). Median progression-free survival was significantly improved in the 54 Gy group (30.5 months) compared with the 45 Gy group (16.7 months; p = 0.044). The most common grade 3-4 adverse events were neutropenia (30 [28%] of 108 patients in the 54 Gy group vs 27 [23%] of 116 patients in the 45 Gy group), neutropenic infections (6 [6%] vs 2 [2%]), thrombocytopenia (13 [12%] vs 12 [10%]), anemia (6 [6%] vs 4 [3%]), and esophagitis (1 [1%] vs 3 [3%]). Treatment-related serious adverse events occurred in 9 [8%] patients in the 54 Gy group and 16 [14%] patients in the 45 Gy group. There were one treatment-related deaths in 54 Gy group (myocardial infarction). CONCLUSION Compared with standard thoracic radiotherapy dose of 45 Gy, the high dose of 54 Gy improved overall survival and progression-free survival without increasing toxicities in patients with LS-SCLC, supporting twice-daily hyperfractionated thoracic radiotherapy of 54 Gy with concurrent chemotherapy is an alternative treatment option for LS-SCLC. This study is complete and registered with ClinicalTrials.gov, NCT03214003.
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Affiliation(s)
- J Yu
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Radiation Oncology, Peking University Cancer Hospital & Institute, Beijing, China
| | - L Jiang
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Radiation Oncology, Peking University Cancer Hospital & Institute, Beijing, China
| | - L Zhao
- Department of Radiation Oncology, Xijing Hospital, Fourth Military Medical University. ty, Xi'an, China
| | - X Wang
- Department of Radiation Oncology, Anyang Cancer Hospital, Anyang, China
| | - X Yang
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Department of Thoracic Medical Oncology, Peking University Cancer Hospital and Institute, Beijing, China., Beijing, China
| | - D Yang
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Radiation Oncology, Peking University Cancer Hospital & Institute, Beijing, China
| | - M Zhuo
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Department of Thoracic Medical Oncology, Peking University Cancer Hospital and Institute, Beijing, China., Beijing, China
| | - H Chen
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Department of Thoracic Medical Oncology, Peking University Cancer Hospital and Institute, Beijing, China., Beijing, China
| | - Y D Zhao
- Department of Radiation Oncology, Anyang Tumor Hospital, Anyang, China
| | - F Zhou
- Yantai Yuhuangding Hospital, Yantai, China
| | - Q Li
- Ordos School of Clinical Medicine I.M.M.U, Ordos, China
| | - Z Zhu
- Department of Radiation Oncology, Fudan University Shanghai Cancer Center, Shanghai, China
| | - L Chu
- Fudan University Shanghai Cancer Center, Shanghai, China
| | - Z Ma
- Chifeng Affiliated Hospital, Chifeng, China
| | - Q Wang
- Department of Radiation Oncology, Sichuan Cancer Hospital and Institution, Chengdu, China
| | - Y Qu
- Liaoning cancer hospital & institute, Shenyang, China
| | - W Huang
- Shandong Cancer Hospital & Institute, Jinan, Shandong, China
| | - M Zhang
- Department of Radiation Oncology, Peking University People's Hospital, Beijing, China; Department of Radiation Oncology, Peking University First Hospital, Peking University, Beijing, China
| | - T Gu
- The First Hospital of Qinhuangdao, Qinhuangdao, China
| | - S Liu
- Jilin Provincial Cancer Hospital, Changchun, China
| | - Y Yang
- Jilin Provincial Cancer Hospital, Changchun, China
| | - J Yang
- Department of Oncology, The first Affiliated Hospital of Xinxiang Medical University, Weihui, China
| | - H Yu
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Radiation Oncology, Peking University Cancer Hospital & Institute, Beijing, China
| | - R Yu
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Radiation Oncology, Peking University Cancer Hospital & Institute, Beijing, China
| | - J Zhao
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Department of Thoracic Medical Oncology, Peking University Cancer Hospital and Institute, Beijing, China., Beijing, China
| | - A Shi
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Radiation Oncology, Peking University Cancer Hospital & Institute, Beijing, China
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Chen F, Zhou P, Lee KW, Liu Q, Helali AE, Jin JY, Lee AWM, Yu H, Kong FM. Interpretable Deep Learning Identified the Significance of 1 Gy Volume on Lymphopenia after Radiotherapy in Breast Cancer. Int J Radiat Oncol Biol Phys 2023; 117:e168. [PMID: 37784771 DOI: 10.1016/j.ijrobp.2023.06.1006] [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) Lymphopenia is common after radiotherapy (RT) and is known for its significance on poor survival outcomes in patients with breast cancer. Previous work has demonstrated the significance of point dosimetric factors like the volume receiving 5 Gy. Considering the full dosimetric data together, this study aimed to develop and validate predictive models for lymphopenia after RT in breast cancer. MATERIALS/METHODS Patients with breast cancer treated with radiation therapy in adjuvant setting and with complete dosimetric data were eligible. Combining dose-volume histogram (DVH) dosimetric and clinical factors, dense neural network (DNN) models were developed to predict both the reduction in lymphocyte counts and the graded lymphopenia in breast cancer patients after adjuvant RT. A Shapley additive explanation was applied to explain each feature's directional contributions. The generalization of DNN models was validated in both internal and independent external validation cohorts. P<0.05 was considered to be significant. RESULTS A total of 928 consecutive patients with invasive breast cancer were eligible for this study. Treatment volumes of nearly all irradiation dose levels of DVH were significant predictors for lymphopenia after RT, including volumes at very low-dose 1 Gy (V1) of all structures considered including the lung, heart and body. DNN models using full DVH dosimetric and clinical factors were built and a simplified model was further established and validated in both internal and external validation cohorts. This simplified DNN AI model, combining full DVH dosimetric parameters of all OARs and five key clinical factors including baseline lymphocyte counts, tumor stage, RT technique, RT fields and RT fractionation, showed a predictive accuracy of 77% and above. CONCLUSION This study demonstrated and externally validated the significance of an AI model of combining clinical and full dosimetric data, especially the volume of low dose at as low as 1 Gy of all critical structures on lymphopenia after RT in patients with breast cancer. The significance of V1 deserves special attention, as modern arc RT technology often has relatively high value of this parameter. Further study warranted for breast cancer RT plan optimization.
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Affiliation(s)
- F Chen
- The University of Hong Kong-Shenzhen Hospital, Shenzhen, China; The University of Hong Kong, Hong Kong, China
| | - P Zhou
- Department of Radiotherapy, The First Affiliated Hospital of Hainan Medical University, Haikou, China
| | - K W Lee
- The University of Hong Kong-Shenzhen Hospital, Shenzhen, China
| | - Q Liu
- The University of Hong Kong, Hong Kong, China
| | - A E Helali
- The University of Hong Kong, Hong Kong, China
| | - J Y Jin
- School of biomedical engineering, Capital Medical University, Beijing, China
| | - A W M Lee
- The University of Hong Kong-Shenzhen Hospital, Shenzhen, China
| | - H Yu
- Chinese Academy of Sciences Shenzhen Institutes of Advanced Technology, Shenzhen, China, Shenzhen, China
| | - F M Kong
- The University of Hong Kong-Shenzhen Hospital, Shenzhen, China; The University of Hong Kong, Hong Kong, China
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23
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Zhou Z, Wang Y, Zhao F, Yao G, Yu H, Yu H, Bu L, Lu Z, Yan S. Radiation Induced Lung Injury in Rats after Pre-Oxygenation Radiotherapy. Int J Radiat Oncol Biol Phys 2023; 117:e279-e280. [PMID: 37785046 DOI: 10.1016/j.ijrobp.2023.06.1260] [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) Deep inspiratory breath holding (DIBH) has been widely used during the radiotherapy of thoracic tumors. The main disadvantage of voluntary DIBH is the short duration of each breath hold. The hypocapnia induced by hyperoxia (oxygen concentration > 50%) pre-oxygenation (PreO2) combined with mechanical hyperventilation has been reported to prolong the duration of single breath hold, but its safety remains controversial, especially the sensitivity of lung tissue to radiation damage under hyperoxia exposure has not been elucidated. In this study, we aim to investigate the changes of radiation induced lung injury in rats after PreO2 radiation. MATERIALS/METHODS We evaluated the lung tissue of rats at different time points (48h, 2w, 4w, 8w, 12w) after thoracic radiation (15Gy single fraction to the right lung), and sequenced the transcriptome of lung tissue at 48 hours after irradiation. Rat cohorts (n = 7/group): 1. Control (Con); 2. Radiation group (RT); 3. Pre-oxygenation (oxygen concentration > 90%) for 8 hours before thoracic radiation (PreO2). RESULTS The inflammatory exudation emerged in the pulmonary interstitium at 48 hours, and reached the most serious alveolitis after four weeks of irradiation (the comparison of alveolitis scores in RT4w vs Con4w and PreO2(4w) vs Con4w, P<0.001) on hematoxylin-eosin staining. While the alveolitis scores in RT group and PreO2 group were not statistically different at each time point. Masson staining showed that the pulmonary fibrosis in the RT group and the PreO2 group reached an obvious pathological change at 12 weeks after irradiation, but the difference between the two groups was not significant. Transcriptome sequencing showed that the number of differential genes in PreO2 vs Con was 559 (302 up-regulated genes and 257 down-regulated genes). The GO enrichment analysis indicated that chromosome segregation was the most significant functional item with P value in the comparative analysis, and the KEGG enrichment analysis suggested that cell division was the most significant enrichment pathway of these differential genes. While there was a small quantity of differential genes in PreO2 vs RT group (3 up-regulated genes and 12 down-regulated genes). Pentose and glucuronate conversions were the most significant enrichment pathway of these differential genes. CONCLUSION This study demonstrated that PreO2 radiotherapy did not increase the severity of radiation induced lung injury in rats compared to conventional radiotherapy. Further study should be conducted to confirm these results and to investigate the regulatory mechanism of pneumonia caused by PreO2 radiotherapy.
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Affiliation(s)
- Z Zhou
- Department of Radiation Oncology, the First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China
| | - Y Wang
- Department of Radiation Oncology, the First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China
| | - F Zhao
- Department of Radiation Oncology, the First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, Zhejiang, China
| | - G Yao
- Department of Radiation Oncology, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - H Yu
- The First Affiliated Hospital Zhejiang University School of Medicine, Hangzhou, China
| | - H Yu
- Department of Radiation Oncology, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - L Bu
- Department of Radiation Oncology, the First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China
| | - Z Lu
- Department of Radiation Oncology, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - S Yan
- Department of Radiation Oncology, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
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Zhang Y, Ye X, Ge J, Guo D, Zheng D, Yu H, Chen Y, Yao G, Lu Z, Yuille A, Lu L, Jin D, Yan S. Deep Learning-Based Multi-Modality Segmentation of Primary Gross Tumor Volume in CT and MRI for Nasopharyngeal Carcinoma. Int J Radiat Oncol Biol Phys 2023; 117:e498. [PMID: 37785566 DOI: 10.1016/j.ijrobp.2023.06.1739] [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) The delineation of primary gross tumor volume (GTV) of nasopharyngeal carcinoma (NPC) is an essential step for radiotherapy planning. In clinical practice, radiation oncologists manually delineate the GTV in planning CT with the help of diagnostic MRI. This is because NPC tumors are closely adjacent to many important anatomic structures, and CT and MRI provide complementary strength to accurately determine the tumor extension boundary. Manual delineation is time-consuming with the potential registration errors between MRI and CT decreasing the delineation accuracy. In this study, we propose a fully automated GTV segmentation method based on CT and MRI by first aligning MRI to CT, and then, segmenting the GTV using a multi-modality deep learning model. MATERIALS/METHODS We collected 104 nasopharyngeal carcinoma patients with both planning CT and diagnostic MRI scans (T1 & T2 phases). An experienced radiation oncologists manually delineated the GTV, which was further examined by another senior radiation oncologist. Then, a coarse to fine cross-modality registration from MRI to CT was conducted as follows: (1) A rigid transformation was performed on MRI to roughly align MRI to CT with similar anatomic position. (2) Then, the region of interest (RoI) on both CT and rigid-transformed MRI were cropped. (3) A leading cross-modality deformable registration algorithm, named DEEDS, was applied on the cropped MRI and CT RoIs to find an accurate local alignment. Next, using CT and registered MRI as the combined input, a multi-modality deep segmentation network based on nnUNet was trained to generate the GTV prediction. 20% patients were randomly selected as the unseen testing set to quantitatively evaluate the performance. RESULTS The quantitative NPC GTV segmentation performance is summarized in Table 1. The deep segmentation model using CT alone achieved reasonable high performance with 76.6% Dice score and 1.34mm average surface distance (ASD). When both CT and registered MRI were used, the segmentation model further improved the performance by 0.9% Dice score increase and 11% relative ASD error reduction, demonstrating the complementary strength of CT and MRI in determining NPC GTV. Notably, the achieved 77.5% Dice score and 1.19mm ASD by the multimodality model is among the top performing results reported in recent automatic NPC GTV segmentation using either CT or MRI modality. CONCLUSION We developed a fully automated multi-modal deep-learning model for NPC GTV segmentation. The developed model can segment the NPC GTV in high accuracy. With further optimization and validation, this automated model has potential to standardize the NPC GTV segmentation and significantly decrease the workload of radiation oncologists in clinical practice.
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Affiliation(s)
- Y Zhang
- Johns Hopkins University, Baltimore, MD
| | - X Ye
- Department of Radiation Oncology, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - J Ge
- Department of Radiation Oncology, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - D Guo
- Alibaba Group (US) Inc., New York, NY
| | - D Zheng
- Department of Radiation Oncology, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - H Yu
- Department of Radiation Oncology, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Y Chen
- Department of Radiation Oncology, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - G Yao
- Department of Radiation Oncology, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Z Lu
- Department of Radiation Oncology, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - A Yuille
- Johns Hopkins University, Baltimore, MD
| | - L Lu
- Alibaba Group (US) Inc., New York, NY
| | - D Jin
- Alibaba Group (US) Inc., New York, NY
| | - S Yan
- Department of Radiation Oncology, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
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Park SH, Jeong S, Yu H, Woo D, Chong GO, Han HS, Kim J. Deep Learning vs. Handcrafted Radiomics to Predict Chemoradiotherapy Response for Locally Advanced Cervical Cancer. Int J Radiat Oncol Biol Phys 2023; 117:e480. [PMID: 37785521 DOI: 10.1016/j.ijrobp.2023.06.1700] [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) To predict CRT response in locally advanced cervical cancer (LACC) with handcrafted radiomics (HCR) and deep learning radiomics (DLR) using pretreatment MRI. Furthermore, we investigate whether the incorporation of clinical factors improves prediction performance. MATERIALS/METHODS Two hundred and fifty-two patients with LACC are enrolled. All patients are treated with external beam radiotherapy, followed by high-dose-rate intracavitary brachytherapy with concurrent cisplatin. The patients are randomly divided into two independent groups for the training (167 patients) and test datasets (85 patients). Contrast-enhanced T1- and T2-weighted MR scans are obtained. Patients in the training and test sets have similar characteristics in terms of age, tumor size, FIGO stage, HPV infection status, or CRT response. For HCR analysis, 1890 imaging features are extracted and a support vector machine classifier with a five-fold cross-validation is trained using training dataset to predict CRT response and validated using test dataset. For DLR analysis, a 3-dimensional convolutional neural network was trained and validated using test dataset. RESULTS A comparison of the DLR and HCR models reveals that the DLR model exhibits better prediction performance than the HCR model for the test dataset (AUC = 0.721 vs. 0.597, p = 0.097). The incorporation of clinical factors could improve performance in both DLR and HCR models. CONCLUSION The DLR models outperform the HCR models in predicting CRT responses in patients with LACC. Combining clinical factors and MRI may improve the prediction performance in both HCR and DLR analyses.
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Affiliation(s)
- S H Park
- Department of Radiation Oncology, School of Medicine, Kyungpook National University, Daegu, Korea, Republic of (South) Korea
| | - S Jeong
- Department of Medical Informatics, School of Medicine, Kyungpook National University, Daegu, Korea, Republic of (South) Korea; Research Center for Artificial Intelligence in Medicine, Kyungpook National University Hospital, Daegu, Korea, Republic of (South) Korea
| | - H Yu
- Research Center for Artificial Intelligence in Medicine, Kyungpook National University Hospital, Daegu, Korea, Republic of (South) Korea
| | - D Woo
- Research Center for Artificial Intelligence in Medicine, Kyungpook National University Hospital, Daegu, Korea, Republic of (South) Korea
| | - G O Chong
- Department of Obstetrics and Gynecology, School of Medicine, Kyungpook National University, Daegu, Korea, Republic of (South) Korea; Clinical Omics Research Center, School of Medicine, Kyungpook National University, Daegu, Korea, Republic of (South) Korea
| | - H S Han
- Clinical Omics Research Center, School of Medicine, Kyungpook National University, Daegu, Korea, Republic of (South) Korea; Department of Physiology, School of Medicine, Kyungpook National University, Daegu, Korea, Republic of (South) Korea
| | - J Kim
- Department of Radiation Oncology, School of Medicine, Kyungpook National University, Daegu, Korea, Republic of (South) Korea
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Danyang Z, Xu Z, Ye B, Zhang Y, Zhao C, Xu W, Liang Z, Yu H, Kong FM. Single-Cell and Spatial Transcriptomics Revealing the Role of IDO1 in HPV+ Cervical Cancer Tumor Immune Microenvironment and Its Implications in Radiotherapy and Immunotherapy. Int J Radiat Oncol Biol Phys 2023; 117:S157-S158. [PMID: 37784395 DOI: 10.1016/j.ijrobp.2023.06.583] [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) Persistent infection of human papillomavirus (HPV) is one major etiology of cervical cancer (CC). By now, anti-PD-1 immunotherapy is approved for advanced CC patients, but the response rate was just about 10-20%, tumor immune microenvironment (TIME) might be one factor that affect the efficacy. The indoleamine 2,3-dioxygenase (IDO), a metabolic immune checkpoint, is recently shown to have a correlation-ship with HPV carcinogenesis in CC, with unknown mechanism. This study, using the single cell transcriptomic single-cell sequencing and spatial transcription sequencing analysis/immunologic technology, aimed to exam the role of IDO1 expression in HPV+ CC TIME and explore the changes after radiotherapy. MATERIALS/METHODS Newly diagnosed advanced HPV- CC and HPV+ CC patients were tested for the tumor and tumor immune microenvironment (TIME) heterogeneity and their changes after fractionated radiation therapy. Tumor tissues were collected, single cell suspension was made for Single-cell RNA sequencing (SCRNAseq) using the 10 × Genomics, while frozen tissue was embedded for spatial transcriptome sequencing (STRNAseq). Seurat 4.0 was used to cluster and annotate cell clusters and map SCRNAseq data to the STRNAseq data. The specific characters of cell clusters were computed by Gene Set Enrichment Analysis (GSEA). SPOTLight and CellChat were used to analyze cell location and interaction respectively. RESULTS A total of 28631 cells were clustered into 31 cell subsets in HPV- CC and HPV+ CC tissues, including baseline (Pre HPV- CC and Pre HPV+ CC) and 3-week after radiotherapy (Post 3w HPV- CC and Post 3w HPV+CC). There were 10431 epithelial cells (Epi) in all these 4 tumor tissues, with heterogenous IDO1 expression, including IDO1-high Epi, IDO1-low Epi, and IDO1-neg Epi. Interestingly, more than 99% of Epi in Pre HPV- CC tissues were IDO1-neg cells, while more than 99% in Pre HPV+ CC tissue were IDO1-high. Furthermore, the proportion of IDO1-high Epi in Pre HPV+ CC patient dropped to 16.7% after radiotherapy, while the proportion of IDO1-low Epi rase to 63.3%. Using GSEA, the characters of IDO1-high Epi group was shown to have positive regulation of leukocyte chemotaxis and negative regulation of cell adhesion and differentiation. IDO1-high Epi cells also had the hallmark of interferon gamma response. These cells could mainly receive regulative information of interferon gamma pathway from exhausted CD8 T cells, which could affect the apoptosis of tumor cells. CONCLUSION This study comprehensively analyzed the immune suppressive role of IDO1-high Epi cells in HPV+ CC TIME at the single-cell transcriptional scale and explored their functional characters in CC radiotherapy. This would be able to provide more evidence to combine with radiotherapy and immunotherapy to improve patients' prognosis.
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Affiliation(s)
- Z Danyang
- the University of Hong Kong, Hong Kong, China; Department of Clinical Oncology, The University of Hong Kong, Hong Kong, China
| | - Z Xu
- University of Virginia Health System, Charlottesville, VA
| | - B Ye
- Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Y Zhang
- The University of Hong Kong-Shenzhen Hospital, Shenzhen, China
| | - C Zhao
- Department of Clinical Oncology, The University of Hong Kong, Hong Kong, China
| | - W Xu
- The University of Hong Kong-Shenzhen Hospital, Shenzhen, China
| | - Z Liang
- The University of Hong Kong-Shenzhen Hospital, Shenzhen, China
| | - H Yu
- Chinese Academy of Sciences Shenzhen Institutes of Advanced Technology, Shenzhen, China, Shenzhen, China
| | - F M Kong
- The University of Hong Kong, Hong Kong, China
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Iovoli AJ, Yu H, Advani PG, Malhotra H, Fung-Kee-Fung S, Malik NK, Singh AK, Farrugia MK. Sinoatrial Node Irradiation in Patients Undergoing Definitive Stereotactic Body Radiation Therapy (SBRT) for Central Lung Cancers. Int J Radiat Oncol Biol Phys 2023; 117:e27. [PMID: 37785020 DOI: 10.1016/j.ijrobp.2023.06.707] [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) The clinical consequences of sinoatrial node (SAN) and atrioventricular node (AVN) irradiation in patients undergoing thoracic stereotactic body radiation therapy (SBRT) remains unclear. We examined the relationship between SAN and AVN dose with survival outcomes in patients with central non-small cell lung cancer (NSCLC) tumors. MATERIALS/METHODS A single-institutional retrospective review of patients with primary NSCLC undergoing definitive SBRT for centrally located tumors from February 2007 to December 2021 was performed. Central tumors were defined as within 2 cm of the proximal airway, mediastinum, great vessels, or spinal cord whereas ultracentral tumors were directly abutting any of the above structures. All patients underwent five-fraction SBRT to a total dose of 50 to 60 Gy. The SAN and AVN were contoured in accordance with a published contouring atlas and the maximum dose (Dmax) and mean dose (Dmean) for each structure were calculated. Sequential log rank testing between the 50th and 90th percentiles was used to identify potential cutoff values for the corresponding dosimetric parameters and non-cancer associated survival. RESULTS Among 93 eligible patients, the median age was 72.5 years (Inter-Quartile Range [IQR], 66.6-78.3), median follow up was 32.4 months (IQR, 13.0-49.6), and 48 patients were female (52%). There were 49 ultracentral tumors (53%) and the median planning target volume (PTV) was 31.0 cc (IQR, 18.0-53.3). The median SAN Dmax and Dmean were 95 cGy (IQR, 37-1,072) and 58 cGy (IQR, 26-641), respectively. The median AVN Dmax and Dmean were 45 cGy (IQR, 19-506) and 34 cGy (IQR, 15-160), respectively. Candidate cutoff values for SAN Dmax and Dmean were 1,309 cGy and 814 cGy, respectively. No significant cutoff values were identified for either AVN parameter. Kaplan-Meier analysis for the proposed SAN Dmean constraint was significantly associated with overall (p = 0.016) and non-cancer associated survival (p = 0.028). The SAN Dmax constraint was significantly associated with only overall survival (p = 0.029). In a multivariate model, the SAN Dmean cutoff significantly correlated with both overall (Hazard Ratio [HR] 2.1 [1.13-3.78], p = 0.019) and non-cancer associated survival (HR 2.39 [1.12-5.10], p = 0.025) whereas the SAN Dmax cutoff was only significantly associated with overall survival (HR 1.95 [1.03-3.68], p = 0.041). CONCLUSION SAN Dmax and Dmean were associated with significantly worse overall survival using cut-off values of 1,309 cGy and 814 cGy, respectively. SAN dose should be considered in radiation planning and further study on the consequence of SAN irradiation during SBRT is warranted.
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Affiliation(s)
- A J Iovoli
- Roswell Park Comprehensive Cancer Center, Buffalo, NY
| | - H Yu
- Roswell Park Comprehensive Cancer Center, Buffalo, NY
| | - P G Advani
- Roswell Park Comprehensive Cancer Center, Buffalo, NY
| | - H Malhotra
- Roswell Park Comprehensive Cancer Center, Buffalo, NY
| | | | - N K Malik
- Roswell Park Comprehensive Cancer Center, Buffalo, NY
| | - A K Singh
- Roswell Park Comprehensive Cancer Center, Buffalo, NY
| | - M K Farrugia
- Roswell Park Comprehensive Cancer Center, Buffalo, NY
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Veeraraghavan H, Jiang J, Jee J, Lebow ES, Deasy JO, Rimner A, Shaverdian N, Yu H, Gomez DR. AI Serial Image Prediction of Progression-Free Survival (PFS) for Locally Advanced Non-Small Cell Lung Cancer (LA-NSCLC) Patients Treated with Chemoradiation (CRT) and Durvalumab Consolidation. Int J Radiat Oncol Biol Phys 2023; 117:e68. [PMID: 37786001 DOI: 10.1016/j.ijrobp.2023.06.796] [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) Patient outcomes with definitive CRT for LA-NSCLC remain poor, with no imaging biomarkers to predict benefit. Hence, we developed a serial image AI model using paired planning CT (pCT) and first week cone-beam CT (CBCT) to predict PFS and measured AI model fairness defined as the bias in the classification with respect to gender as a protected attribute. MATERIALS/METHODS Sixty-four consecutive patients with LA-NSCLC treated with concurrent CRT to 60 Gy in 30 fractions and durvalumab consolidation were analyzed. Three prediction models were created. A previously developed AI image foundation model [1] was pre-trained with unlabeled 6,402 3D CT scans sourced from institutional and the Cancer Imaging Archive and modified to predict PFS as a binarized outcome (high PFS > 6 months and low PFS < 6 months) using pCT scans. Serial image AI model was created by adding the first week CBCT scan. The third model measured tumor growth rate (TGR) as relative change in tumor and nodal volume from pCT to CBCT derived using a different published AI model [2]. Association with PFS using univariable and multivariable Cox regression after adjusting for age, gender, planning tumor volume, and smoking status were measured using TGR and the two AI model predictions using a cutoff of > 50% probability for low PFS. AI model fairness metrics area under receiver operating curve (AUROC), precision, sensitivity, and specificity were computed. RESULTS TGR was not associated with PFS on univariate (Hazard ratio [HR] of 1.515, 95% confidence interval [CI] of 0.32 to 7.26, p = 0.60) or multivariate analysis (HR: 1.58, 95% CI: 0.32 to 7.80, p = 0.58) and resulted in a Harrell's C-index of 54.7%. The serial image AI model prediction was associated with PFS in both univariable (HR: 2.12, 95% CI: 1.02 to 4.40, p = 0.045) and multivariable analysis (HR 2.39, 95% CI of 1.09 to 5.25, p = 0.029), and a C-index of 62.5%. The pCT AI model was associated with PFS in univariate (HR 2.06, 95% CI of 1.06 to 4.01, p = 0.034) but not in multivariable analysis (HR 1.89, 95% CI of 0.93 to 3.87, p = 0.08), and a C-index of 59.9%. The serial image AI model reduced the parity in classification compared to pCT AI model indicating higher fairness (Table I). CONCLUSION The multi-image AI model predicted PFS with slightly higher accuracy and resulted in higher fairness than the pCT AI model. These results underscore the potential for incorporating multi-imaging biomarkers to predict treatment response.
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Affiliation(s)
- H Veeraraghavan
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY
| | - J Jiang
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY
| | - J Jee
- Memorial Sloan Kettering Cancer Center, New York, NY
| | - E S Lebow
- Department of Radiation Oncology, Memorial Sloan Kettering Cancer Center, New York, NY
| | - J O Deasy
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY
| | - A Rimner
- Department of Radiation Oncology, Memorial Sloan Kettering Cancer Center, New York, NY
| | - N Shaverdian
- Department of Radiation Oncology, Memorial Sloan Kettering Cancer Center, New York, NY
| | - H Yu
- Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY
| | - D R Gomez
- Department of Radiation Oncology, Memorial Sloan Kettering Cancer Center, New York, NY
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Wu S, Yin J, Li X, Xie J, Ding H, Han L, Bie S, Li F, Zhu B, Kang L, Song X, Yu H, Li Z. An Exploration of Dynamic Changes in the Mulberry Growth Process Based on UPLC-Q-Orbitrap-MS, HS-SPME-GC-MS, and HS-GC-IMS. Foods 2023; 12:3335. [PMID: 37761044 PMCID: PMC10529768 DOI: 10.3390/foods12183335] [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] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2023] [Revised: 07/28/2023] [Accepted: 07/31/2023] [Indexed: 09/29/2023] Open
Abstract
This work was designed to investigate the dynamic changes process of non-volatile organic compounds (n-VOCs) and volatile organic compounds (VOCs) in mulberries during different growth periods using UPLC-Q-Orbitrap-MS, HS-SPME-GC-MS, and HS-GC-IMS. A total of 166 compounds were identified, including 68 n-VOCs and 98 VOCs. Furthermore, principal component analysis (PCA), random forest analysis (RFA) and orthogonal partial least squares discriminant analysis (OPLS-DA) were used to analyze differences in mulberries at different ripening stages. A total of 74 compounds appeared or disappeared at different ripening periods and 24 compounds were presented throughout the growth process. Quantitative analysis and antioxidant experiments revealed that as the mulberries continued to mature, flavonoids and phenolic acids continued to increase, and the best antioxidant activity occurred from stage IV. Conclusively, an effective strategy was established for analyzing the composition change process during different growth periods, which could assist in achieving dynamic change process analysis and quality control.
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Affiliation(s)
- Shufang Wu
- College of Pharmaceutical Engineering of Traditional Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin 301617, China; (S.W.); (J.Y.); (X.L.); (J.X.); (H.D.); (S.B.); (F.L.); (B.Z.); (X.S.); (Z.L.)
- Haihe Laboratory of Modern Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin 301617, China;
| | - Jiaxin Yin
- College of Pharmaceutical Engineering of Traditional Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin 301617, China; (S.W.); (J.Y.); (X.L.); (J.X.); (H.D.); (S.B.); (F.L.); (B.Z.); (X.S.); (Z.L.)
- Haihe Laboratory of Modern Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin 301617, China;
| | - Xuejuan Li
- College of Pharmaceutical Engineering of Traditional Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin 301617, China; (S.W.); (J.Y.); (X.L.); (J.X.); (H.D.); (S.B.); (F.L.); (B.Z.); (X.S.); (Z.L.)
- Haihe Laboratory of Modern Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin 301617, China;
| | - Jingyi Xie
- College of Pharmaceutical Engineering of Traditional Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin 301617, China; (S.W.); (J.Y.); (X.L.); (J.X.); (H.D.); (S.B.); (F.L.); (B.Z.); (X.S.); (Z.L.)
- Haihe Laboratory of Modern Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin 301617, China;
| | - Hui Ding
- College of Pharmaceutical Engineering of Traditional Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin 301617, China; (S.W.); (J.Y.); (X.L.); (J.X.); (H.D.); (S.B.); (F.L.); (B.Z.); (X.S.); (Z.L.)
- Haihe Laboratory of Modern Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin 301617, China;
| | - Lifeng Han
- Haihe Laboratory of Modern Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin 301617, China;
- State Key Laboratory of Component-Based Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin 301617, China
| | - Songtao Bie
- College of Pharmaceutical Engineering of Traditional Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin 301617, China; (S.W.); (J.Y.); (X.L.); (J.X.); (H.D.); (S.B.); (F.L.); (B.Z.); (X.S.); (Z.L.)
- Haihe Laboratory of Modern Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin 301617, China;
- State Key Laboratory of Component-Based Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin 301617, China
| | - Fangyi Li
- College of Pharmaceutical Engineering of Traditional Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin 301617, China; (S.W.); (J.Y.); (X.L.); (J.X.); (H.D.); (S.B.); (F.L.); (B.Z.); (X.S.); (Z.L.)
- Haihe Laboratory of Modern Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin 301617, China;
- State Key Laboratory of Component-Based Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin 301617, China
| | - Beibei Zhu
- College of Pharmaceutical Engineering of Traditional Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin 301617, China; (S.W.); (J.Y.); (X.L.); (J.X.); (H.D.); (S.B.); (F.L.); (B.Z.); (X.S.); (Z.L.)
- Haihe Laboratory of Modern Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin 301617, China;
- State Key Laboratory of Component-Based Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin 301617, China
| | - Liping Kang
- State Key Laboratory Breeding Base of Dao-di Herbs, National Resource Center for Chinese Materia Medica, China Academy of Chinese Medical Sciences, Beijing 100700, China;
| | - Xinbo Song
- College of Pharmaceutical Engineering of Traditional Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin 301617, China; (S.W.); (J.Y.); (X.L.); (J.X.); (H.D.); (S.B.); (F.L.); (B.Z.); (X.S.); (Z.L.)
- Haihe Laboratory of Modern Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin 301617, China;
- State Key Laboratory of Component-Based Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin 301617, China
| | - Heshui Yu
- College of Pharmaceutical Engineering of Traditional Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin 301617, China; (S.W.); (J.Y.); (X.L.); (J.X.); (H.D.); (S.B.); (F.L.); (B.Z.); (X.S.); (Z.L.)
- Haihe Laboratory of Modern Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin 301617, China;
- State Key Laboratory of Component-Based Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin 301617, China
| | - Zheng Li
- College of Pharmaceutical Engineering of Traditional Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin 301617, China; (S.W.); (J.Y.); (X.L.); (J.X.); (H.D.); (S.B.); (F.L.); (B.Z.); (X.S.); (Z.L.)
- Haihe Laboratory of Modern Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin 301617, China;
- State Key Laboratory of Component-Based Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin 301617, China
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Yang YX, Zhang DK, Lu HY, Zhao XL, Yu H. [Change trends and related risk factors of disease burden on mesothelioma in Jiangsu Province from 1990 to 2019]. Zhonghua Lao Dong Wei Sheng Zhi Ye Bing Za Zhi 2023; 41:594-600. [PMID: 37667155 DOI: 10.3760/cma.j.cn121094-20220815-00400] [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: 09/06/2023]
Abstract
Objective: To analyze the change trends and risk factors of mesothelioma disease burden in Jiangsu Province from 1990 to 2019. Methods: In January 2022, using the 2019 Global Burden of Disease Study Data, the Joinpoint regression model was used to analyze the change trends of incidence, mortality, disable-adjusted life years (DALY) and premature mortality of mesothelioma residents in Jiangsu Province from 1990 to 2019, and the attribution level of mesothelioma risk factors was estimated by population attributing fraction. Results: The standardized incidence rates of mesothelioma in Jiangsu Province from 1990 to 2019 ranged from 0.07/10(5) to 0.09/10(5), with an average annual percentage change (AAPC) of -1.1% (t=-13.56, P<0.001). AAPCs in males and females were -0.3% (t=-2.18, P=0.029) and -1.6% (t=-11.39, P<0.001), respectively. The standardized mortality rates of mesothelioma ranged from 0.07/10(5) to 0.09/10(5), the AAPC was -1.1% (t=-12.23, P<0.001), AAPC was -1.6% (t=-14.09, P<0.001) for females, and there was no significant change in males (t=-1.83, P=0.068). The premature mortality was 0.004%-0.006%, the AAPC was -1.0% (t=-4.40, P<0.001), AAPC was -1.7% (t=-13.72, P<0.001) for females, and there was no significant change in males (t=-0.68, P=0.495). The standardized DALY rates ranged from 1.86/10(5) to 2.32/10(5), the AAPC was -0.9% (t=-11.08, P<0.001), AAPC was -1.6% (t=-11.05, P<0.001) for females, and there was no significant change in males (t=-0.95, P=0.343). Both the standardized years of life lost (YLL) rate and the standardized years lived with disability (YLD) rate showed a decreasing trend, and the AAPCs were -0.9% (t=-7.66, P<0.001) and -1.0% (t=-12.88, P<0.001), respectively. The proportion of YLL in DALY was more than 98.5%. Among the risk factors for mesothelioma burden attribution, the AAPC attributed to occupational asbestos exposure of DALY was 1.4% (t=3.43, P=0.001). The AAPC of DALY rate of standardized attribution was -1.7% (t=-12.11, P<0.001) . Conclusion: The overall burden of mesothelioma in Jiangsu Province is decreasing, occupational asbestos exposure is still the main risk factor of mesothelioma in Jiangsu Province, and early diagnosis and treatment should be strengthened.
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Affiliation(s)
- Y X Yang
- Department of Non-communicable Chronic Disease Control and Prevention, Taizhou Center for Disease Control and Prevention, Taizhou 225300, China
| | - D K Zhang
- Department of Non-communicable Chronic Disease Control and Prevention, Taizhou Center for Disease Control and Prevention, Taizhou 225300, China
| | - H Y Lu
- Department of Non-communicable Chronic Disease Control and Prevention, Taizhou Center for Disease Control and Prevention, Taizhou 225300, China
| | - X L Zhao
- Department of Non-communicable Chronic Disease Control and Prevention, Taizhou Center for Disease Control and Prevention, Taizhou 225300, China
| | - H Yu
- Department of Non-communicable Chronic Disease Control, Jiangsu Provincial Center for Disease Control and Prevention, Nanjing 210009, China
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Xu WC, Zhou MM, Ding MK, Yu H, Zhu Z, Xu WG, Zhou JY. [Disease burden and risk factors of chronic respiratory diseases in Jiangsu Province from 1990 to 2019]. Zhonghua Yu Fang Yi Xue Za Zhi 2023; 57:1141-1146. [PMID: 37574303 DOI: 10.3760/cma.j.cn112150-20230208-00089] [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: 08/15/2023]
Abstract
Objective: To analyze the prevalence and the trend of the disease burden of chronic respiratory diseases and relevant risk factors in Jiangsu province from 1990 to 2019 and provide evidence for the prevention and treatment of chronic respiratory diseases. Methods: The data from the 2019 Global Burden of Disease Study (GBD2019) were used to calculate the prevalence rate, mortality rate and disability-adjusted life year (DALY) rate. Software Joinpoint was used to calculate the annual percent change (APC) and average annual percent change (AAPC) of the standardized prevalence rate, standardized mortality rate and standardized DALY rate. The population attributable fractions (PAF) were used to estimate the proportion of chronic respiratory disease caused by different risk factors. Results: In 1990 and 2019, the prevalence rates of chronic respiratory diseases were 4.83% and 5.45%. The mortality rates were 134.91/100 000 and 80.99/100 000 respectively, and the DALY rates were 2 678.52/100 000 and 1 534.31/100 000 respectively. From 1990 to 2019, the age-standardized prevalence rate, mortality rate and DALY rate in Jiangsu showed a significant downward trend (AAPC values were -0.90%, -5.28% and -4.70% respectively, P<0.05). Tobacco use was the leading cause of chronic respiratory diseases, followed by air pollution, occupational exposure, suboptimal temperature and high BMI. Compared with 1990, the proportion of DALYs of chronic respiratory diseases attributable to tobacco use and high BMI increased in 2019. Conclusion: The overall burden of chronic respiratory diseases in Jiangsu shows a downward trend. Prevention and health education should be focused on the population with a smoking history and high BMI. At the same time, environmental management, attention to suboptimal temperature and control of occupational exposure factors should also be adopted as important means to prevent and control chronic respiratory diseases.
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Affiliation(s)
- W C Xu
- Department of Chronic Non-communicable Disease Control and Prevention, Changzhou Cencer for Disease Control and Prevention, Changzhou 213022, China
| | - M M Zhou
- Department of Chronic Non-communicable Disease Control and Prevention, Changzhou Cencer for Disease Control and Prevention, Changzhou 213022, China
| | - M K Ding
- Department of Chronic Non-communicable Disease Control and Prevention, Changzhou Cencer for Disease Control and Prevention, Changzhou 213022, China
| | - H Yu
- Department of Chronic Non-communicable Disease Control and Prevention, Jiangsu Cencer for Disease Control and Prevention, Nanjing 210009, China
| | - Z Zhu
- Department of Chronic Non-communicable Disease Control and Prevention, Jiangsu Cencer for Disease Control and Prevention, Nanjing 210009, China
| | - W G Xu
- Changzhou Cencer for Disease Control and Prevention, Changzhou 213022, China
| | - J Y Zhou
- Department of Chronic Non-communicable Disease Control and Prevention, Jiangsu Cencer for Disease Control and Prevention, Nanjing 210009, China
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Wang CH, Huang ML, Zhuo ZQ, Wang ZX, Chen L, Song YQ, Yu H. [Clinical features and antimicrobial resistance of invasive non-typhoid Salmonella infection in children at Xiamen]. Zhonghua Er Ke Za Zhi 2023; 61:685-689. [PMID: 37528007 DOI: 10.3760/cma.j.cn112140-20230227-00135] [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: 08/03/2023]
Abstract
Objective: To investigate the clinical characteristics, serogroups and antimicrobial resistance of invasive non-typhoid Salmonella infection in children at Xiamen. Methods: Retrospective cohort study. The clinical manifestations, treatment, prognosis, serogroups and antimicrobial resistance of 29 hospitalized children with invasive non-typhoid Salmonella infection confirmed by blood, cerebrospinal fluid, bone marrow and other sterile body fluids or deep pus culture at the Department of Infectious Diseases, the Department of Orthopedics and the Department of General Surgery in Xiamen Children's Hospital from January 2016 to December 2021 were analyzed. According to the clinical diagnosis criteria, the patients were divided into sepsis group and non-sepsis group (bacteremia and local suppurative infection). The inflammatory markers, serogroups distribution and drug resistance were compared between the two groups. Comparison between groups using Mann-Whitney U test and χ2 test. Results: Among the 29 cases, there were 17 males and 12 females, with an onset age of 14 (9, 25) months, and 10 cases (34%) of patients were younger than 1 year old, 15 cases (52%) under 1 to 3 years old, and 4 cases (14%) greater than or equal 3 years old. The onset time of 25 cases (86%) was from April to September. The diseases included 19 cases (66%) septicemia (2 of which were combined with suppurative meningitis), 10 cases (34%) non-sepsis group, including 7 cases bacteremia and 3 cases local suppurative infection (2 cases of osteomyelitis, 1 case of appendicitis with peritonitis). The clinical manifestations were fever in 29 cases (100%), diarrhea and abdominal pain in 18 cases (62%), cough and runny nose in 10 cases (34%). Eighteen cases (62%) were cured and 11 cases (38%) were improved by effective antibiotics treatment. C-reactive protein in sepsis group was significantly higher than that in non-sepsis group (25.2 (16.1, 56.4) vs. 3.4 (0.5, 7.5) mg/L, Z=-3.81, P<0.001).The serogroups of C, B and E were the most prevalent among non-typhoid Salmonella isolates, accounting for 10 cases (34%), 9 cases (31%) and 7 cases (24%) respectively. Antibacterial drug sensitivity test showed that the sensitivity rates of imipenem, ertapenem and piperaciratazobactam were all 100% (31/31), those of ceftazidime, ceftriaxone, and cefepime were 94% (29/31), 94% (29/31) and 97% (30/31) respectively. The drug resistance rates of ampicillin, ampicillin-sulbactam and trimethoprim-sulfamethoxazole were 51% (16/31), 48% (15/31) and 48% (15/31) respectively, those of cefazolin, cefotetan, tobramycin, gentamicin and amikacinwere all 100% (31/31). There were no significant differences in the drug resistance rates of ceftazidime, ceftriaxone, aztreonam, ampicillin-sulbactam, ampicillin, trimethoprim-sulfamethoxazole and ciprofloxacin between the sepsis group and the non-sepsis group (χ2=0.31,0.31,0.00,0.02,0.02,0.02,0.26, all P>0.05). Conclusions: Invasive non-typhoid Salmonella infection in children at Xiamen mainly occurred in infants younger than 3 years old.The main clinical manifestations are fever, abdominal pain and diarrhea. C-reactive protein can be served as the laboratory indicators for indicating sepsis. The third generation of cephalosporins is recommended as the first choice for treatment.
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Affiliation(s)
- C H Wang
- Department of Infectious Diseases, Xiamen Hospital (Xiamen Children's Hospital), Children's Hospital of Fudan University, Xiamen 361006, China
| | - M L Huang
- Department of Clinical Medical Labortaory,Xiamen Hospital (Xiamen Children's Hospital), Children's Hospital of Fudan University, Xiamen 361006, China
| | - Z Q Zhuo
- Department of Infectious Diseases, Xiamen Hospital (Xiamen Children's Hospital), Children's Hospital of Fudan University, Xiamen 361006, China
| | - Z X Wang
- Department of Infectious Diseases, Children's Hospital of Fudan University, National Children's Medical Center, Shanghai 201102, China
| | - L Chen
- Department of Clinical Medical Labortaory,Xiamen Hospital (Xiamen Children's Hospital), Children's Hospital of Fudan University, Xiamen 361006, China
| | - Y Q Song
- Department of Infectious Diseases, Xiamen Hospital (Xiamen Children's Hospital), Children's Hospital of Fudan University, Xiamen 361006, China
| | - H Yu
- Department of Infectious Diseases, Children's Hospital of Fudan University, National Children's Medical Center, Shanghai 201102, China
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Yin J, Guo W, Li X, Ding H, Han L, Yang X, Zhu L, Li F, Bie S, Song X, Yu H, Li Z. Extensive evaluation of plasma metabolic sample preparation process based on liquid chromatography-mass spectrometry and its application in the in vivo metabolism of Shuang-Huang-Lian powder injection. J Chromatogr B Analyt Technol Biomed Life Sci 2023; 1228:123808. [PMID: 37453388 DOI: 10.1016/j.jchromb.2023.123808] [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: 03/11/2023] [Revised: 06/04/2023] [Accepted: 06/19/2023] [Indexed: 07/18/2023]
Abstract
Shuang-Huang-Lian powder injection (SHLPI) is a natural drug injection made of honeysuckle, scutellaria baicalensis and forsythia suspensa. It has the characteristics of complex chemical composition and difficult metabolism research in vivo. LC-MS platform has been proven to be an important analytical technology in plasma metabolomics. Unfortunately, the lack of an effective sample preparation strategy before analysis often significantly impacts experimental results. In this work, twenty-one extraction protocols including eight protein precipitation (PPT), eight liquid-liquid extractions (LLE), four solid-phase extractions (SPE), and one ultrafiltration (U) were simultaneously evaluated using plasma metabolism of SHLPI in vivo. In addition, a strategy of "feature ion extraction of the multi-component metabolic platform of traditional Chinese medicine" (FMM strategy) was proposed for the in-depth characterization of metabolites after intravenous injection of SHLPI in rats. The results showed that the LLE-3 protocol (Pentanol:Tetrahydrofuran:H2O, 1:4:35, v:v:v) was the most effective strategy in the in vivo metabolic detection of SHLPI. Furthermore, we used the FMM strategy to elaborate the in vivo metabolic pathways of six representative substances in SHLPI components. This research was completed by ion migration quadrupole time of flight mass spectrometer combined with ultra high performance liquid chromatography (UPLC/Vion™-IMS-QTof-MS) and UNIFI™ metabolic platform. The results showed that 114 metabolites were identified or preliminarily identified in rat plasma. This work provides relevant data and information for further research on the pharmacodynamic substances and in vivo mechanisms of SHLPI. Meanwhile, it also proves that LLE-3 and FMM strategies could achieve the in-depth characterization of complex natural drug metabolites related to Shuang-Huang-Lian in vivo.
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Affiliation(s)
- Jiaxin Yin
- College of Pharmaceutical Engineering of Traditional Chinese Medicine, Tianjin University of Traditional Chinese Medicine, No. 10 Poyanghu Road, West Tuanbo New Town, Jinghai District, Tianjin 301617, PR China
| | - Wen Guo
- College of Pharmaceutical Engineering of Traditional Chinese Medicine, Tianjin University of Traditional Chinese Medicine, No. 10 Poyanghu Road, West Tuanbo New Town, Jinghai District, Tianjin 301617, PR China
| | - Xuejuan Li
- College of Pharmaceutical Engineering of Traditional Chinese Medicine, Tianjin University of Traditional Chinese Medicine, No. 10 Poyanghu Road, West Tuanbo New Town, Jinghai District, Tianjin 301617, PR China
| | - Hui Ding
- College of Pharmaceutical Engineering of Traditional Chinese Medicine, Tianjin University of Traditional Chinese Medicine, No. 10 Poyanghu Road, West Tuanbo New Town, Jinghai District, Tianjin 301617, PR China
| | - Lifeng Han
- State Key Laboratory of Component-based Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin 301617, PR China; Haihe Laboratory of Modern Chinese Medicine, Tianjin 301617, PR China
| | - Xiangdong Yang
- First Teaching Hospital of Tianjin University of Traditional Chinese Medicine, Tianjin 300193, PR China
| | - Limin Zhu
- First Teaching Hospital of Tianjin University of Traditional Chinese Medicine, Tianjin 300193, PR China
| | - Fangyi Li
- College of Pharmaceutical Engineering of Traditional Chinese Medicine, Tianjin University of Traditional Chinese Medicine, No. 10 Poyanghu Road, West Tuanbo New Town, Jinghai District, Tianjin 301617, PR China; State Key Laboratory of Component-based Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin 301617, PR China; Haihe Laboratory of Modern Chinese Medicine, Tianjin 301617, PR China
| | - Songtao Bie
- College of Pharmaceutical Engineering of Traditional Chinese Medicine, Tianjin University of Traditional Chinese Medicine, No. 10 Poyanghu Road, West Tuanbo New Town, Jinghai District, Tianjin 301617, PR China; State Key Laboratory of Component-based Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin 301617, PR China; Haihe Laboratory of Modern Chinese Medicine, Tianjin 301617, PR China
| | - Xinbo Song
- College of Pharmaceutical Engineering of Traditional Chinese Medicine, Tianjin University of Traditional Chinese Medicine, No. 10 Poyanghu Road, West Tuanbo New Town, Jinghai District, Tianjin 301617, PR China; State Key Laboratory of Component-based Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin 301617, PR China; Haihe Laboratory of Modern Chinese Medicine, Tianjin 301617, PR China
| | - Heshui Yu
- College of Pharmaceutical Engineering of Traditional Chinese Medicine, Tianjin University of Traditional Chinese Medicine, No. 10 Poyanghu Road, West Tuanbo New Town, Jinghai District, Tianjin 301617, PR China; State Key Laboratory of Component-based Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin 301617, PR China; Haihe Laboratory of Modern Chinese Medicine, Tianjin 301617, PR China.
| | - Zheng Li
- College of Pharmaceutical Engineering of Traditional Chinese Medicine, Tianjin University of Traditional Chinese Medicine, No. 10 Poyanghu Road, West Tuanbo New Town, Jinghai District, Tianjin 301617, PR China; State Key Laboratory of Component-based Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin 301617, PR China; Haihe Laboratory of Modern Chinese Medicine, Tianjin 301617, PR China.
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Pofelski A, Deng S, Yu H, Park TJ, Jia H, Manna S, Chan MKY, Sankaranarayanan SKR, Ramanathan S, Zhu Y. Dopant Mapping of Partially Hydrogenated Vanadium Dioxide using the Energy Loss Near Edge Structure Technique. Microsc Microanal 2023; 29:1667-1668. [PMID: 37613910 DOI: 10.1093/micmic/ozad067.858] [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] [Subscribe] [Scholar Register] [Indexed: 08/25/2023]
Affiliation(s)
- A Pofelski
- Condensed Matter Physics and Materials Science Department, Brookhaven National Laboratory, Upton, NY, USA
| | - S Deng
- School of Materials Engineering, Purdue University, West Lafayette, IN, USA
| | - H Yu
- School of Materials Engineering, Purdue University, West Lafayette, IN, USA
| | - T J Park
- School of Materials Engineering, Purdue University, West Lafayette, IN, USA
| | - H Jia
- Center for Nanoscale Materials, Argonne National Laboratory, Lemont, IL, USA
| | - S Manna
- Center for Nanoscale Materials, Argonne National Laboratory, Lemont, IL, USA
- Department of Mechanical and Industrial Engineering, University of Illinois, Chicago, IL, USA
| | - M K Y Chan
- Center for Nanoscale Materials, Argonne National Laboratory, Lemont, IL, USA
| | - S K Rs Sankaranarayanan
- Center for Nanoscale Materials, Argonne National Laboratory, Lemont, IL, USA
- Department of Mechanical and Industrial Engineering, University of Illinois, Chicago, IL, USA
| | - S Ramanathan
- School of Materials Engineering, Purdue University, West Lafayette, IN, USA
- Department of Electrical and Computer Engineering, Rutgers, The State University of New Jersey, Piscataway, NJ, USA
| | - Y Zhu
- Condensed Matter Physics and Materials Science Department, Brookhaven National Laboratory, Upton, NY, USA
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Chen YJ, Qin Y, Yu H, Zhu Z, Shen C, Lu Y, Cheng TT, Zhang N, Gu SJ, Zhou JY, Wu M, Su J. [A prospective cohort study of long-term fasting blood glucose variability and risk of mortality in patients with type 2 diabetes]. Zhonghua Liu Xing Bing Xue Za Zhi 2023; 44:1099-1105. [PMID: 37482713 DOI: 10.3760/cma.j.cn112338-20221226-01084] [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: 07/25/2023]
Abstract
Objective: To investigate the association between long-term fasting blood glucose (FPG) variability and all-cause mortality in patients with type 2 diabetes. Methods: A total of 7 174 type 2 diabetic patients included in National Basic Public Health Service Program in Changshu of Jiangsu Province were recruited as participants. Long-term glucose variability was assessed using standard deviation (SD), coefficient of variation (CV), average real variability (ARV), and variability independent of the mean (VIM) across FPG measurements at the more than three visits. Death information were mainly obtained from the death registry system in Jiangsu. Then Cox proportional hazards regression models were used to estimate the associations of four variability indicators and all-cause mortality's hazard ratios (HRs) and their 95%CIs. Results: Among 55 058.50 person-years of the follow-up, the mean follow-up time was 7.67 years, and 898 deaths occurred during the follow-up period. After adjustment, compared with T1 group, the Cox regression model showed that HRs of T3 group in SD, CV, ARV and VIM were 1.24 (95%CI: 1.03-1.49), 1.20 (95%CI: 1.01-1.43), 1.28 (95%CI: 1.07-1.55) and 1.20 (95%CI:1.01-1.41), respectively. HRs of per 1 SD higher SD, CV, ARV and VIM were 1.13 (95%CI: 1.06-1.21), 1.08 (95%CI: 1.01-1.15), 1.05 (95%CI: 1.00-1.12) and 1.09 (95%CI: 1.02-1.16) for all-cause mortality, respectively. In the stratified analysis, age, gender, hypoglycemic agent and insulin uses had no effect on the above associations (all P for interaction >0.05). Conclusion: Long-term FPG glycemic variability was positively associated with the risk of all-cause mortality in type 2 diabetes patients.
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Affiliation(s)
- Y J Chen
- Department of Non-communicable Chronic Disease Prevention, Nanjing Municipal Center for Disease Control and Prevention, Nanjing 210003, China
| | - Y Qin
- Department of Non-communicable Chronic Disease Control, Jiangsu Provincial Center for Disease Control and Prevention, Nanjing 210009, China
| | - H Yu
- Department of Non-communicable Chronic Disease Control, Jiangsu Provincial Center for Disease Control and Prevention, Nanjing 210009, China
| | - Z Zhu
- Department of Non-communicable Chronic Disease Control, Jiangsu Provincial Center for Disease Control and Prevention, Nanjing 210009, China
| | - C Shen
- Department of Epidemiology and Biostatistics, School of Public Health, Nanjing Medical University, Nanjing 211166, China
| | - Y Lu
- Department of Non-communicable Chronic Disease Prevention, Suzhou Prefectural Center for Disease Control and Prevention, Suzhou 215004, China
| | - T T Cheng
- Department of Infectious Disease Control Division, Suzhou National New & Hi-Tech Industrial Development Zone (Huqiu District) Center for Disease Control and Prevention, Suzhou 215163, China
| | - N Zhang
- Changshu County Center for Disease Control and Prevention, Changshu 215500, China
| | - S J Gu
- Department of Non-communicable Chronic Disease Prevention, Changshu County Center for Disease Control and Prevention, Changshu 215500, China
| | - J Y Zhou
- Department of Non-communicable Chronic Disease Control, Jiangsu Provincial Center for Disease Control and Prevention, Nanjing 210009, China
| | - M Wu
- Department of Non-communicable Chronic Disease Control, Jiangsu Provincial Center for Disease Control and Prevention, Nanjing 210009, China
| | - J Su
- Department of Non-communicable Chronic Disease Control, Jiangsu Provincial Center for Disease Control and Prevention, Nanjing 210009, China
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Yu H, Yang RT, Wang SY, Wu JH, Wang MY, Qin XY, Wu T, Chen DF, Wu YQ, Hu YH. [Metformin use and risk of ischemic stroke in patients with type 2 diabetes: A cohort study]. Beijing Da Xue Xue Bao Yi Xue Ban 2023; 55:456-464. [PMID: 37291921] [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: 06/10/2023]
Abstract
OBJECTIVE To explore the association between the use of metformin and the risk of ischemic stroke in patients with type 2 diabetes. METHODS A prospective cohort study was designed from the Fangshan family cohort in Beijing. According to metformin use at baseline, 2 625 patients with type 2 diabetes in Fangshan, Beijing were divided into metformin group or non-metformin group and the incidence of ischemic stroke between the different groups during follow-up was estimated and compared by Cox proportional hazard regression model. The participants with metformin were first compared with all the parti-cipants who did not use metformin, and then were further compared with those who did not use hypoglycemic agents and those who used other hypoglycemic agents. RESULTS The patients with type 2 diabetes were with an average age of (59.5±8.7) years, and 41.9% of them were male. The median follow-up time was 4.5 years. A total of 84 patients developed ischemic stroke during follow-up, with a crude incidence of 6.4 (95%CI: 5.0-7.7) per 1 000 person-years. Among all the participants, 1 149 (43.8%) took metformin, 1 476 (56.2%) were metformin non-users, including 593 (22.6%) used other hypoglycemic agents, and 883 (33.6%) did not use any hypoglycemic agents. Compared with metformin non-users, the Hazard ratio (HR) for ischemic stroke in metformin users was 0.58 (95%CI: 0.36-0.93; P = 0.024). Compared with other hypoglycemic agents, HR was 0.48 (95%CI: 0.28-0.84; P < 0.01); Compared with the group without hypoglycemic agents, HR was 0.65 (95%CI: 0.37-1.13; P=0.13). The association between metformin and ischemic stroke was statistically significant in the patients ≥ 60 years old compared with all the metformin non-users and those who used other hypoglycemic agents (HR: 0.48, 95%CI: 0.25-0.92; P < 0.05). Metformin use was associated with a lower incidence of ischemic stroke in the patients with good glycemic control (0.32, 95%CI: 0.13-0.77; P < 0.05). In the patients with poor glycemic control, and the association was not statistically significant (HR: 0.97, 95%CI: 0.53-1.79; P>0.05). There was an interaction between glycemic control and metformin use on incidence of ischemic stroke (Pinteraction < 0.05). The results of the sensitivity analysis were consistent with the results in the main analysis. CONCLUSION Among patients with type 2 diabetic in rural areas of northern China, metformin use was associated with lower incidence of ischemic stroke, especially in patients older than 60 years. There was an interaction between glycemic control and metformin use in the incidence of ischemic stroke.
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Affiliation(s)
- H Yu
- Department of Epidemiology and Biostatistics, Peking University School of Public Health; Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing 100191, China
| | - R T Yang
- Department of Epidemiology and Biostatistics, Peking University School of Public Health; Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing 100191, China
| | - S Y Wang
- Department of Epidemiology and Biostatistics, Peking University School of Public Health; Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing 100191, China
| | - J H Wu
- Department of Epidemiology and Biostatistics, Peking University School of Public Health; Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing 100191, China
| | - M Y Wang
- Department of Epidemiology and Biostatistics, Peking University School of Public Health; Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing 100191, China
| | - X Y Qin
- Department of Epidemiology and Biostatistics, Peking University School of Public Health; Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing 100191, China
| | - T Wu
- Department of Epidemiology and Biostatistics, Peking University School of Public Health; Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing 100191, China
| | - D F Chen
- Department of Epidemiology and Biostatistics, Peking University School of Public Health; Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing 100191, China
| | - Y Q Wu
- Department of Epidemiology and Biostatistics, Peking University School of Public Health; Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing 100191, China
| | - Y H Hu
- Department of Epidemiology and Biostatistics, Peking University School of Public Health; Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing 100191, China
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Yang MS, Fan XK, Su J, Yu H, Lu Y, Hua YJ, Pei P, Lyu J, Tao R, Zhou JY, Wu M. [Incidence of chronic obstructive pulmonary disease and risk factors in the Suzhou cohort]. Zhonghua Liu Xing Bing Xue Za Zhi 2023; 44:868-876. [PMID: 37380406 DOI: 10.3760/cma.j.cn112338-20221202-01033] [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: 06/30/2023]
Abstract
Objective: To understand the incidence of chronic obstructive pulmonary disease (COPD) in the Suzhou cohort, and explore the risk factors for the development of COPD in Suzhou, and provide a scientific basis for COPD prevention. Methods: This study was based on the China Kadoorie Biobank project in Wuzhong District, Suzhou. After excluding individuals with airflow obstruction and self-reported chronic bronchitis, emphysema, or pulmonary heart disease at baseline, 45 484 individuals were finally included in the analysis. Cox proportional risk models were used to analyze risk factors of COPD and calculate hazard ratios and 95% confidence interval (CI) in the Suzhou cohort. The effect modifications of smoking on the association between other risk factors and COPD were evaluated. Results: Complete follow-up was available through December 31, 2017. Participants were followed up for a median of 11.12 years, and 524 individuals were diagnosed with COPD during the follow-up period; the incidence was 105.54 per 100 000 person-years. Multivariate Cox proportional risk regression models showed that age (HR=3.78, 95%CI:3.32-4.30), former smoking (HR=2.00, 95%CI:1.24-3.22), current smoking (<10 cigarettes/day, HR=2.14, 95%CI:1.36-3.35;≥10 cigarettes/day, HR=2.69, 95%CI:1.60-4.54), history of respiratory disease (HR=2.08, 95%CI:1.33-3.26), daily sleep duration ≥10 hours (HR=1.41, 95%CI:1.02-1.95) were associated with increased risk of COPD. However, education level of primary school and above (primary or junior high school, HR=0.65, 95%CI:0.52-0.81; high school and above, HR=0.54, 95%CI:0.33-0.87), consuming fresh fruit daily (HR=0.59, 95%CI:0.42-0.83) and consuming spicy food weekly (HR=0.71, 95%CI:0.53-0.94) were associated with reduced risk of COPD. Conclusions: The incidence of COPD is low in Suzhou. Older age, smoking, history of respiratory disease, and long sleep duration were risk factors for the development of COPD in the Suzhou cohort.
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Affiliation(s)
- M S Yang
- Department of Epidemiology and Health Statistics, School of Public Health, Southeast University, Nanjing 210009, China
| | - X K Fan
- Department of Non-communicable Chronic Disease Control and Prevention, Jiangsu Provincial Center for Disease Control and Prevention, Nanjing 210009, China
| | - J Su
- Department of Non-communicable Chronic Disease Control and Prevention, Jiangsu Provincial Center for Disease Control and Prevention, Nanjing 210009, China
| | - H Yu
- Department of Non-communicable Chronic Disease Control and Prevention, Jiangsu Provincial Center for Disease Control and Prevention, Nanjing 210009, China
| | - Y Lu
- Department of Non-communicable Chronic Disease Control and Prevention, Suzhou Center for Disease Control and Prevention, Suzhou 215004, China
| | - Y J Hua
- Department of Non-communicable Chronic Disease Control and Prevention, Suzhou Center for Disease Control and Prevention, Suzhou 215004, China
| | - P Pei
- Peking University Center for Public Health and Epidemic Preparedness & Response, Beijing 100191, China
| | - J Lyu
- Peking University Center for Public Health and Epidemic Preparedness & Response, Beijing 100191, China Department of Epidemiology and Health Statistics, School of Public Health, Peking University, Beijing 100191, China
| | - R Tao
- Department of Non-communicable Chronic Disease Control and Prevention, Jiangsu Provincial Center for Disease Control and Prevention, Nanjing 210009, China
| | - J Y Zhou
- Department of Non-communicable Chronic Disease Control and Prevention, Jiangsu Provincial Center for Disease Control and Prevention, Nanjing 210009, China
| | - M Wu
- Department of Epidemiology and Health Statistics, School of Public Health, Southeast University, Nanjing 210009, China Department of Non-communicable Chronic Disease Control and Prevention, Jiangsu Provincial Center for Disease Control and Prevention, Nanjing 210009, China
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Abratenko P, Aduszkiewicz A, Akbar F, Pons MA, Asaadi J, Aslin M, Babicz M, Badgett WF, Bagby LF, Baibussinov B, Behera B, Bellini V, Beltramello O, Benocci R, Berger J, Berkman S, Bertolucci S, Bertoni R, Betancourt M, Bettini M, Biagi S, Biery K, Bitter O, Bonesini M, Boone T, Bottino B, Braggiotti A, Brailsford D, Bremer J, Brice SJ, Brio V, Brizzolari C, Brown J, Budd HS, Calaon F, Campani A, Carber D, Carneiro M, Terrazas IC, Carranza H, Casazza D, Castellani L, Castro A, Centro S, Cerati G, Chalifour M, Chambouvet P, Chatterjee A, Cherdack D, Cherubini S, Chithirasreemadam N, Cicerchia M, Cicero V, Coan T, Cocco AG, Convery MR, Copello S, Cristaldo E, Dange AA, de Icaza Astiz I, De Roeck A, Di Domizio S, Di Noto L, Di Stefano C, Di Ferdinando D, Diwan M, Dolan S, Domine L, Donati S, Doubnik R, Drielsma F, Dyer J, Dytman S, Fabre C, Fabris F, Falcone A, Farnese C, Fava A, Ferguson H, Ferrari A, Ferraro F, Gallice N, Garcia FG, Geynisman M, Giarin M, Gibin D, Gigli SG, Gioiosa A, Gu W, Guerzoni M, Guglielmi A, Gurung G, Hahn S, Hardin K, Hausner H, Heggestuen A, Hilgenberg C, Hogan M, Howard B, Howell R, Hrivnak J, Iliescu M, Ingratta G, James C, Jang W, Jung M, Jwa YJ, Kashur L, Ketchum W, Kim JS, Koh DH, Kose U, Larkin J, Laurenti G, Lukhanin G, Marchini S, Marshall CM, Martynenko S, Mauri N, Mazzacane A, McFarland KS, Méndez DP, Menegolli A, Meng G, Miranda OG, Mladenov D, Mogan A, Moggi N, Montagna E, Montanari C, Montanari A, Mooney M, Moreno-Granados G, Mueller J, Naples D, Nebot-Guinot M, Nessi M, Nichols T, Nicoletto M, Norris B, Palestini S, Pallavicini M, Paolone V, Papaleo R, Pasqualini L, Patrizii L, Peghin R, Petrillo G, Petta C, Pia V, Pietropaolo F, Poirot J, Poppi F, Pozzato M, Prata MC, Prosser A, Putnam G, Qian X, Rampazzo G, Rappoldi A, Raselli GL, Rechenmacher R, Resnati F, Ricci AM, Riccobene G, Rice L, Richards E, Rigamonti A, Rosenberg M, Rossella M, Rubbia C, Sala P, Sapienza P, Savage G, Scaramelli A, Scarpelli A, Schmitz D, Schukraft A, Sergiampietri F, Sirri G, Smedley JS, Soha AK, Spanu M, Stanco L, Stewart J, Suarez NB, Sutera C, Tanaka HA, Tenti M, Terao K, Terranova F, Togo V, Torretta D, Torti M, Tortorici F, Tosi N, Tsai YT, Tufanli S, Turcato M, Usher T, Varanini F, Ventura S, Vercellati F, Vicenzi M, Vignoli C, Viren B, Warner D, Williams Z, Wilson RJ, Wilson P, Wolfs J, Wongjirad T, Wood A, Worcester E, Worcester M, Wospakrik M, Yu H, Yu J, Zani A, Zatti PG, Zennamo J, Zettlemoyer JC, Zhang C, Zucchelli S, Zuckerbrot M. ICARUS at the Fermilab Short-Baseline Neutrino program: initial operation. Eur Phys J C Part Fields 2023; 83:467. [PMID: 37303462 PMCID: PMC10239613 DOI: 10.1140/epjc/s10052-023-11610-y] [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] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/23/2023] [Accepted: 05/12/2023] [Indexed: 06/13/2023]
Abstract
The ICARUS collaboration employed the 760-ton T600 detector in a successful 3-year physics run at the underground LNGS laboratory, performing a sensitive search for LSND-like anomalous ν e appearance in the CERN Neutrino to Gran Sasso beam, which contributed to the constraints on the allowed neutrino oscillation parameters to a narrow region around 1 eV2 . After a significant overhaul at CERN, the T600 detector has been installed at Fermilab. In 2020 the cryogenic commissioning began with detector cool down, liquid argon filling and recirculation. ICARUS then started its operations collecting the first neutrino events from the booster neutrino beam (BNB) and the Neutrinos at the Main Injector (NuMI) beam off-axis, which were used to test the ICARUS event selection, reconstruction and analysis algorithms. ICARUS successfully completed its commissioning phase in June 2022. The first goal of the ICARUS data taking will be a study to either confirm or refute the claim by Neutrino-4 short-baseline reactor experiment. ICARUS will also perform measurement of neutrino cross sections with the NuMI beam and several Beyond Standard Model searches. After the first year of operations, ICARUS will search for evidence of sterile neutrinos jointly with the Short-Baseline Near Detector, within the Short-Baseline Neutrino program. In this paper, the main activities carried out during the overhauling and installation phases are highlighted. Preliminary technical results from the ICARUS commissioning data with the BNB and NuMI beams are presented both in terms of performance of all ICARUS subsystems and of capability to select and reconstruct neutrino events.
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Affiliation(s)
| | | | - F. Akbar
- University of Rochester, Rochester, NY 14627 USA
| | - M. Artero Pons
- INFN Sezione di Padova and University of Padova, Padua, Italy
| | - J. Asaadi
- University of Texas at Arlington, Arlington, TX 76019 USA
| | - M. Aslin
- Fermi National Accelerator Laboratory, Batavia, IL 60510 USA
- Present Address: University of Wisconsin, Madison, USA
| | - M. Babicz
- CERN, European Organization for Nuclear Research, 1211 Geneva 23, Switzerland
- INP-Polish Acad. Sci, Kraków, Poland
- Present Address: University of Zurich, Zurich, Switzerland
| | - W. F. Badgett
- Fermi National Accelerator Laboratory, Batavia, IL 60510 USA
| | - L. F. Bagby
- Fermi National Accelerator Laboratory, Batavia, IL 60510 USA
| | - B. Baibussinov
- INFN Sezione di Padova and University of Padova, Padua, Italy
| | - B. Behera
- Colorado State University, Fort Collins, CO 80523 USA
| | - V. Bellini
- INFN Sezione di Catania and University of Catania, Catania, Italy
| | - O. Beltramello
- CERN, European Organization for Nuclear Research, 1211 Geneva 23, Switzerland
| | - R. Benocci
- INFN Sezione di Milano Bicocca and University of Milano Bicocca, Milan, Italy
| | - J. Berger
- Colorado State University, Fort Collins, CO 80523 USA
| | - S. Berkman
- Fermi National Accelerator Laboratory, Batavia, IL 60510 USA
| | - S. Bertolucci
- INFN Sezione di Bologna and University of Bologna, Bologna, Italy
| | - R. Bertoni
- INFN Sezione di Milano Bicocca and University of Milano Bicocca, Milan, Italy
| | - M. Betancourt
- Fermi National Accelerator Laboratory, Batavia, IL 60510 USA
| | - M. Bettini
- INFN Sezione di Padova and University of Padova, Padua, Italy
| | | | - K. Biery
- Fermi National Accelerator Laboratory, Batavia, IL 60510 USA
| | - O. Bitter
- Fermi National Accelerator Laboratory, Batavia, IL 60510 USA
- Present Address: Northwestern University, Evanston, USA
| | - M. Bonesini
- INFN Sezione di Milano Bicocca and University of Milano Bicocca, Milan, Italy
| | - T. Boone
- Colorado State University, Fort Collins, CO 80523 USA
| | - B. Bottino
- INFN Sezione di Genova and University of Genova, Genoa, Italy
| | - A. Braggiotti
- INFN Sezione di Padova and University of Padova, Padua, Italy
- Istituto di Neuroscienze, CNR, Padua, Italy
| | - D. Brailsford
- SBND Collaboration, Lancaster University, Lancaster, UK
| | - J. Bremer
- CERN, European Organization for Nuclear Research, 1211 Geneva 23, Switzerland
| | - S. J. Brice
- Fermi National Accelerator Laboratory, Batavia, IL 60510 USA
| | - V. Brio
- INFN Sezione di Catania and University of Catania, Catania, Italy
| | - C. Brizzolari
- INFN Sezione di Milano Bicocca and University of Milano Bicocca, Milan, Italy
| | - J. Brown
- Fermi National Accelerator Laboratory, Batavia, IL 60510 USA
| | - H. S. Budd
- University of Rochester, Rochester, NY 14627 USA
| | - F. Calaon
- INFN Sezione di Padova and University of Padova, Padua, Italy
| | - A. Campani
- INFN Sezione di Genova and University of Genova, Genoa, Italy
| | - D. Carber
- Colorado State University, Fort Collins, CO 80523 USA
| | - M. Carneiro
- Brookhaven National Laboratory, Upton, NY 11973 USA
| | | | - H. Carranza
- University of Texas at Arlington, Arlington, TX 76019 USA
| | - D. Casazza
- INFN Sezione di Genova and University of Genova, Genoa, Italy
| | - L. Castellani
- INFN Sezione di Padova and University of Padova, Padua, Italy
| | - A. Castro
- Centro de Investigacion y de Estudios Avanzados del IPN (Cinvestav), Mexico City, Mexico
| | - S. Centro
- INFN Sezione di Padova and University of Padova, Padua, Italy
| | - G. Cerati
- Fermi National Accelerator Laboratory, Batavia, IL 60510 USA
| | - M. Chalifour
- CERN, European Organization for Nuclear Research, 1211 Geneva 23, Switzerland
| | - P. Chambouvet
- CERN, European Organization for Nuclear Research, 1211 Geneva 23, Switzerland
| | | | - D. Cherdack
- University of Houston, Houston, TX 77204 USA
| | | | | | - M. Cicerchia
- INFN Sezione di Padova and University of Padova, Padua, Italy
| | - V. Cicero
- INFN Sezione di Bologna and University of Bologna, Bologna, Italy
| | - T. Coan
- Southern Methodist University, Dallas, TX 75275 USA
| | | | - M. R. Convery
- SLAC National Acceleratory Laboratory, Menlo Park, CA 94025 USA
| | - S. Copello
- INFN Sezione di Pavia and University of Pavia, Pavia, Italy
| | - E. Cristaldo
- SBND Collaboration, Universidad Nacional de Asuncion, San Lorenzo, Paraguay
| | - A. A. Dange
- University of Texas at Arlington, Arlington, TX 76019 USA
| | | | - A. De Roeck
- CERN, European Organization for Nuclear Research, 1211 Geneva 23, Switzerland
| | - S. Di Domizio
- INFN Sezione di Genova and University of Genova, Genoa, Italy
| | - L. Di Noto
- INFN Sezione di Genova and University of Genova, Genoa, Italy
| | | | - D. Di Ferdinando
- INFN Sezione di Bologna and University of Bologna, Bologna, Italy
| | - M. Diwan
- Brookhaven National Laboratory, Upton, NY 11973 USA
| | - S. Dolan
- CERN, European Organization for Nuclear Research, 1211 Geneva 23, Switzerland
| | - L. Domine
- SLAC National Acceleratory Laboratory, Menlo Park, CA 94025 USA
| | | | - R. Doubnik
- Fermi National Accelerator Laboratory, Batavia, IL 60510 USA
| | - F. Drielsma
- SLAC National Acceleratory Laboratory, Menlo Park, CA 94025 USA
| | - J. Dyer
- Colorado State University, Fort Collins, CO 80523 USA
| | - S. Dytman
- University of Pittsburgh, Pittsburgh, PA 15260 USA
| | - C. Fabre
- CERN, European Organization for Nuclear Research, 1211 Geneva 23, Switzerland
| | - F. Fabris
- INFN Sezione di Padova and University of Padova, Padua, Italy
| | - A. Falcone
- INFN Sezione di Milano Bicocca and University of Milano Bicocca, Milan, Italy
| | - C. Farnese
- INFN Sezione di Padova and University of Padova, Padua, Italy
| | - A. Fava
- Fermi National Accelerator Laboratory, Batavia, IL 60510 USA
| | - H. Ferguson
- Fermi National Accelerator Laboratory, Batavia, IL 60510 USA
| | | | - F. Ferraro
- INFN Sezione di Genova and University of Genova, Genoa, Italy
| | | | - F. G. Garcia
- SLAC National Acceleratory Laboratory, Menlo Park, CA 94025 USA
| | - M. Geynisman
- Fermi National Accelerator Laboratory, Batavia, IL 60510 USA
| | - M. Giarin
- INFN Sezione di Padova and University of Padova, Padua, Italy
| | - D. Gibin
- INFN Sezione di Padova and University of Padova, Padua, Italy
| | - S. G. Gigli
- INFN Sezione di Pavia and University of Pavia, Pavia, Italy
| | | | - W. Gu
- Brookhaven National Laboratory, Upton, NY 11973 USA
| | - M. Guerzoni
- INFN Sezione di Bologna and University of Bologna, Bologna, Italy
| | - A. Guglielmi
- INFN Sezione di Padova and University of Padova, Padua, Italy
| | - G. Gurung
- University of Texas at Arlington, Arlington, TX 76019 USA
| | - S. Hahn
- Fermi National Accelerator Laboratory, Batavia, IL 60510 USA
| | - K. Hardin
- Fermi National Accelerator Laboratory, Batavia, IL 60510 USA
| | - H. Hausner
- Fermi National Accelerator Laboratory, Batavia, IL 60510 USA
| | - A. Heggestuen
- Colorado State University, Fort Collins, CO 80523 USA
| | - C. Hilgenberg
- Colorado State University, Fort Collins, CO 80523 USA
- Present Address: University of Minnesota, Minneapolis, USA
| | - M. Hogan
- Colorado State University, Fort Collins, CO 80523 USA
| | - B. Howard
- Fermi National Accelerator Laboratory, Batavia, IL 60510 USA
| | - R. Howell
- University of Rochester, Rochester, NY 14627 USA
| | - J. Hrivnak
- CERN, European Organization for Nuclear Research, 1211 Geneva 23, Switzerland
| | - M. Iliescu
- INFN Sezione di Bologna and University of Bologna, Bologna, Italy
- Present Address: INFN-LNF, Frascati, Italy
| | - G. Ingratta
- INFN Sezione di Bologna and University of Bologna, Bologna, Italy
| | - C. James
- Fermi National Accelerator Laboratory, Batavia, IL 60510 USA
| | - W. Jang
- University of Texas at Arlington, Arlington, TX 76019 USA
| | - M. Jung
- University of Chicago, Chicago, IL 60637 USA
- SBND Collaboration, Batavia, USA
| | - Y.-J. Jwa
- SLAC National Acceleratory Laboratory, Menlo Park, CA 94025 USA
| | - L. Kashur
- Colorado State University, Fort Collins, CO 80523 USA
| | - W. Ketchum
- Fermi National Accelerator Laboratory, Batavia, IL 60510 USA
| | - J. S. Kim
- University of Rochester, Rochester, NY 14627 USA
| | - D.-H. Koh
- SLAC National Acceleratory Laboratory, Menlo Park, CA 94025 USA
| | - U. Kose
- CERN, European Organization for Nuclear Research, 1211 Geneva 23, Switzerland
- Present Address: ETH Zurich, Zurich, Switzerland
| | - J. Larkin
- Brookhaven National Laboratory, Upton, NY 11973 USA
| | - G. Laurenti
- INFN Sezione di Bologna and University of Bologna, Bologna, Italy
| | - G. Lukhanin
- Fermi National Accelerator Laboratory, Batavia, IL 60510 USA
| | - S. Marchini
- INFN Sezione di Padova and University of Padova, Padua, Italy
| | | | | | - N. Mauri
- INFN Sezione di Bologna and University of Bologna, Bologna, Italy
| | - A. Mazzacane
- Fermi National Accelerator Laboratory, Batavia, IL 60510 USA
| | | | - D. P. Méndez
- Brookhaven National Laboratory, Upton, NY 11973 USA
| | - A. Menegolli
- INFN Sezione di Pavia and University of Pavia, Pavia, Italy
| | - G. Meng
- INFN Sezione di Padova and University of Padova, Padua, Italy
| | - O. G. Miranda
- Centro de Investigacion y de Estudios Avanzados del IPN (Cinvestav), Mexico City, Mexico
| | - D. Mladenov
- CERN, European Organization for Nuclear Research, 1211 Geneva 23, Switzerland
| | - A. Mogan
- Colorado State University, Fort Collins, CO 80523 USA
| | - N. Moggi
- INFN Sezione di Bologna and University of Bologna, Bologna, Italy
| | - E. Montagna
- INFN Sezione di Bologna and University of Bologna, Bologna, Italy
| | - C. Montanari
- Fermi National Accelerator Laboratory, Batavia, IL 60510 USA
- On leave of absence from INFN Pavia, Pavia, Italy
| | - A. Montanari
- INFN Sezione di Bologna and University of Bologna, Bologna, Italy
| | - M. Mooney
- Colorado State University, Fort Collins, CO 80523 USA
| | - G. Moreno-Granados
- Centro de Investigacion y de Estudios Avanzados del IPN (Cinvestav), Mexico City, Mexico
| | - J. Mueller
- Colorado State University, Fort Collins, CO 80523 USA
| | - D. Naples
- University of Pittsburgh, Pittsburgh, PA 15260 USA
| | | | - M. Nessi
- CERN, European Organization for Nuclear Research, 1211 Geneva 23, Switzerland
| | - T. Nichols
- Fermi National Accelerator Laboratory, Batavia, IL 60510 USA
| | - M. Nicoletto
- INFN Sezione di Padova and University of Padova, Padua, Italy
| | - B. Norris
- Fermi National Accelerator Laboratory, Batavia, IL 60510 USA
| | - S. Palestini
- CERN, European Organization for Nuclear Research, 1211 Geneva 23, Switzerland
| | - M. Pallavicini
- INFN Sezione di Genova and University of Genova, Genoa, Italy
| | - V. Paolone
- University of Pittsburgh, Pittsburgh, PA 15260 USA
| | | | - L. Pasqualini
- INFN Sezione di Bologna and University of Bologna, Bologna, Italy
| | - L. Patrizii
- INFN Sezione di Bologna and University of Bologna, Bologna, Italy
| | - R. Peghin
- INFN Sezione di Padova and University of Padova, Padua, Italy
| | - G. Petrillo
- SLAC National Acceleratory Laboratory, Menlo Park, CA 94025 USA
| | - C. Petta
- INFN Sezione di Catania and University of Catania, Catania, Italy
| | - V. Pia
- INFN Sezione di Bologna and University of Bologna, Bologna, Italy
| | - F. Pietropaolo
- CERN, European Organization for Nuclear Research, 1211 Geneva 23, Switzerland
- On leave of absence from INFN Padova, Padua, Italy
| | - J. Poirot
- CERN, European Organization for Nuclear Research, 1211 Geneva 23, Switzerland
| | - F. Poppi
- INFN Sezione di Bologna and University of Bologna, Bologna, Italy
| | - M. Pozzato
- INFN Sezione di Bologna and University of Bologna, Bologna, Italy
| | - M. C. Prata
- INFN Sezione di Pavia and University of Pavia, Pavia, Italy
| | - A. Prosser
- Fermi National Accelerator Laboratory, Batavia, IL 60510 USA
| | - G. Putnam
- University of Chicago, Chicago, IL 60637 USA
| | - X. Qian
- Brookhaven National Laboratory, Upton, NY 11973 USA
| | - G. Rampazzo
- INFN Sezione di Padova and University of Padova, Padua, Italy
| | - A. Rappoldi
- INFN Sezione di Pavia and University of Pavia, Pavia, Italy
| | - G. L. Raselli
- INFN Sezione di Pavia and University of Pavia, Pavia, Italy
| | - R. Rechenmacher
- Fermi National Accelerator Laboratory, Batavia, IL 60510 USA
| | - F. Resnati
- CERN, European Organization for Nuclear Research, 1211 Geneva 23, Switzerland
| | | | | | - L. Rice
- University of Pittsburgh, Pittsburgh, PA 15260 USA
| | - E. Richards
- University of Pittsburgh, Pittsburgh, PA 15260 USA
| | - A. Rigamonti
- CERN, European Organization for Nuclear Research, 1211 Geneva 23, Switzerland
| | | | - M. Rossella
- INFN Sezione di Pavia and University of Pavia, Pavia, Italy
| | | | - P. Sala
- INFN Sezione di Milano, Milan, Italy
| | | | - G. Savage
- Fermi National Accelerator Laboratory, Batavia, IL 60510 USA
| | - A. Scaramelli
- INFN Sezione di Pavia and University of Pavia, Pavia, Italy
| | - A. Scarpelli
- Brookhaven National Laboratory, Upton, NY 11973 USA
| | - D. Schmitz
- University of Chicago, Chicago, IL 60637 USA
| | - A. Schukraft
- Fermi National Accelerator Laboratory, Batavia, IL 60510 USA
| | - F. Sergiampietri
- CERN, European Organization for Nuclear Research, 1211 Geneva 23, Switzerland
- Present Address: IPSI-INAF Torino, Turin, Italy
| | - G. Sirri
- INFN Sezione di Bologna and University of Bologna, Bologna, Italy
| | | | - A. K. Soha
- Fermi National Accelerator Laboratory, Batavia, IL 60510 USA
| | - M. Spanu
- INFN Sezione di Milano Bicocca and University of Milano Bicocca, Milan, Italy
| | - L. Stanco
- INFN Sezione di Padova and University of Padova, Padua, Italy
| | - J. Stewart
- Brookhaven National Laboratory, Upton, NY 11973 USA
| | - N. B. Suarez
- University of Pittsburgh, Pittsburgh, PA 15260 USA
| | - C. Sutera
- INFN Sezione di Catania and University of Catania, Catania, Italy
| | - H. A. Tanaka
- SLAC National Acceleratory Laboratory, Menlo Park, CA 94025 USA
| | - M. Tenti
- INFN Sezione di Bologna and University of Bologna, Bologna, Italy
| | - K. Terao
- SLAC National Acceleratory Laboratory, Menlo Park, CA 94025 USA
| | - F. Terranova
- INFN Sezione di Milano Bicocca and University of Milano Bicocca, Milan, Italy
| | - V. Togo
- INFN Sezione di Bologna and University of Bologna, Bologna, Italy
| | - D. Torretta
- Fermi National Accelerator Laboratory, Batavia, IL 60510 USA
| | - M. Torti
- INFN Sezione di Milano Bicocca and University of Milano Bicocca, Milan, Italy
| | - F. Tortorici
- INFN Sezione di Catania and University of Catania, Catania, Italy
| | - N. Tosi
- INFN Sezione di Bologna and University of Bologna, Bologna, Italy
| | - Y.-T. Tsai
- SLAC National Acceleratory Laboratory, Menlo Park, CA 94025 USA
| | - S. Tufanli
- CERN, European Organization for Nuclear Research, 1211 Geneva 23, Switzerland
| | - M. Turcato
- INFN Sezione di Padova and University of Padova, Padua, Italy
| | - T. Usher
- SLAC National Acceleratory Laboratory, Menlo Park, CA 94025 USA
| | - F. Varanini
- INFN Sezione di Padova and University of Padova, Padua, Italy
| | - S. Ventura
- INFN Sezione di Padova and University of Padova, Padua, Italy
| | - F. Vercellati
- INFN Sezione di Pavia and University of Pavia, Pavia, Italy
| | - M. Vicenzi
- Brookhaven National Laboratory, Upton, NY 11973 USA
| | | | - B. Viren
- Brookhaven National Laboratory, Upton, NY 11973 USA
| | - D. Warner
- Colorado State University, Fort Collins, CO 80523 USA
| | - Z. Williams
- University of Texas at Arlington, Arlington, TX 76019 USA
| | - R. J. Wilson
- Colorado State University, Fort Collins, CO 80523 USA
| | - P. Wilson
- Fermi National Accelerator Laboratory, Batavia, IL 60510 USA
| | - J. Wolfs
- University of Rochester, Rochester, NY 14627 USA
| | | | - A. Wood
- University of Houston, Houston, TX 77204 USA
| | - E. Worcester
- Brookhaven National Laboratory, Upton, NY 11973 USA
| | - M. Worcester
- Brookhaven National Laboratory, Upton, NY 11973 USA
| | - M. Wospakrik
- Fermi National Accelerator Laboratory, Batavia, IL 60510 USA
| | - H. Yu
- Brookhaven National Laboratory, Upton, NY 11973 USA
| | - J. Yu
- University of Texas at Arlington, Arlington, TX 76019 USA
| | - A. Zani
- INFN Sezione di Milano, Milan, Italy
| | - P. G. Zatti
- INFN Sezione di Padova and University of Padova, Padua, Italy
| | - J. Zennamo
- Fermi National Accelerator Laboratory, Batavia, IL 60510 USA
| | | | - C. Zhang
- Brookhaven National Laboratory, Upton, NY 11973 USA
| | - S. Zucchelli
- INFN Sezione di Bologna and University of Bologna, Bologna, Italy
| | - M. Zuckerbrot
- Fermi National Accelerator Laboratory, Batavia, IL 60510 USA
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Zhang Y, Wang K, Yu H, Zhao T, Lin L, Qin X, Wu T, Chen D, Hu Y, Wu Y. Incidence and characteristics of aspiration pneumonia in adults in Beijing, China, 2011-2017. Public Health 2023; 220:65-71. [PMID: 37270854 DOI: 10.1016/j.puhe.2023.04.021] [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: 12/16/2022] [Revised: 04/21/2023] [Accepted: 04/26/2023] [Indexed: 06/06/2023]
Abstract
OBJECTIVES This study aimed to estimate aspiration pneumonia (AP) incidence and describe comorbid characteristics and mortality in Beijing, China. STUDY DESIGN A historical cohort study was conducted based on medical claim records. METHODS Patients admitted with a primary diagnosis of AP were identified from approximately 12 million adults who enrolled in the Urban Employee Basic Medical Insurance program in Beijing, China, from January 2011 to December 2017. The incidences of AP and pneumonia with risk factors for aspiration (PRFA) were estimated by a Poisson distribution. The estimated annual percentage change was reported to represent the average percentage change in incidence per year. Characteristics and 6-month and 1-year all-cause mortality rates for AP and suspected AP patients were described and compared with community-acquired pneumonia (CAP). RESULTS The incidence rates of hospitalized AP and PRFA were 9.4 (95% confidence interval [CI]: 7.6, 11.3) and 102.9 (95% CI: 95.8, 110.3) per 100,000 person-years, respectively. The incidences increased rapidly with age and were stable across the observed years. Patients with AP and PRFA possessed a greater burden of comorbidities than CAP (mean age-adjusted Charlson comorbidity indices for AP: 7.72, PRFA: 7.83, and CAP: 2.84). The 6-month and 1-year all-cause mortality rates for those with AP and PRFA were higher than those for patients with CAP (6-month mortality, AP: 35.2%, PRFA: 21.8%, CAP: 11.1%; 1-year mortality, AP: 42.7%, PRFA: 26.6%, CAP: 13.2%). CONCLUSIONS The incidence of AP and PRFA in Beijing was reported, presenting a full picture of the disease burden. The results provide baseline information for AP prevention.
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Affiliation(s)
- Y Zhang
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Health Science Center, 100191, China
| | - K Wang
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Health Science Center, 100191, China
| | - H Yu
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Health Science Center, 100191, China
| | - T Zhao
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Health Science Center, 100191, China
| | - L Lin
- Geriatric Department, Peking University First Hospital, 100034, China
| | - X Qin
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Health Science Center, 100191, China; Key Laboratory of Epidemiology of Major Diseases, Peking University, Ministry of Education, 100191, China
| | - T Wu
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Health Science Center, 100191, China; Key Laboratory of Epidemiology of Major Diseases, Peking University, Ministry of Education, 100191, China
| | - D Chen
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Health Science Center, 100191, China; Key Laboratory of Epidemiology of Major Diseases, Peking University, Ministry of Education, 100191, China
| | - Y Hu
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Health Science Center, 100191, China; Key Laboratory of Epidemiology of Major Diseases, Peking University, Ministry of Education, 100191, China.
| | - Y Wu
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Health Science Center, 100191, China; Key Laboratory of Epidemiology of Major Diseases, Peking University, Ministry of Education, 100191, China.
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Zhang L, Song S, Chen B, Li R, Wang L, Wang C, Han L, Fu Z, Zhang Z, Wang Q, Yu H. Integration of UHPLC/Q-OrbitrapMS-based metabolomics and activities evaluation to rapidly explore the anti-inflammatory components from lasianthus. Heliyon 2023; 9:e16117. [PMID: 37274662 PMCID: PMC10238613 DOI: 10.1016/j.heliyon.2023.e16117] [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: 03/25/2023] [Revised: 04/21/2023] [Accepted: 05/06/2023] [Indexed: 06/06/2023] Open
Abstract
Lasianthus, belonging to Rubiaceae, has been verified to improve clinical syndrome in immune diseases (e.g., hepatitis, nephritis, and rheumatoid arthritis). Both the anti-inflammatory function and chemical composition of Lasianthus vary considerably between different species but few studies focus. So essential it is to explore lasianthus and further search for anti-inflammatory substances. The target of this artical is to analyze the anti-inflammatory activity and chemical composition of lasianthus of different species. And the subsequent active compounds were explored. Primary, the anti-inflammatory activity among seven species of lasianthus (e.g., L. fordii., L. wallichii., L. hookeri C., L. verticillatus., L. sikkimensis., L. appressihirtus., and L. hookeri var) were evaluated by vitro experiments (RAW 264.7 cells). Next, UHPLC/Q-Orbitrap-MS-based metabolomics and the mass defect filter (MDF) algorithm were performed to explore metabolites. In addition, principal component analysis (PCA) was to screen out differential compounds in seven species. Finally, the correlation analysis between activities and composition to rapidly discover the active compounds (compounds were verified pharmacologically). Among the 7 species of lasianthus, the L. fordii. and L. hookeri C indicated the best anti-inflammatory activity. Untargeted metabolomics and MDF show 112 compounds, classified into six dominant types (e.g., flavonoids, phenolic acids, alkaloids, iridoids, coumarins, and anthraquinones). Furthermore, 33 differential metabolites were confirmed by PCA. Then according to correlation analysis and pharmacological validation, 7 compounds IC50<100 (e.g., scopoletin, asperulosidic acid, chlorogenic acid, ferulic acid, betaine, syringic acid, and emodin) were verified as anti-inflammatory compounds and conduct quantitative analysis. Metabolomics integrated with activities evaluation might be a rapid and effective strategy to explore the active compounds from natural products.
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Affiliation(s)
- Lele Zhang
- State Key Laboratory of Component-based Chinese Medicine, Tianjin Key Laboratory of TCM Chemistry and Analysis, Tianjin University of Traditional Chinese Medicine, 10 Poyanghu Road, Jinghai District, Tianjin 301617, PR China
| | - Shaofei Song
- State Key Laboratory of Component-based Chinese Medicine, Tianjin Key Laboratory of TCM Chemistry and Analysis, Tianjin University of Traditional Chinese Medicine, 10 Poyanghu Road, Jinghai District, Tianjin 301617, PR China
| | - Biying Chen
- State Key Laboratory of Component-based Chinese Medicine, Tianjin Key Laboratory of TCM Chemistry and Analysis, Tianjin University of Traditional Chinese Medicine, 10 Poyanghu Road, Jinghai District, Tianjin 301617, PR China
| | - Rongrong Li
- State Key Laboratory of Component-based Chinese Medicine, Tianjin Key Laboratory of TCM Chemistry and Analysis, Tianjin University of Traditional Chinese Medicine, 10 Poyanghu Road, Jinghai District, Tianjin 301617, PR China
| | - Liming Wang
- State Key Laboratory of Component-based Chinese Medicine, Tianjin Key Laboratory of TCM Chemistry and Analysis, Tianjin University of Traditional Chinese Medicine, 10 Poyanghu Road, Jinghai District, Tianjin 301617, PR China
| | - Chenxi Wang
- State Key Laboratory of Component-based Chinese Medicine, Tianjin Key Laboratory of TCM Chemistry and Analysis, Tianjin University of Traditional Chinese Medicine, 10 Poyanghu Road, Jinghai District, Tianjin 301617, PR China
| | - Lifeng Han
- State Key Laboratory of Component-based Chinese Medicine, Tianjin Key Laboratory of TCM Chemistry and Analysis, Tianjin University of Traditional Chinese Medicine, 10 Poyanghu Road, Jinghai District, Tianjin 301617, PR China
- Haihe Laboratory of Modern Chinese Medicine, Tianjin, 301617, PR China
| | - Zhifei Fu
- State Key Laboratory of Component-based Chinese Medicine, Tianjin Key Laboratory of TCM Chemistry and Analysis, Tianjin University of Traditional Chinese Medicine, 10 Poyanghu Road, Jinghai District, Tianjin 301617, PR China
| | - Zhonglian Zhang
- Yunnan Key Laboratory of Southern Medicine Utilization, Yunnan Branch of Institute of Medicinal Plant Development, Chinese Academy of Medical Sciences, Peking Union Medical College, Jinghong, 666100, China
| | - Qilong Wang
- State Key Laboratory of Component-based Chinese Medicine, Tianjin Key Laboratory of TCM Chemistry and Analysis, Tianjin University of Traditional Chinese Medicine, 10 Poyanghu Road, Jinghai District, Tianjin 301617, PR China
- Haihe Laboratory of Modern Chinese Medicine, Tianjin, 301617, PR China
| | - Heshui Yu
- State Key Laboratory of Component-based Chinese Medicine, Tianjin Key Laboratory of TCM Chemistry and Analysis, Tianjin University of Traditional Chinese Medicine, 10 Poyanghu Road, Jinghai District, Tianjin 301617, PR China
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Westberry TK, Behrenfeld MJ, Shi YR, Yu H, Remer LA, Bian H. Atmospheric nourishment of global ocean ecosystems. Science 2023; 380:515-519. [PMID: 37141373 DOI: 10.1126/science.abq5252] [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: 05/06/2023]
Abstract
Over the vast open ocean, vital nutrients for phytoplankton growth in the sunlit surface layer are largely provided through physical transport from deep waters, but some nutrients are also provided through atmospheric deposition of desert dust. The extent and magnitude of dust-mediated effects on surface ocean ecosystems have been difficult to estimate globally. In this work, we use global satellite ocean color products to demonstrate widespread responses to atmospheric dust deposition across a diverse continuum of phytoplankton nutritional conditions. The observed responses vary regionally, with some areas exhibiting substantial changes in phytoplankton biomass, whereas in other areas, the response reflects a change in physiological status or health. Climate-driven changes in atmospheric aerosols will alter the relative importance of this nutrient source.
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Affiliation(s)
- T K Westberry
- Department of Botany and Plant Pathology, Oregon State University, Corvallis, OR, USA
| | - M J Behrenfeld
- Department of Botany and Plant Pathology, Oregon State University, Corvallis, OR, USA
| | - Y R Shi
- Joint Center for Earth Systems Technology, University of Maryland, Baltimore County, Baltimore, MD, USA
- Earth Sciences Division, NASA Goddard Space Flight Center, Greenbelt, MD, USA
| | - H Yu
- Earth Sciences Division, NASA Goddard Space Flight Center, Greenbelt, MD, USA
| | - L A Remer
- Joint Center for Earth Systems Technology, University of Maryland, Baltimore County, Baltimore, MD, USA
- Airphoton Inc., Baltimore, MD, USA
| | - H Bian
- Joint Center for Earth Systems Technology, University of Maryland, Baltimore County, Baltimore, MD, USA
- Earth Sciences Division, NASA Goddard Space Flight Center, Greenbelt, MD, USA
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Zhong BJ, Yang S, Hong DW, Cheng YL, Attin T, Yu H. The Efficacy of At-home, In-office, and Combined Bleaching Regimens: A Randomized Controlled Clinical Trial. Oper Dent 2023:492392. [PMID: 37079917 DOI: 10.2341/22-099-c] [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] [Accepted: 01/06/2023] [Indexed: 04/22/2023]
Abstract
OBJECTIVE The aim of this study was to compare the clinical efficacy of at-home, in-office, and combined bleaching regimens. METHODS Forty-eight participants were recruited and randomly divided into four groups based on the bleaching regimen (n=12) as follows: 1) at-home bleaching using 10% carbamide peroxide (Opalescence PF 10%, Ultradent) for 14 days (HB); 2) two sessions of in-office bleaching using 40% hydrogen peroxide (Opalescence BOOST PF 40%, Ultradent) with a one-week interval (OB); 3) one session of in-office bleaching followed by at-home bleaching for seven days (OHB); and 4) at-home bleaching for seven days followed by one session of in-office bleaching (HOB). Tooth color was measured using a spectrophotometer (Easyshade, Vita ZahnFabrik) at baseline (T0), day 8 (T1), day 15 (T2), and day 43 (T3, four weeks after the end of the bleaching treatment). The color data were calculated using the CIEDE2000 (ΔE00) and whiteness index for dentistry (WID) formulas. Tooth sensitivity (TS) was recorded using a visual analogue scale (VAS) for 16 days. Data were analyzed by one-way analysis of variance (ANOVA) and the Wilcoxon signed-rank test (α=0.05). RESULTS All bleaching regimens resulted in a significant increase in WID values (all p<0.05), while no significant differences in WID and ΔWID values were found among the different groups at each time point (all p>0.05). Significant differences in ΔE00 values were observed between T1 and T3 for all groups (all p<0.05), while no significant differences in ΔE00 values were found among the different groups at any time point (all p>0.05). Significantly lower TS values were observed in the HB group than in the OB and HOB groups (p=0.006 and p=0.001, respectively). CONCLUSIONS All bleaching regimens resulted in great color improvement, and different regimens led to similar color changes at any of the evaluation time points. The sequence of treatments applying in-office bleaching or at-home bleaching did not affect the bleaching efficacy. The in-office bleaching and combined bleaching regimens yielded a higher intensity of TS than did at-home bleaching.
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Affiliation(s)
- B-J Zhong
- †Bing-jie Zhong, DDS, PhD candidate, Fujian Key Laboratory of Oral Diseases & Fujian Provincial Engineering Research Center of Oral Biomaterial & Stomatological Key Laboratory of Fujian College and University, School and Hospital of Stomatology, Fujian Medical University, Fuzhou, China
| | - S Yang
- †Song Yang, DDS, MS, PhD candidate, Fujian Key Laboratory of Oral Diseases & Fujian Provincial Engineering Research Center of Oral Biomaterial & Stomatological Key Laboratory of Fujian College and University, School and Hospital of Stomatology, Fujian Medical University, Fuzhou, China
| | - D-W Hong
- Deng-wei Hong, DDS, MS, dentist, School and Hospital of Stomatology, Fujian Medical University, Fuzhou, China
| | - Y-L Cheng
- Yi-ling Cheng, DDS, PhD candidate, Fujian Key Laboratory of Oral Diseases & Fujian Provincial Engineering Research Center of Oral Biomaterial & Stomatological Key Laboratory of Fujian College and University, School and Hospital of Stomatology, Fujian Medical University, Fuzhou, China
| | - T Attin
- Thomas Attin, Dr Med Dent, professor, Clinic of Conservative and Preventive Dentistry, Center for Dental Medicine, University of Zurich, Switzerland
| | - H Yu
- *Hao Yu, DDS, PhD, Dr Med Dent, associate professor and Associate Dean, School and Hospital of Stomatology, Fujian Medical University, Fuzhou, China; adjunct professor, Department of Applied Prosthodontics, Graduate School of Biomedical Sciences, Nagasaki University, Nagasaki, Japan
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Liu Q, Dai Y, Yu H, Shen Y, Deng J, Lu W, Jin J. [NKD1 promotes glucose uptake in colon cancer cells by activating YWHAE transcription]. Nan Fang Yi Ke Da Xue Xue Bao 2023; 43:585-589. [PMID: 37202194 DOI: 10.12122/j.issn.1673-4254.2023.04.11] [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: 05/20/2023]
Abstract
OBJECTIVE Bo investigate the regulatory relationship between NKD1 and YWHAE and the mechanism of NKD1 for promoting tumor cell proliferation. METHODS HCT116 cells transfected with pcDNA3.0-NKD1 plasmid, SW620 cells transfected with NKD1 siRNA, HCT116 cells with stable NKD1 overexpression (HCT116-NKD1 cells), SW620 cells with nkd1knockout (SW620-nkd1-/- cells), and SW620-nkd1-/- cells transfected with pcDNA3.0-YWHAE plasmid were examined for changes in mRNA and protein expression levels of YWHAE using qRT-PCR and Western blotting. Chromatin immunoprecipitation (ChIP) assay was used to detect the binding of NKD1 to the promoter region of YWHAE gene. The regulatory effect of NKD1 on YWHAE gene promoter activity was analyzed by dual-luciferase reporter gene assay, and the interaction between NKD1 and YWHAE was analyzed with immunofluorescence assay. The regulatory effect of NKD1 on glucose uptake was examined in the tumor cells. RESULTS In HCT116 cells, overexpression of NKD1 significantly enhanced the expression of YWHAE at both the mRNA and protein levels, while NKD1 knockout decreased its expression in SW620 cells (P < 0.001). ChIP assay showed that NKD1 protein was capable of binding to the YWHAE promoter sequence; dual luciferase reporter gene assay showed that NKD1 overexpression (or knockdown) in the colon cancer cells significantly enhanced (or reduced) the transcriptional activity of YWHAE promoter (P < 0.05). Immunofluorescence assay demonstrated the binding of NKD1 and YWHAE proteins in colon cancer cells. NKD1 knockout significantly reduced glucose uptake in colon cancer cells (P < 0.01), while YWHAE overexpression restored the glucose uptake in NKD1-knockout cells (P < 0.05). CONCLUSION NKD1 protein activates the transcriptional activity of YWHAE gene to promote glucose uptake in colon cancer cells.
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Affiliation(s)
- Q Liu
- Department of Oncology, Wujin Hospital Affiliated to Jiangsu University/Wujin Clinical College, Xuzhou Medical University, Changzhou 213017, China
- Changzhou Key Laboratory of Molecular Diagnostics and Precision Cancer Medicine/Wujin Institute of Molecular Diagnostics and Precision Cancer Medicine of Jiangsu University, Changzhou 213017, China
| | - Y Dai
- Department of Oncology, Wujin Hospital Affiliated to Jiangsu University/Wujin Clinical College, Xuzhou Medical University, Changzhou 213017, China
- Changzhou Key Laboratory of Molecular Diagnostics and Precision Cancer Medicine/Wujin Institute of Molecular Diagnostics and Precision Cancer Medicine of Jiangsu University, Changzhou 213017, China
| | - H Yu
- Department of Oncology, Wujin Hospital Affiliated to Jiangsu University/Wujin Clinical College, Xuzhou Medical University, Changzhou 213017, China
- Changzhou Key Laboratory of Molecular Diagnostics and Precision Cancer Medicine/Wujin Institute of Molecular Diagnostics and Precision Cancer Medicine of Jiangsu University, Changzhou 213017, China
| | - Y Shen
- Department of Oncology, Wujin Hospital Affiliated to Jiangsu University/Wujin Clinical College, Xuzhou Medical University, Changzhou 213017, China
- Changzhou Key Laboratory of Molecular Diagnostics and Precision Cancer Medicine/Wujin Institute of Molecular Diagnostics and Precision Cancer Medicine of Jiangsu University, Changzhou 213017, China
| | - J Deng
- Department of Oncology, Wujin Hospital Affiliated to Jiangsu University/Wujin Clinical College, Xuzhou Medical University, Changzhou 213017, China
- Changzhou Key Laboratory of Molecular Diagnostics and Precision Cancer Medicine/Wujin Institute of Molecular Diagnostics and Precision Cancer Medicine of Jiangsu University, Changzhou 213017, China
| | - W Lu
- Department of Oncology, Wujin Hospital Affiliated to Jiangsu University/Wujin Clinical College, Xuzhou Medical University, Changzhou 213017, China
- Changzhou Key Laboratory of Molecular Diagnostics and Precision Cancer Medicine/Wujin Institute of Molecular Diagnostics and Precision Cancer Medicine of Jiangsu University, Changzhou 213017, China
| | - J Jin
- Department of Oncology, Wujin Hospital Affiliated to Jiangsu University/Wujin Clinical College, Xuzhou Medical University, Changzhou 213017, China
- Changzhou Key Laboratory of Molecular Diagnostics and Precision Cancer Medicine/Wujin Institute of Molecular Diagnostics and Precision Cancer Medicine of Jiangsu University, Changzhou 213017, China
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Yin J, Li C, Zhang J, Ding H, Han L, Yang W, Li F, Song X, Bie S, Yu H, Li Z. Comprehensive multicomponent characterization and quality assessment of Shuang-Huang-Lian powder injection using ultra-high-performance liquid chromatography-quadrupole time-of-flight-mass spectrometry and ultra-high-performance liquid chromatography-quadrupole-Orbitrap-mass spectrometry. Rapid Commun Mass Spectrom 2023; 37:e9479. [PMID: 36690334 DOI: 10.1002/rcm.9479] [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] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/19/2022] [Revised: 01/18/2023] [Accepted: 01/18/2023] [Indexed: 06/17/2023]
Abstract
RATIONALE Shuang-Huang-Lian powder injection (SHLPI) is a well-known modern traditional Chinese medicine formula preparation (TCMFP) widely used to treat acute upper respiratory infections. However, SHLPI is extracted from pure Chinese medicine and administered through an injection, and many adverse reactions have been reported clinically. Therefore, it is necessary to characterize in depth the chemical composition of SHLPI and quantitatively analyze its potential allergenic components. METHODS In this study, the samples were analyzed using ion mobility ultra-high-performance liquid chromatography-quadrupole time-of-flight-mass spectrometry (UHPLC-QTOF-MS) combined with a self-built database. Furthermore, the parallel reaction monitoring (PRM) model of ultra-high-performance liquid chromatography-quadrupole-Orbitrap-mass spectrometry (UHPLC-Q-Orbitrap-MS) was used to successfully quantify 10 representative bioactive components. RESULTS Using this strategy 90 compounds were identified, the fragmentation pathways of five representative compounds in the five main components of SHLPI were summarized, and 10 components (neochlorogenic acid, chlorogenic acid, sweroside, forsythiaside A, luteoloside, isochlorogenic acid B, isochlorogenic acid C, baicalin, phillyrin, and baicalein) were determine as the quality markers of SHLPI based on UPLC-Q-Orbitrap-MS. CONCLUSIONS This work comprehensively characterized the material basis of SHLPI, summarized the cracking laws of representative substances, and quantitatively analyzed 10 potential allergenic components. Therefore, this study could provide a basis for the quality control of SHLPI and the clinical rational use of drugs to reduce its adverse reactions.
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Affiliation(s)
- Jiaxin Yin
- College of Pharmaceutical Engineering of Traditional Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin, P. R. China
| | - Chao Li
- College of Pharmaceutical Engineering of Traditional Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin, P. R. China
| | - Jie Zhang
- College of Pharmaceutical Engineering of Traditional Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin, P. R. China
| | - Hui Ding
- College of Pharmaceutical Engineering of Traditional Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin, P. R. China
| | - Lifeng Han
- State Key Laboratory of Component-Based Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin, P. R. China
| | - Wenzhi Yang
- State Key Laboratory of Component-Based Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin, P. R. China
| | - Fangyi Li
- College of Pharmaceutical Engineering of Traditional Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin, P. R. China
- State Key Laboratory of Component-Based Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin, P. R. China
- Haihe Laboratory of Modern Chinese Medicine, Tianjin, P. R. China
| | - Xinbo Song
- College of Pharmaceutical Engineering of Traditional Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin, P. R. China
- State Key Laboratory of Component-Based Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin, P. R. China
- Haihe Laboratory of Modern Chinese Medicine, Tianjin, P. R. China
| | - Songtao Bie
- College of Pharmaceutical Engineering of Traditional Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin, P. R. China
- State Key Laboratory of Component-Based Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin, P. R. China
- Haihe Laboratory of Modern Chinese Medicine, Tianjin, P. R. China
| | - Heshui Yu
- College of Pharmaceutical Engineering of Traditional Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin, P. R. China
- State Key Laboratory of Component-Based Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin, P. R. China
- Haihe Laboratory of Modern Chinese Medicine, Tianjin, P. R. China
| | - Zheng Li
- College of Pharmaceutical Engineering of Traditional Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin, P. R. China
- State Key Laboratory of Component-Based Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin, P. R. China
- Haihe Laboratory of Modern Chinese Medicine, Tianjin, P. R. China
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Zheng ZB, Yu H, Zheng W, Chen Q, Lou XQ, Liu XD, Wang HQ, Pan JC. [Drug resistance and genomic characteristics of Salmonella enterica serovar London from clinical and food sources in Hangzhou City from 2017 to 2021]. Zhonghua Yu Fang Yi Xue Za Zhi 2023; 57:508-515. [PMID: 37032160 DOI: 10.3760/cma.j.cn112150-20220622-00645] [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: 04/11/2023]
Abstract
Objective: To analyze the drug resistance and genomic characteristics of Salmonella enterica serovar London isolated from clinical and food sources in Hangzhou City from 2017 to 2021. Methods: A total of 91 Salmonella enterica serovar London strains isolated from Hangzhou City from 2017 to 2021 were analyzed for drug susceptibility, pulsed field gel electrophoresis (PFGE) typing and whole genome sequencing. Multilocus sequence typing (MLST), core genome multilocus sequence typing (cgMLST) and detection of drug resistance genes were performed by using the sequencing data. Phylogenetic analysis was conducted to compare the 91 genomes from Hangzhou City with 347 genomes from public databases. Results: No significant difference in the drug resistance rate was observed between clinical strains and food strains to 18 drugs in Hangzhou City(all P>0.05), and the multidrug resistance (MDR) rate was 75.8% (69/91). Most strains were resistant to 7 drug classes simultaneously. One strain was resistant to Polymyxin E as well as positive for mcr-1.1, and 50.5% (46/91) of the strains were resistant to Azithromycin and were positive for mph(A). All 91 Salmonella enterica serovar London strains were ST155, which were subdivided into 44 molecular types by PFGE and 82 types by cgMLST. Phylogenetic analysis showed that most strains from Hangzhou City (83/91) were clustered together, and a small number of human isolates from Europe, North America and pork isolates from Hubei and Shenzhen were mixed in the cluster. Other strains from Hangzhou City (8/91) were closely related to strains from Europe, America and Southeast Asia. Strains isolated from pork were the most closely related to clinical strains. Conclusion: The epidemic of Salmonella enterica serovar London in Hangzhou City is mainly caused by the spread of ST155 strains, which is mainly transmitted locally. At the same time, cross-region transmission to Europe, North America, Southeast Asia, and other provinces and cities in China may also occur. There is no significant difference in the drug resistance rate between clinical strains and food strains, and a high level of MDR is found in the strains. Clinical infection of Salmonella enterica serovar London may be closely related to pork consumption in Hangzhou City.
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Affiliation(s)
- Z B Zheng
- Health Inspection Center, Hangzhou Center for Disease Control and Prevention, Hangzhou 310021, China
| | - H Yu
- Health Inspection Center, Hangzhou Center for Disease Control and Prevention, Hangzhou 310021, China
| | - W Zheng
- Health Inspection Center, Hangzhou Center for Disease Control and Prevention, Hangzhou 310021, China
| | - Q Chen
- Health Inspection Center, Hangzhou Center for Disease Control and Prevention, Hangzhou 310021, China
| | - X Q Lou
- Health Inspection Center, Hangzhou Center for Disease Control and Prevention, Hangzhou 310021, China
| | - X D Liu
- Health Inspection Center, Hangzhou Center for Disease Control and Prevention, Hangzhou 310021, China
| | - H Q Wang
- Health Inspection Center, Hangzhou Center for Disease Control and Prevention, Hangzhou 310021, China
| | - J C Pan
- Health Inspection Center, Hangzhou Center for Disease Control and Prevention, Hangzhou 310021, China
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Zhou N, Li X, Wang J, Yu H, Su C, Zu L, Huang D, Xu S. 224P Genetic landscape, PD-L1 expression, and CD8+ infiltration in Chinese pulmonary carcinoids. J Thorac Oncol 2023. [DOI: 10.1016/s1556-0864(23)00477-x] [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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Chen X, He J, Shen H, Xi Y, Chen B, He X, Gao J, Yu H, Shen W. 97P Aumolertinib as adjuvant therapy in postoperative EGFR-mutated stage I–III non-small cell lung cancer with high-risk pathological factors. J Thorac Oncol 2023. [DOI: 10.1016/s1556-0864(23)00352-0] [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: 04/04/2023]
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Chen M, Yu H. 61P Early palliative care in patients with non-small cell lung cancer: A 36-weeks randomised controlled trial in China. J Thorac Oncol 2023. [DOI: 10.1016/s1556-0864(23)00315-5] [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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Chen M, Yu H. Gut Microbiota Mediates The Protective Effects Of Resveratrol Against The Intestinal Barrier Dysfunction In Non-Alcoholic Steatohepatitis Induced By High-Fat Diet. Clin Nutr ESPEN 2023. [DOI: 10.1016/j.clnesp.2022.09.298] [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: 03/28/2023]
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Chen M, Yu H. Early Palliative Care Focus On Nutritional Status In Patients With Non-Small-Cell Lung Cancer: A Randomised Controlled Trial In Southwest China. Clin Nutr ESPEN 2023. [DOI: 10.1016/j.clnesp.2022.09.093] [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: 03/28/2023]
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