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Liu CJ, Li HY, Gao Y, Xie GY, Chi JH, Li GL, Zeng SQ, Xiong XM, Liu JH, Shi LL, Li X, Cheng XD, Song K, Ma D, Guo AY, Gao QL. Platelet RNA signature independently predicts ovarian cancer prognosis by deep learning neural network model. Protein Cell 2023; 14:618-622. [PMID: 37526343 PMCID: PMC10392027 DOI: 10.1093/procel/pwac053] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2022] [Revised: 09/29/2022] [Accepted: 10/19/2022] [Indexed: 08/02/2023] Open
Affiliation(s)
- Chun-Jie Liu
- Department of Gynecological Oncology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
- National Clinical Research Center for Obstetrics and Gynecology, Cancer Biology Research Center (Key Laboratory of the Ministry of Education), Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
- Center for Artificial Intelligence Biology, Hubei Bioinformatics & Molecular Imaging Key Laboratory, Key Laboratory of Molecular Biophysics of the Ministry of Education, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan 430074, China
| | - Hua-Yi Li
- Department of Gynecological Oncology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
- National Clinical Research Center for Obstetrics and Gynecology, Cancer Biology Research Center (Key Laboratory of the Ministry of Education), Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
| | - Yue Gao
- Department of Gynecological Oncology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
- National Clinical Research Center for Obstetrics and Gynecology, Cancer Biology Research Center (Key Laboratory of the Ministry of Education), Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
| | - Gui-Yan Xie
- Center for Artificial Intelligence Biology, Hubei Bioinformatics & Molecular Imaging Key Laboratory, Key Laboratory of Molecular Biophysics of the Ministry of Education, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan 430074, China
| | - Jian-Hua Chi
- Department of Gynecological Oncology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
- National Clinical Research Center for Obstetrics and Gynecology, Cancer Biology Research Center (Key Laboratory of the Ministry of Education), Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
| | - Gui-Ling Li
- Cancer Center, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430022, China
| | - Shao-Qing Zeng
- Department of Gynecological Oncology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
- National Clinical Research Center for Obstetrics and Gynecology, Cancer Biology Research Center (Key Laboratory of the Ministry of Education), Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
| | - Xiao-Ming Xiong
- Department of Gynecological Oncology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
- National Clinical Research Center for Obstetrics and Gynecology, Cancer Biology Research Center (Key Laboratory of the Ministry of Education), Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
| | - Jia-Hao Liu
- Department of Gynecological Oncology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
- National Clinical Research Center for Obstetrics and Gynecology, Cancer Biology Research Center (Key Laboratory of the Ministry of Education), Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
| | - Lin-Li Shi
- Cancer Center, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430022, China
| | - Xiong Li
- Department of Gynecology and Obstetrics, The Central Hospital of Wuhan, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
| | - Xiao-Dong Cheng
- Department of Gynecological Oncology, Women's Hospital, School of Medicine, Zhejiang University, Hangzhou 310011, China
| | - Kun Song
- Gynecological Oncology Key Laboratory, Qilu Hospital, Shandong University, Jinan 250100, China
| | - Ding Ma
- Department of Gynecological Oncology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
- National Clinical Research Center for Obstetrics and Gynecology, Cancer Biology Research Center (Key Laboratory of the Ministry of Education), Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
| | - An-Yuan Guo
- Center for Artificial Intelligence Biology, Hubei Bioinformatics & Molecular Imaging Key Laboratory, Key Laboratory of Molecular Biophysics of the Ministry of Education, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan 430074, China
| | - Qing-Lei Gao
- Department of Gynecological Oncology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
- National Clinical Research Center for Obstetrics and Gynecology, Cancer Biology Research Center (Key Laboratory of the Ministry of Education), Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
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Gao Y, Liu CJ, Li HY, Xiong XM, Li GL, In 't Veld SGJG, Cai GY, Xie GY, Zeng SQ, Wu Y, Chi JH, Liu JH, Zhang Q, Jiao XF, Shi LL, Lu WR, Lv WG, Yang XS, Piek JMJ, de Kroon CD, Lok CAR, Supernat A, Łapińska-Szumczyk S, Łojkowska A, Żaczek AJ, Jassem J, Tannous BA, Sol N, Post E, Best MG, Kong BH, Xie X, Ma D, Wurdinger T, Guo AY, Gao QL. Platelet RNA enables accurate detection of ovarian cancer: an intercontinental, biomarker identification study. Protein Cell 2022:6821244. [PMID: 36905391 PMCID: PMC10246718 DOI: 10.1093/procel/pwac056] [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: 08/15/2022] [Accepted: 10/19/2022] [Indexed: 11/12/2022] Open
Abstract
Platelets are reprogrammed by cancer via a process called education, which favors cancer development. The transcriptional profile of tumor-educated platelets (TEPs) is skewed and therefore practicable for cancer detection. This intercontinental, hospital-based, diagnostic study included 761 treatment-naïve inpatients with histologically confirmed adnexal masses and 167 healthy controls from nine medical centers (China, n = 3; Netherlands, n = 5; Poland, n = 1) between September 2016 and May 2019. The main outcomes were the performance of TEPs and their combination with CA125 in two Chinese (VC1 and VC2) and the European (VC3) validation cohorts collectively and independently. Exploratory outcome was the value of TEPs in public pan-cancer platelet transcriptome datasets. The AUCs for TEPs in the combined validation cohort, VC1, VC2, and VC3 were 0.918 (95% CI 0.889-0.948), 0.923 (0.855-0.990), 0.918 (0.872-0.963), and 0.887 (0.813-0.960), respectively. Combination of TEPs and CA125 demonstrated an AUC of 0.922 (0.889-0.955) in the combined validation cohort; 0.955 (0.912-0.997) in VC1; 0.939 (0.901-0.977) in VC2; 0.917 (0.824-1.000) in VC3. For subgroup analysis, TEPs exhibited an AUC of 0.858, 0.859, and 0.920 to detect early-stage, borderline, non-epithelial diseases and 0.899 to discriminate ovarian cancer from endometriosis. TEPs had robustness, compatibility, and universality for preoperative diagnosis of ovarian cancer since it withstood validations in populations of different ethnicities, heterogeneous histological subtypes, and early-stage ovarian cancer. However, these observations warrant prospective validations in a larger population before clinical utilities.
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Affiliation(s)
- Yue Gao
- Department of Gynecological Oncology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China.,National Clinical Research Center for Obstetrics and Gynecology, Cancer Biology Research Center (Key Laboratory of the Ministry of Education), Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
| | - Chun-Jie Liu
- Center for Artificial Intelligence Biology, Hubei Bioinformatics & Molecular Imaging Key Laboratory, Key Laboratory of Molecular Biophysics of the Ministry of Education, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan 430074, China
| | - Hua-Yi Li
- Department of Gynecological Oncology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China.,National Clinical Research Center for Obstetrics and Gynecology, Cancer Biology Research Center (Key Laboratory of the Ministry of Education), Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
| | - Xiao-Ming Xiong
- Department of Gynecological Oncology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China.,National Clinical Research Center for Obstetrics and Gynecology, Cancer Biology Research Center (Key Laboratory of the Ministry of Education), Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China.,Department of Obstetrics and Gynecology, The First Affiliated Hospital of Nanchang University, Nanchang 330006, China
| | - Gui-Ling Li
- Cancer Center, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430022, China
| | - Sjors G J G In 't Veld
- Department of Neurosurgery, Amsterdam UMC, VU University Medical Center, Cancer Center Amsterdam, De Boelelaan 1117, 1081 HV Amsterdam, The Netherlands.,Brain Tumor Center Amsterdam, Amsterdam UMC, VU University Medical Center, Cancer Center Amsterdam, De Boelelaan 1117, 1081 HV Amsterdam, The Netherlands
| | - Guang-Yao Cai
- Department of Gynecological Oncology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China.,National Clinical Research Center for Obstetrics and Gynecology, Cancer Biology Research Center (Key Laboratory of the Ministry of Education), Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
| | - Gui-Yan Xie
- Center for Artificial Intelligence Biology, Hubei Bioinformatics & Molecular Imaging Key Laboratory, Key Laboratory of Molecular Biophysics of the Ministry of Education, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan 430074, China
| | - Shao-Qing Zeng
- Department of Gynecological Oncology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China.,National Clinical Research Center for Obstetrics and Gynecology, Cancer Biology Research Center (Key Laboratory of the Ministry of Education), Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
| | - Yuan Wu
- Department of Gynecological Oncology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China.,National Clinical Research Center for Obstetrics and Gynecology, Cancer Biology Research Center (Key Laboratory of the Ministry of Education), Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
| | - Jian-Hua Chi
- Department of Gynecological Oncology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China.,National Clinical Research Center for Obstetrics and Gynecology, Cancer Biology Research Center (Key Laboratory of the Ministry of Education), Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
| | - Jia-Hao Liu
- Department of Gynecological Oncology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China.,National Clinical Research Center for Obstetrics and Gynecology, Cancer Biology Research Center (Key Laboratory of the Ministry of Education), Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
| | - Qiong Zhang
- Center for Artificial Intelligence Biology, Hubei Bioinformatics & Molecular Imaging Key Laboratory, Key Laboratory of Molecular Biophysics of the Ministry of Education, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan 430074, China
| | - Xiao-Fei Jiao
- Department of Gynecological Oncology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China.,National Clinical Research Center for Obstetrics and Gynecology, Cancer Biology Research Center (Key Laboratory of the Ministry of Education), Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
| | - Lin-Li Shi
- Cancer Center, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430022, China
| | - Wan-Rong Lu
- Department of Gynecological Oncology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China.,National Clinical Research Center for Obstetrics and Gynecology, Cancer Biology Research Center (Key Laboratory of the Ministry of Education), Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
| | - Wei-Guo Lv
- Department of Gynecological Oncology, Women's Hospital, School of Medicine, Zhejiang University, Hangzhou 310011, China
| | - Xing-Sheng Yang
- Gynecological Oncology Key Laboratory, Qilu Hospital, Shandong University, Jinan 250100, China
| | - Jurgen M J Piek
- Department of Obstetrics and Gynecology, Catharina Hospital, Michelangelolaan 2, 5623EJ Eindhoven, Eindhoven, The Netherlands
| | - Cornelis D de Kroon
- Department of Obstetrics and Gynecology, Leiden University Medical Center, Albinusdreef 2, 2300 RC, Leiden, The Netherlands
| | - C A R Lok
- Department of Gynecological Oncology, Center of Gynecologic Oncology Amsterdam, Antoni van Leeuwenhoek, Plesmanlaan 121, 1066 CX Amsterdam, The Netherlands
| | - Anna Supernat
- Laboratory of Translational Oncology, Intercollegiate Faculty of Biotechnology, University of Gdańsk and Medical University of Gdańsk, Gdańsk, Poland
| | - Sylwia Łapińska-Szumczyk
- Department of Gynecology, Gynecological Oncology and Gynecological Endocrinology, Medical University of Gdańsk, Gdańsk, Poland
| | - Anna Łojkowska
- Department of Gynecology, Gynecological Oncology and Gynecological Endocrinology, Medical University of Gdańsk, Gdańsk, Poland
| | - Anna J Żaczek
- Laboratory of Translational Oncology, Intercollegiate Faculty of Biotechnology, University of Gdańsk and Medical University of Gdańsk, Gdańsk, Poland
| | - Jacek Jassem
- Department of Oncology and Radiotherapy, Medical University of Gdańsk, Gdańsk, Poland
| | - Bakhos A Tannous
- Department of Neurology, Massachusetts General Hospital and Neuroscience Program, Harvard Medical School, 149 13th Street, Charlestown, MA 02129, USA
| | - Nik Sol
- Department of Neurosurgery, Amsterdam UMC, VU University Medical Center, Cancer Center Amsterdam, De Boelelaan 1117, 1081 HV Amsterdam, The Netherlands.,Department of Neurology, Amsterdam UMC, VU University Medical Center, Cancer Center Amsterdam, De Boelelaan 1117, 1081 HV Amsterdam, The Netherlands
| | - Edward Post
- Department of Neurosurgery, Amsterdam UMC, VU University Medical Center, Cancer Center Amsterdam, De Boelelaan 1117, 1081 HV Amsterdam, The Netherlands
| | - Myron G Best
- Department of Neurosurgery, Amsterdam UMC, VU University Medical Center, Cancer Center Amsterdam, De Boelelaan 1117, 1081 HV Amsterdam, The Netherlands
| | - Bei-Hua Kong
- Gynecological Oncology Key Laboratory, Qilu Hospital, Shandong University, Jinan 250100, China
| | - Xing Xie
- Department of Gynecological Oncology, Women's Hospital, School of Medicine, Zhejiang University, Hangzhou 310011, China
| | - Ding Ma
- Department of Gynecological Oncology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China.,National Clinical Research Center for Obstetrics and Gynecology, Cancer Biology Research Center (Key Laboratory of the Ministry of Education), Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
| | - Thomas Wurdinger
- Department of Neurosurgery, Amsterdam UMC, VU University Medical Center, Cancer Center Amsterdam, De Boelelaan 1117, 1081 HV Amsterdam, The Netherlands
| | - An-Yuan Guo
- Center for Artificial Intelligence Biology, Hubei Bioinformatics & Molecular Imaging Key Laboratory, Key Laboratory of Molecular Biophysics of the Ministry of Education, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan 430074, China
| | - Qing-Lei Gao
- Department of Gynecological Oncology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China.,National Clinical Research Center for Obstetrics and Gynecology, Cancer Biology Research Center (Key Laboratory of the Ministry of Education), Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
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Gao Y, Liu CJ, Li HY, Xiong XM, In ‘t Veld SG, Li GL, Liu JH, Cai GY, Xie GY, Zeng SQ, Wu Y, Chi JH, Zhang Q, Jiao XF, Shi LL, Lu WR, Lv WG, Yang XS, Piek JM, de Kroon CD, Lok C, Supernat A, Łapińska-Szumczyk S, Łojkowska A, Żaczek AJ, Jassem J, Tannous BA, Sol N, Post E, Best MG, Kong BH, Xie X, Ma D, Wurdinger T, Guo AY, Gao QL. Abstract LB168: Platelet RNA signature enables early and accurate detection of ovarian cancer: An intercontinental, biomarker identification study. Cancer Res 2022. [DOI: 10.1158/1538-7445.am2022-lb168] [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: 11/16/2022]
Abstract
Abstract
Background: Morpho-physiological alternations of platelets provided a rationale to harness RNA sequencing of tumor-educated platelets (TEPs) for preoperative diagnosis of cancer. Timely, accurate, and non-invasive detection of ovarian cancer in women with adnexal masses presents a significant clinical challenge.
Patients and Methods: This intercontinental, hospital-based, diagnostic study included 761 treatment-naïve inpatients with histologically confirmed adnexal masses and 167 healthy controls from nine medical centers (China, n=3; Netherlands, n=5; Poland, n=1) between September 2016 and May 2019. The main outcomes were the performance of TEPs and their combination with CA125 in two Chinese (VC1 and VC2) and the European (VC3) validation cohorts collectively and independently. Exploratory outcome was the value of TEPs in public pan-cancer platelet transcriptome datasets.
Results: The AUCs for TEPs in the combined validation cohort, VC1, VC2, and VC3 were 0.918 (95% CI 0.889-0.948), 0.923 (0.855-0.990), 0.918 (0.872-0.963), and 0.887 (0.813-0.960), respectively. Combination of TEPs and CA125 demonstrated an AUC of 0.922 (0.889-0.955) in the combined validation cohort; 0.955 (0.912-0.997) in VC1; 0.939 (0.901-0.977) in VC2; 0.917 (0.824-1.000) in VC3. For subgroup analysis, TEPs exhibited an AUC of 0.858, 0.859, and 0.920 to detect early-stage, borderline, non-epithelial diseases and 0.899 to discriminate ovarian cancer from endometriosis. Analysis of public datasets suggested that TEPs had potential to detect multiple malignancies (Table 1).
Conclusions: TEPs had robustness, compatibility, and universality for preoperative diagnosis of ovarian cancer since it withstood validations in populations of different ethnicities, heterogeneous histological subtypes, early-stage ovarian cancer as well as other malignancies. However, these observations warrant prospective validations in a larger population before clinical utilities.
Table 1. Performance for TEPs in public pan-cancer datasets. Disease n Healthy Control AUC, area under the curve (95% CI) Women NSCLC (non-small-cell lung cancer) 126 77 0.758 (0.691-0.825) Breast cancer 38 77 0.817 (0.726-0.909) Colorectal cancer 18 77 0.973 (0.945-1.000) Pancreatic cancer 16 77 0.993 (0.981-1.000) Glioblastoma 10 77 0.923 (0.831-1.000) Men NSCLC 119 82 0.746 (0.677-0.815) Colorectal cancer 25 82 0.933 (0.884-0.982) Pancreatic cancer 22 82 0.993 (0.984-1.000) Glioblastoma 19 82 0.981 (0.959-1.000) All NSCLC 245 159 0.774 (0.728-0.820) Colorectal cancer 40 159 0.978 (0.961-0.996) Breast cancer 38 159 0.821 (0.736-0.906) Pancreatic cancer 35 159 0.987 (0.974-0.999) Glioblastoma 35 159 0.931 (0.890-0.972) Hepatobiliary carcinomas 14 159 0.991 (0.978-1.000)
Citation Format: Yue Gao, Chun-Jie Liu, Hua-Yi Li, Xiao-Ming Xiong, Sjors G.j.g. In ‘t Veld, Gui-Ling Li, Jia-Hao Liu, Guang-Yao Cai, Gui-Yan Xie, Shao-Qing Zeng, Yuan Wu, Jian-Hua Chi, Qiong Zhang, Xiao-Fei Jiao, Lin-Li Shi, Wan-Rong Lu, Wei-Guo Lv, Xing-Sheng Yang, Jurgen M.j. Piek, Cornelis D de Kroon, C.a.r. Lok, Anna Supernat, Sylwia Łapińska-Szumczyk, Anna Łojkowska, Anna J. Żaczek, Jacek Jassem, Bakhos A. Tannous, Nik Sol, Edward Post, Myron G. Best, Bei-Hua Kong, Xing Xie, Ding Ma, Thomas Wurdinger, An-Yuan Guo, Qing-Lei Gao. Platelet RNA signature enables early and accurate detection of ovarian cancer: An intercontinental, biomarker identification study [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr LB168.
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Affiliation(s)
- Yue Gao
- 1Department of Obstetrics and Gynecology, Cancer Biology Research Center (Key Laboratory of the Ministry of Education), Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Chun-Jie Liu
- 2Center for Artificial Intelligence Biology, Hubei Bioinformatics & Molecular Imaging Key Laboratory, Key Laboratory of Molecular Biophysics of the Ministry of Education, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, China
| | - Hua-Yi Li
- 1Department of Obstetrics and Gynecology, Cancer Biology Research Center (Key Laboratory of the Ministry of Education), Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Xiao-Ming Xiong
- 1Department of Obstetrics and Gynecology, Cancer Biology Research Center (Key Laboratory of the Ministry of Education), Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Sjors G.j.g. In ‘t Veld
- 3Department of Neurosurgery, Brain Tumor Center Amsterdam, Amsterdam UMC, VU University Medical Center, Cancer Center Amsterdam, Amsterdam, Netherlands
| | - Gui-Ling Li
- 4Cancer Center, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Jia-Hao Liu
- 1Department of Obstetrics and Gynecology, Cancer Biology Research Center (Key Laboratory of the Ministry of Education), Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Guang-Yao Cai
- 1Department of Obstetrics and Gynecology, Cancer Biology Research Center (Key Laboratory of the Ministry of Education), Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Gui-Yan Xie
- 2Center for Artificial Intelligence Biology, Hubei Bioinformatics & Molecular Imaging Key Laboratory, Key Laboratory of Molecular Biophysics of the Ministry of Education, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, China
| | - Shao-Qing Zeng
- 1Department of Obstetrics and Gynecology, Cancer Biology Research Center (Key Laboratory of the Ministry of Education), Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Yuan Wu
- 1Department of Obstetrics and Gynecology, Cancer Biology Research Center (Key Laboratory of the Ministry of Education), Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Jian-Hua Chi
- 1Department of Obstetrics and Gynecology, Cancer Biology Research Center (Key Laboratory of the Ministry of Education), Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Qiong Zhang
- 2Center for Artificial Intelligence Biology, Hubei Bioinformatics & Molecular Imaging Key Laboratory, Key Laboratory of Molecular Biophysics of the Ministry of Education, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, China
| | - Xiao-Fei Jiao
- 1Department of Obstetrics and Gynecology, Cancer Biology Research Center (Key Laboratory of the Ministry of Education), Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Lin-Li Shi
- 4Cancer Center, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Wan-Rong Lu
- 1Department of Obstetrics and Gynecology, Cancer Biology Research Center (Key Laboratory of the Ministry of Education), Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Wei-Guo Lv
- 5Department of Gynecological Oncology, Women's Hospital, School of Medicine, Zhejiang University, Hangzhou, China
| | - Xing-Sheng Yang
- 6Gynecological Oncology Key Laboratory, Qilu Hospital, Shandong University, Jinan, China
| | - Jurgen M.j. Piek
- 7Department of Obstetrics and Gynecology, Catharina Hospital, Eindhoven, Netherlands
| | - Cornelis D de Kroon
- 8Department of Obstetrics and Gynecology, Leiden University Medical Center, Leiden, Netherlands
| | - C.a.r. Lok
- 9Department of Gynecological Oncology, Center of Gynecologic Oncology Amsterdam, Antoni van Leeuwenhoek, Amsterdam, Netherlands
| | - Anna Supernat
- 10Laboratory of Translational Oncology, Intercollegiate Faculty of Biotechnology, University of Gdańsk and Medical University of Gdańsk, Gdańsk, Poland
| | - Sylwia Łapińska-Szumczyk
- 11Department of Gynecology, Gynecological Oncology and Gynecological Endocrinology, Medical University of Gdańsk, Gdańsk, Poland
| | - Anna Łojkowska
- 11Department of Gynecology, Gynecological Oncology and Gynecological Endocrinology, Medical University of Gdańsk, Gdańsk, Poland
| | - Anna J. Żaczek
- 10Laboratory of Translational Oncology, Intercollegiate Faculty of Biotechnology, University of Gdańsk and Medical University of Gdańsk, Gdańsk, Poland
| | - Jacek Jassem
- 12Department of Oncology and Radiotherapy, Medical University of Gdańsk, Gdańsk, Poland, Gdańsk, Poland
| | - Bakhos A. Tannous
- 13Department of Neurology, Massachusetts General Hospital and Neuroscience Program, Harvard Medical School, Charlestown, MA
| | - Nik Sol
- 14Brain Tumor Center Amsterdam, Department of Neurology, Amsterdam UMC, VU University Medical Center, Cancer Center Amsterdam, Amsterdam, Netherlands
| | - Edward Post
- 3Department of Neurosurgery, Brain Tumor Center Amsterdam, Amsterdam UMC, VU University Medical Center, Cancer Center Amsterdam, Amsterdam, Netherlands
| | - Myron G. Best
- 3Department of Neurosurgery, Brain Tumor Center Amsterdam, Amsterdam UMC, VU University Medical Center, Cancer Center Amsterdam, Amsterdam, Netherlands
| | - Bei-Hua Kong
- 6Gynecological Oncology Key Laboratory, Qilu Hospital, Shandong University, Jinan, China
| | - Xing Xie
- 5Department of Gynecological Oncology, Women's Hospital, School of Medicine, Zhejiang University, Hangzhou, China
| | - Ding Ma
- 1Department of Obstetrics and Gynecology, Cancer Biology Research Center (Key Laboratory of the Ministry of Education), Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Thomas Wurdinger
- 3Department of Neurosurgery, Brain Tumor Center Amsterdam, Amsterdam UMC, VU University Medical Center, Cancer Center Amsterdam, Amsterdam, Netherlands
| | - An-Yuan Guo
- 2Center for Artificial Intelligence Biology, Hubei Bioinformatics & Molecular Imaging Key Laboratory, Key Laboratory of Molecular Biophysics of the Ministry of Education, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, China
| | - Qing-Lei Gao
- 1Department of Obstetrics and Gynecology, Cancer Biology Research Center (Key Laboratory of the Ministry of Education), Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
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Huang ZX, Deng WM, Zheng SL, Guo X, Zeng SQ, Li TW. Magnetic resonance imaging in ankylosing spondylitis: reduction of active sacroiliitis and hip arthritis during treatment with an adalimumab biosimilar. Clin Rheumatol 2021; 40:2099-2101. [PMID: 33559010 DOI: 10.1007/s10067-021-05628-4] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2020] [Revised: 01/11/2021] [Accepted: 01/31/2021] [Indexed: 11/25/2022]
Affiliation(s)
- Zhi-Xiang Huang
- Department of Rheumatology and Immunology, Guangdong Second Provincial General Hospital, No 466 Xingangzhong Road, Guangzhou, 510317, China
- The Second School of Clinical Medicine, Southern Medical University, Guangzhou, China
| | - Wei-Ming Deng
- Department of Rheumatology and Immunology, Guangdong Second Provincial General Hospital, No 466 Xingangzhong Road, Guangzhou, 510317, China
- The Second School of Clinical Medicine, Southern Medical University, Guangzhou, China
| | - Shao-Ling Zheng
- Department of Rheumatology and Immunology, Guangdong Second Provincial General Hospital, No 466 Xingangzhong Road, Guangzhou, 510317, China
- The Second School of Clinical Medicine, Southern Medical University, Guangzhou, China
| | - Xin Guo
- Department of Rheumatology and Immunology, Guangdong Second Provincial General Hospital, No 466 Xingangzhong Road, Guangzhou, 510317, China
- The Second School of Clinical Medicine, Southern Medical University, Guangzhou, China
| | - Shao-Qing Zeng
- The Second School of Clinical Medicine, Southern Medical University, Guangzhou, China
- Department of Medical Imaging, Guangdong Second Provincial General Hospital, Guangzhou, China
| | - Tian-Wang Li
- Department of Rheumatology and Immunology, Guangdong Second Provincial General Hospital, No 466 Xingangzhong Road, Guangzhou, 510317, China.
- The Second School of Clinical Medicine, Southern Medical University, Guangzhou, China.
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5
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Gao Y, Cai GY, Fang W, Li HY, Wang SY, Chen L, Yu Y, Liu D, Xu S, Cui PF, Zeng SQ, Feng XX, Yu RD, Wang Y, Yuan Y, Jiao XF, Chi JH, Liu JH, Li RY, Zheng X, Song CY, Jin N, Gong WJ, Liu XY, Huang L, Tian X, Li L, Xing H, Ma D, Li CR, Ye F, Gao QL. Machine learning based early warning system enables accurate mortality risk prediction for COVID-19. Nat Commun 2020; 11:5033. [PMID: 33024092 PMCID: PMC7538910 DOI: 10.1038/s41467-020-18684-2] [Citation(s) in RCA: 148] [Impact Index Per Article: 37.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2020] [Accepted: 08/27/2020] [Indexed: 01/08/2023] Open
Abstract
Soaring cases of coronavirus disease (COVID-19) are pummeling the global health system. Overwhelmed health facilities have endeavored to mitigate the pandemic, but mortality of COVID-19 continues to increase. Here, we present a mortality risk prediction model for COVID-19 (MRPMC) that uses patients' clinical data on admission to stratify patients by mortality risk, which enables prediction of physiological deterioration and death up to 20 days in advance. This ensemble model is built using four machine learning methods including Logistic Regression, Support Vector Machine, Gradient Boosted Decision Tree, and Neural Network. We validate MRPMC in an internal validation cohort and two external validation cohorts, where it achieves an AUC of 0.9621 (95% CI: 0.9464-0.9778), 0.9760 (0.9613-0.9906), and 0.9246 (0.8763-0.9729), respectively. This model enables expeditious and accurate mortality risk stratification of patients with COVID-19, and potentially facilitates more responsive health systems that are conducive to high risk COVID-19 patients.
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Affiliation(s)
- Yue Gao
- National Medical Center for Major Public Health Events, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430000, China
- Department of Gynecology and Obstetrics, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430000, China
| | - Guang-Yao Cai
- National Medical Center for Major Public Health Events, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430000, China
- Department of Gynecology and Obstetrics, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430000, China
| | - Wei Fang
- GNSS Research Center, Wuhan University, Wuhan, 430079, China
| | - Hua-Yi Li
- National Medical Center for Major Public Health Events, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430000, China
- Department of Gynecology and Obstetrics, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430000, China
| | - Si-Yuan Wang
- National Medical Center for Major Public Health Events, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430000, China
- Department of Gynecology and Obstetrics, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430000, China
| | - Lingxi Chen
- City University of Hong Kong Shenzhen Research Institute, Shenzhen, 518000, China
| | - Yang Yu
- National Medical Center for Major Public Health Events, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430000, China
- Department of Gynecology and Obstetrics, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430000, China
| | - Dan Liu
- National Medical Center for Major Public Health Events, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430000, China
- Department of Gynecology and Obstetrics, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430000, China
| | - Sen Xu
- National Medical Center for Major Public Health Events, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430000, China
- Department of Gynecology and Obstetrics, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430000, China
| | - Peng-Fei Cui
- National Medical Center for Major Public Health Events, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430000, China
- Department of Gynecology and Obstetrics, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430000, China
| | - Shao-Qing Zeng
- National Medical Center for Major Public Health Events, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430000, China
- Department of Gynecology and Obstetrics, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430000, China
| | - Xin-Xia Feng
- Department of Gastroenterology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430000, China
| | - Rui-Di Yu
- National Medical Center for Major Public Health Events, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430000, China
- Department of Gynecology and Obstetrics, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430000, China
| | - Ya Wang
- National Medical Center for Major Public Health Events, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430000, China
- Department of Gynecology and Obstetrics, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430000, China
| | - Yuan Yuan
- National Medical Center for Major Public Health Events, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430000, China
- Department of Gynecology and Obstetrics, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430000, China
| | - Xiao-Fei Jiao
- National Medical Center for Major Public Health Events, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430000, China
- Department of Gynecology and Obstetrics, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430000, China
| | - Jian-Hua Chi
- National Medical Center for Major Public Health Events, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430000, China
- Department of Gynecology and Obstetrics, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430000, China
| | - Jia-Hao Liu
- National Medical Center for Major Public Health Events, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430000, China
- Department of Gynecology and Obstetrics, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430000, China
| | - Ru-Yuan Li
- National Medical Center for Major Public Health Events, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430000, China
- Department of Gynecology and Obstetrics, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430000, China
| | - Xu Zheng
- National Medical Center for Major Public Health Events, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430000, China
- Department of Gynecology and Obstetrics, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430000, China
| | - Chun-Yan Song
- National Medical Center for Major Public Health Events, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430000, China
- Department of Gynecology and Obstetrics, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430000, China
| | - Ning Jin
- National Medical Center for Major Public Health Events, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430000, China
- Department of Gynecology and Obstetrics, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430000, China
| | - Wen-Jian Gong
- National Medical Center for Major Public Health Events, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430000, China
- Department of Gynecology and Obstetrics, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430000, China
| | - Xing-Yu Liu
- National Medical Center for Major Public Health Events, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430000, China
- Department of Gynecology and Obstetrics, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430000, China
| | - Lei Huang
- Department of Obstetrics and Gynecology, The Central Hospital of Wuhan, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Xun Tian
- Department of Obstetrics and Gynecology, The Central Hospital of Wuhan, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Lin Li
- Department of Obstetrics and Gynecology, Xiangyang Central Hospital, Affiliated Hospital of Hubei University of Arts and Science, Xiangyang, Hubei, China
| | - Hui Xing
- Department of Obstetrics and Gynecology, Xiangyang Central Hospital, Affiliated Hospital of Hubei University of Arts and Science, Xiangyang, Hubei, China
| | - Ding Ma
- National Medical Center for Major Public Health Events, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430000, China
- Department of Gynecology and Obstetrics, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430000, China
| | - Chun-Rui Li
- Department of Hematology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430000, China
| | - Fei Ye
- Department of Neurosurgery, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430000, China.
| | - Qing-Lei Gao
- National Medical Center for Major Public Health Events, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430000, China.
- Department of Gynecology and Obstetrics, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430000, China.
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6
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Abstract
Based on the principle of Joinpoint regression (JPR) model and the additivity of Poisson distribution, this paper constructed a JPR model for series cumulative data. The notifiable incidence number of dengue fever cases per week and weekly cumulative data in Guangdong province from 2008 to 2017 were analyzed, using (mean squared errors) MSE and (mean absolute percentage error) MAPE to evaluate different models. Except for 2015, the MSE and MAPE produced from the logarithmic linear JPR model based on weekly cumulative incidence number were smaller than those based on the weekly data. The fitting accuracy of JPR model for series cumulative data for trend analysis had been improved significantly. This model could be applied to the analysis of the trend change and the prediction of staged cumulative incidence.
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Affiliation(s)
- S Q Zeng
- Guangdong Provincial Institute of Public Health/Guangdong Provincial Center for Disease Control and Prevention, Guangzhou 511430, China
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7
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Zeng SQ, Wu YQ. [Update on the cardioprotective role of heat shock proteins inducer,geranylgeranylacetone]. Zhonghua Xin Xue Guan Bing Za Zhi 2016; 44:1059-1063. [PMID: 28056240 DOI: 10.3760/cma.j.issn.0253-3758.2016.12.015] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
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Abstract
The newly developed scientific complementary metal oxide semiconductor (sCMOS) cameras are capable of realizing fast single molecule localization microscopy without sacrificing field-of-view, benefiting from their readout speed which is significantly higher than that of conventional charge-coupled device (CCD) cameras. However, the poor image uniformity (suffered from fixed pattern noise, FPN) is a major obstruction for widespread use of sCMOS cameras in single molecule localization microscopy. Here we present a quantitative investigation on the effects of FPN on single molecule localization microscopy via localization precision and localization bias. We found that FPN leads to almost no effect on localization precision, but introduces a certain amount of localization bias. However, for a commercial Hamamatsu Flash 4.0 sCMOS camera, such localization bias is usually <2 nm and thus can be neglected for most localization microscopy experiments. This study addresses the FPN concern which worries researchers, and thus will promote the application of sCMOS cameras in single molecule localization microscopy.
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Affiliation(s)
- F Long
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology, Wuhan 430074, China.
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9
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Wang ZL, Hao J, Chan HLW, Law GL, Wong WT, Wong KL, Murphy MB, Su T, Zhang ZH, Zeng SQ. Simultaneous synthesis and functionalization of water-soluble up-conversion nanoparticles for in-vitro cell and nude mouse imaging. Nanoscale 2011; 3:2175-2181. [PMID: 21437348 DOI: 10.1039/c1nr10090d] [Citation(s) in RCA: 65] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/30/2023]
Abstract
Water-solubility and biocompatibility are prerequisites for rare-earth up-converting nanophosphors applied to biological imaging. In this work, we have developed a facile and one-step synthesis technique, through which water-soluble NaYF(4): Yb(3+), Er(3+) nanoparticles (NPs) with functional groups including 3-mercaptopropionic acid, 6-aminocaproic acid and poly(ethylene glycol)methyl ether on their surface can be directly prepared without any further surface treatment. Some inorganic salts will be selected as starting materials, water and some low toxic organic agents have been used as reaction media, which differs from earlier works. Structural and up-converting fluorescence are characterized by a variety of techniques. Cell uptake and in-vitro imaging of the as-synthesized NPs have been investigated using a multiphoton con-focal laser scanning microscope with a near-infrared excitation source. Internalization of the bare and functionalized NPs in human lung carcinoma A549 and human cervical carcinoma HeLa cells are studied at a nanoparticle loading of 10 µg mL(-1) over an exposure period from 30 min to 24 h. The cytotoxicity of modified NPs in HeLa cells is found to be low. In addition, the feasibility of the NPs in animal imaging has been demonstrated by subcutaneously injecting these NPs into nude mouse. The results indicated that our directly synthesized NPs coated with various functional groups are promising as bio-imaging agents due to their easy uptake, long lasting, low cytotoxicity, emissive in various human carcinoma cell lines and small animals through up-conversion with near-infrared excitation.
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Affiliation(s)
- Zhen-Ling Wang
- Department of Applied Physics and Materials Research Centre, The Hong Kong Polytechnic University, Hung Hom, Hong Kong, PR China
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10
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Jiang GH, Zeng SQ, Tian JZ, Lin CL, Zhang LY, Zhong BL, Liang LB. [Correlation analysis between multi-slice CT perfusion imaging and microvessel density in ovarian tumors]. Nan Fang Yi Ke Da Xue Xue Bao 2009; 29:2197-2200. [PMID: 19923065] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 05/28/2023]
Abstract
OBJECTIVE To analyze the correlation between the perfusion data and microvessel density (MVD) in ovarian tumors, and investigate the hemodynamic features of the tumors in terms of anatomy and functional CT imaging. METHODS Six patients with surgically confirmed benign ovarian tumors and 6 with malignant ovarian tumors underwent multi-slice CT perfusion imaging to acquire the perfusion parameters including perfusion, PEI, TTP, BV peak enhancement image(PEI), time to peak(TTP) and blood volume(BV). The tumors were stained and counted by Immunohistochemical staining of the microvessels in the tumor was performed to detect the MVD. RESULTS s The time-density curves of the benign ovarian tumors increased slowly, reaching the peak at 40 s; the curves of the malignant tumors rose rapidly and continuously and reached the peak at 25 s. The differences in the perfusion data (PEI, TTP, BV) were statistically significant between the benign and malignant tumors (P<0.05). The MVD of the malignant tumors was significantly greater than that of the benign tumors (P<0.05). The mean BV of the malignant ovarian tumor was positively correlated to MVD (r=0.786, P<0.05). CONCLUSION Multi-slice spiral CT perfusion imaging can provide accurate enhancement data of the ovarian tumors and helps in the diagnosis and differential diagnosis of the ovarian tumors by presenting the changes of the hemodynamic features in the tumors.
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Affiliation(s)
- Gui-Hua Jiang
- Department of Imaging Diagnosis, Second People's Hospital of Guangdong Province, Guangzhou 510317, China.
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11
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Zeng SQ, Halkosalo A, Salminen M, Szakal ED, Puustinen L, Vesikari T. One-step quantitative RT-PCR for the detection of rotavirus in acute gastroenteritis. J Virol Methods 2008; 153:238-40. [PMID: 18765254 DOI: 10.1016/j.jviromet.2008.08.004] [Citation(s) in RCA: 136] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2008] [Revised: 08/01/2008] [Accepted: 08/06/2008] [Indexed: 11/28/2022]
Abstract
The standard diagnosis of rotavirus gastroenteritis is based on the demonstration of rotavirus antigen in stools using an enzyme immunoassay (EIA). In this study, a one-step quantitative RT-PCR (Q-PCR) was used for sensitive detection of rotavirus in diarrheal stools. The primers and TaqMan probe for the Q-PCR were selected from a highly conserved region of the non-structural protein 3 (NSP3) of rotavirus. After validation, the test was applied to study rotavirus EIA positive (N=25) and EIA negative (N=143) stool specimens from cases of acute gastroenteritis of all degrees of severity in a prospective follow-up cohort of infants from 2 months to 2 years of age. Q-PCR detected all 25 EIA positive rotavirus antigens and seven additional cases that were rotavirus EIA negative, i.e. 28% more rotavirus positive cases than identified by EIA. It is concluded that Q-PCR using primers targeted at NSP3 is a rapid and sensitive method for diagnosing acute rotavirus gastroenteritis.
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Affiliation(s)
- S Q Zeng
- Vaccine Research Center, University of Tampere, Medical School, Vaccine Research Center, Biokatu 10, FIN-33520 Tampere, Finland.
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Abstract
BACKGROUND Sapporo-like viruses (SLVs) occur worldwide, but there is limited information about the SLV-associated gastroenteritis outside Japan. METHODS Stool specimens from 1,432 episodes of gastroenteritis that occurred in children between 2 months and 2 years of age during a rotavirus vaccine trial (776 episodes in placebo-vaccinated and 656 in rotavirus-vaccinated infants) were examined for SLVs using a reverse transcription-PCR assay. The reverse transcription-PCR took advantage of new primers specific for Sapporo virus genetic clusters I, II and III; SV/SV82 (SV/Sapporo virus 82); SV/Lond92 (SV/ London 92); and SV/PV (Parkville virus). RESULTS SLVs were detected in association with 132 (9.2%) of all episodes; in 80 (5.6%) episodes SLV was the only gastroenteritis virus detected. The epidemic season of SLVs peaked from March to May concurrently with rotaviruses and astroviruses and overlapping withNorwalk-like viruses. Clinically SLV gastroenteritis was characterized by a mild diarrheal disease, being sharply different from the Norwalk-like virus-associated "winter vomiting disease." Rotavirus vaccination did not have any effect on the number of SLV episodes, but the intensity and duration of SLV-associated diarrhea were reduced in rotavirus-vaccinated children compared with placebo-vaccinated children (P = 0.0008). CONCLUSIONS SLVs are common causative agents of acute gastroenteritis in young Finnish children. SLV disease is characterized by diarrhea, which is usually mild but can be severe. By an unknown mechanism rotavirus vaccine seems to reduce the severity of SLV-associated diarrhea.
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Affiliation(s)
- X L Pang
- Department of Virology, University of Tampere, Medical School, Finland
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Chen WG, Li PC, Luo QM, Zeng SQ, Hu B. Hemodynamic assessment of ischemic stroke with near-infrared spectroscopy. Space Med Med Eng (Beijing) 2000; 13:84-9. [PMID: 11543057] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 02/21/2023]
Abstract
OBJECTIVE To validate near-infrared cerebral topography (NCT) as a practical tool in tracing the regional hemodynamic changes during normal ischemic stroke model of rat. METHOD Middle cerebral artery occlusion (MCAO) and photosensitizer induced intracranial infarction model of rat were established. The geometric shape and infarction area were measured by NCT, functional magnetic resonance imaging (fMRI), and TTC stained anatomical imaging techniques. RESULT In photosensitizer induced infarction model, the correlation between anatomical infarct area and NCT image area for infarct focus were r = 0.897 for 24 h group (P < 0.05) and r = 0.906 for 2 months group (P < 0.01), respectively. The correlation between anatomical infarction area and NCT image area for infarct focus were r = 0.820 for normothermia group (P < 0.05) and r = 0.851 for hypothermia group (P < 0.05), respectively. The correlation between fMRI and NCT image area for infarction focus were r = 0.874 for normothermia group (P < 0.05) and r = 0.782 for hypothermia group (P < 0. 05), respectively. CONCLUSION Measurement with NCT for infarction focus matched well with fMRI and anatomic sample in rats. NCT technique might be a practical tool for short-term prediction of stroke and the rehabilitation after stroke in real time.
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Affiliation(s)
- W G Chen
- Institute of Biomedical Photonics, Huazhong University of Science & Technology, Wuhan, China
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14
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Li PC, Gong H, Yang JJ, Zeng SQ, Luo QM, Guan LC. Left prefrontal cortex activation during semantic encoding accessed with functional near infrared imaging. Space Med Med Eng (Beijing) 2000; 13:79-83. [PMID: 11543056] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 02/21/2023]
Abstract
OBJECTIVE To investigate the left prefrontal lobe activation during semantic and non-semantic encoding tasks with functional near-infrared imaging (fNIRI) technique. METHOD 22 healthy subjects were assigned semantic encoding and non-semantic encoding tasks. During semantic encoding tasks, subjects were asked to make a meaningful sentence including two unrelated Chinese word pairs, while during non-semantic encoding task they were asked to judge whether the two Chinese word pairs had the same morphological structure or not. Light intensity of two wavelengths (760 nm and 850 nm) diffused through skull and left prefrontal lobe were real-time recorded and used to reconstruct the brain activation image during the experiment. RESULT With the fNIRI, significant activations were observed in the left inferior prefrontal cortex (Brodmann' areas 45 and 47) during the two tasks, but the evoked activations were more significant for semantic than non-semantic task. These observations were consistent with the results reported by others with functional megnetic resonance imaging (fMRI) and positron-emission tomography PET. CONCLUSION The results suggest that fNIRI provides an important, non-invasive way to map the prefrontal activation during cognitive tasks.
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Affiliation(s)
- P C Li
- Institute of Biomedical Photonics, Huazhong University of Science and Technology, Wuhan
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15
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Abstract
PURPOSE Differentiation of recurrent nasopharyngeal carcinoma (NPC) from radiation fibrosis using conventional diagnostic methods can be difficult. The authors prospectively studied patients with NPC to determine the efficacy of Tc-99m MIBI scintigraphy in detecting the primary, residual, and recurrent tumors. MATERIALS AND METHODS The authors performed Tc-99m MIBI SPECT studies of the head and neck and whole-body scans on 21 healthy adult volunteers and 43 patients with NPC before (n = 26) or after (n = 17) radiotherapy. The images were qualitatively assessed by comparing the nasopharyngeal uptake to scalp radioactivity. MIBI uptake index was calculated as a ratio of mean counts per pixel in the normal nasopharynx or tumor to mean counts per pixel in the scalp. RESULTS There was significantly higher uptake of Tc-99m MIBI by NPC than normal nasopharynx and radiation fibrosis (P < .05). The authors determined the optimum cutoff MIBI uptake index value of 1.3 with a sensitivity of 97%, a specificity of 100%, a positive predictive value of 100%, a negative predictive value of 96%, and an accuracy of 98% for diagnosing NPC. CONCLUSION This study suggests that Tc-99m MIBI SPECT is useful for detecting primary NPC and for differentiating residual or recurrent tumor from radiation fibrosis. The authors propose the cutoff MIBI uptake index value of 1.3 for diagnosing NPC.
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Affiliation(s)
- M H Pui
- Department of Nuclear Medicine, First Affiliated Hospital of Sun Yat-Sen University of Medical Sciences, Guangzhou, Peoples' Republic of China
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16
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Abstract
Intestinal lymphangiectasia is a common cause of protein-losing enteropathy characterized by diarrhea, generalized edema, enteric protein loss, hypoproteinemia, and lymphopenia. Diagnosis is based on demonstration of enteric protein loss and characteristic small bowel mucosal histology. Various imaging modalities including barium studies, computed tomography, and lymphangiography have had limited clinical use. The authors report a case of intestinal lymphangiectasia in which Tc-99m dextran lymphoscintigraphy played a significant role in the patient management.
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Affiliation(s)
- T C Yueh
- Department of Nuclear Medicine, First Affiliated Hospital of Sun Yat-Sen, University of Medical Sciences, Guangzhou, Peoples Republic of China
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17
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Abstract
From January 1963 to December 1989, 1585 consecutive cases of retinal detachment were operated with homologous skin as buckling material, the rate of operative success being 91.6%. Human skin is easy to obtain, sterilize and preserve; because of its appropriate thickness and firm but elastic consistency it satisfactorily meets the need to produce sufficient height and to maintain necessary duration of the scleral buckling; in addition to a high rate of reattachment, homologous skin implantation was well tolerated, very rarely rejected (0.06%) and infected (0.25%), and no late complications occurred. Therefore, the authors prefer using human skin as buckling agent rather than conventional synthetic material, e.g., silicon sponge etc.
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Affiliation(s)
- S Q Zeng
- Abteilung für Augenheilkunde, Xiehe Hospital, Tongji Medizinische Universität, Wuhan
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18
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Zeng SQ, Hu CZ, Li JP, Wei HR, Lu YS, Wu ZB, Wang HJ. A consecutive ultrastructural study of action of homoharringtonine on fibroblasts in vitro. Ann Ophthalmol 1991; 23:337-41. [PMID: 1741606] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
Abstract
The authors studied the effect of homoharringtonine (Hh), a semisynthetic Chinese herb, on the ultrastructure of in vitro cultured human conjunctival fibroblasts (HFb) and its mechanism of action. Preliminary results showed that the main characteristics of HFb damage caused by Hh were invagination of the nuclear membrane, aggregation and margination of chromatin, and massive vacuolization of the cytoplasm. In addition, Hh can inhibit the secretion of collagen fibers and the synthesis of microfilaments. Therefore, Hh might be an effective agent in the prevention and treatment of proliferative eye disorders.
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Affiliation(s)
- S Q Zeng
- Department of Ophthalmology, Union Hospital, Tongji Medical University, Wuhan, People's Republic of China
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Li JP, Hu CZ, Zeng SQ, Ren JM, Wei HR. Inhibition of intraocular proliferation by homoharringtonine. An experimental study. Graefes Arch Clin Exp Ophthalmol 1988; 226:367-70. [PMID: 3169589 DOI: 10.1007/bf02172969] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/04/2023] Open
Abstract
Homoharringtonine, an alkaloid indigenous to China, was studied for its effect on fibroblast growth in cell culture and on intraocular proliferation produced in rabbits by injecting homologous fibroblasts into the vitreous. The results demonstrate that homoharringtonine reduced the cell growth by 50% at a concentration of 0.005 mg/l in vitro, significantly inhibited vitreous proliferation, and prevented the occurrence of retinal detachment in vivo. Light and electron microscopy revealed no ocular toxicity in drug-treated eyes. Homoharringtonine may be of considerable value in the prevention and treatment of intraocular proliferation in patients.
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Affiliation(s)
- J P Li
- Department of Ophthalmology, Union Hospital, Tongji Medical University, Wuhan, People's Republic of China
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Wang CZ, Li A, Zhu PF, Yang ZC, Gao JY, Zeng SQ, Wang D, An N. Dynamic changes of lung lymph flow and the release of lysosomal enzyme from the lungs after severe steam inhalation injury in goats. Burns 1986; 12:415-21. [PMID: 3768754 DOI: 10.1016/0305-4179(86)90037-9] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/07/2023]
Abstract
Chronic lung lymph fistulae were produced in six goats according to Winn's and Stothert's methods with our modification to define the pathophysiology of pulmonary oedema after severe steam inhalation injury. Arterial blood gas, lung lymph flow (QLym), lymph/plasma total protein concentration ratio (L/P), and beta-glucuronidase (beta-G) in plasma and lung lymph were monitored for 24 h post-injury. The pathological changes in the lung tissues were also determined at the end of the study. It was found that directly after injury, QLym increased steadily to a peak value at 6 h, followed by declining values at 18 and 24 h. L/P decreased promptly during the 60 min after injury and then also steadily increased to a peak value at 4 h (P less than 0.05). A significant increase in plasma beta-G was only observed at 4 h post-burn. However, lung lymph beta-G activities and lymph beta-G transport increased immediately after injury, reaching a peak at 4 h (5 and 12 times above baseline values, respectively, P less than 0.01). Significant hypoxaemia and hypocapnia occurred at 2 h post-burn and deteriorated progressively throughout the study. There were obvious pulmonary interstitial and alveolar oedema microscopically. This study demonstrates that the increase in transvascular fluid and protein flux after steam inhalation injury is mainly due to increased pulmonary microvascular permeability. Nevertheless, a hydrostatic pressure effect can not be completely excluded, especially in the first hour post-burn. Lysosomal enzyme release is considered to be one of the important factors which damage lung microvascular elements and induce an increase in their permeability.
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Zeng SQ. [Carcinoma of the meibomian gland, report of 15 cases (author's transl)]. Zhonghua Yan Ke Za Zhi 1980; 16:146-7. [PMID: 6775909] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 01/21/2023]
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