1.Habitat radiomics model in predicting the early therapeutic efficacy of hepatic arterial infusion chemotherapy combined with targeted therapy or immunotherapy for advanced hepatocellular carcinoma: a multi-center retrospective study
Mingsong WU ; Zenglong QUE ; Guanhui LI ; Jie LONG ; Yuxin TANG ; Hao ZHONG ; Shujie LAI ; Qixian YAN ; Jun WANG ; Xiang LAN ; Liangzhi WEN
Chinese Journal of Digestion 2025;45(2):89-99
Objective:To develop habitat radiomics models to predict early treatment responses to the hepatic arterial infusion chemotherapy (HAIC) combined with targeted therapy or immunotherapy in advanced hepatocellular carcinoma (HCC) patients, and to guide clinical diagnosis and treatment.Methods:From October 2021 to Decemeber 2023, at Army Characteristic Medical Center of PLA (Chongqing Daping Hospital) and the First Affiliated Hospital of Chongqing Medical University, 94 patients with advanced HCC who received HAIC combined with targeted therapy or immunotherapy were retrospectively enrolled. According to the treatment results, the patients were divided into response group and non-response group. Univariate and multivariate logistic regression were performed to analyze the clinical data of the patients. Based on contrast-enhanced CT images, tumor habitats were delineated and habitat features were extracted with k-means clustering, and the imaging features of arterial and venous phases were also extracted. The least absolute shrinkage and selection operator (LASSO) was used for dimensionality reduction. Feature selection was performed using LASSO to reduce dimensions, and then the selected features were further refined through stepwise logistic regression analysis.Binary logistic regression models were conducted to develop the habitat radiomics model, arterial phase radiomics model (APRM), venous phase radiomics model (VPRM), clinical data model, as well as the combination of radiomics model and clinical data model to predict early treatment (after 2 treatment cycles) response. Receiver operating characteristic curves (ROC) were plotted, and model performance was evaluated by the area under the curve (AUC), calibration curves, and decision curve. The models were validated through Bootstrap methods (1 000 times). DeLong test was used to compare AUC values.Results:The results of cluster analysis identified 3 characteristic habitats in HCC imaging: low-, medium-, and high-enhancement tumor habitats. The proportion of high-enhancement habitats was higher than that in the non-response group. A predictive model was established based on the proportions of these 3 habitats. Based on the proportion of low-, medium-, and high-enhancement habitats within the tumor, a habitat radiomics model was constructed. After LASSO selection and logistic regression analysis, 3 arterial phase and 3 venous phase radiomic features were selected to build the APRM and VPRM, respectively. Logistic regression analysis identified the following factors for the clinical data model: comorbidities ( OR=0.275, P=0.031), maximum tumor diameter ( OR=1.149, P=0.019), red blood cell count ( OR=0.463, P=0.022), alpha fetoprotein >400 μg/L ( OR=3.452, P=0.017), and tyrosine kinase inhibitor therapy ( OR=3.072, P=0.048). Among the single predictive model′s comparison, the AUC of habitat radiomics model was 0.860 (95% confidence interval(95% CI): 0.789 to 0.932), while those of the APRM、VPRM and clinical data model were 0.850 (95% CI: 0.773 to 0.926), 0.855 (95% CI: 0.782 to 0.928), and 0.774 (95% CI: 0.681 to 0.867), respectively, and there were no statistically significant among these models (all P>0.05). Among the combination models, the AUC of the habitat rediomic-clinical data combination model was 0.881 (95% CI: 0.814 to 0.947); the AUC of arterial phase rediomic-clinical data combination model was 0.897 (95% CI: 0.833 to 0.961); and the AUC of venous phase rediomic-clinical data combination model was 0.888 (95% CI: 0.826 to 0.951), but there were no statistically significant among the 3 models (all P>0.05). The calibration curve showed that the habitat rediomic-clinical data combination model had the most accurate predictive probability. Internal validation showed that the AUC of habitat rediomic-clinical data combination model was 0.848 (95% CI: 0.772 to 0.922), and the predictive performance was better than that of the clinical-data model (0.733 (95% CI: 0.670 to 0.863)). Conclusion:The habitat radiomics model based on enhanced CT can effectively predict early treatment responses to the HAIC combined with targeted therapy or immunotherapy in advanced HCC patients, which provides theoretical basis for individualized treatment in advanced HCC.
2.Analysis and policy recommendations of Healthy Zhejiang assessment results based on Non-parametric Tests
Wen-jie LAI ; Jia-huan WAN ; Qian HAO ; Qing SHEN
Chinese Journal of Health Policy 2025;18(1):28-33
Objective:To analyze the results of Healthy Zhejiang assessment and reveal the weakness in the process of Healthy Zhejiang construction,in order to propose suggestions for promoting the high-quality of Healthy Zhejiang construction.Methods:Descriptive statistics were used to analyze the points-deduction rates of the assessment indicators of Healthy Zhejiang in 90 counties(county-level cities and districts)of Zhejiang Province and non-parametric tests were used to analyze the results of Healthy Zhejiang assessment from 2020 to 2023.Results:The overall effect of Healthy Zhejiang construction from 2020 to 2023 was remarkable,but there were inadequacies and weakness in four aspects:residents'proactive health awareness,primary healthcare service capacity,healthy living environments,and the construction and management of sports facilities.Conclusions:It is still necessary to formulate multifaceted health education program,strengthen the construction of the primary healthcare service system,improve the collection,transportation and disposal system of rural domestic waste,and continuously promote the high-quality development of public sports service.
3.Effects of fangchinoline derivative LYY-32 on biological properties of BLM DNA helicase
Wang-ming ZHANG ; Qin-ying FENG ; Xiao-yu SONG ; Xin-zhong ZHOU ; Juan LU ; Wan-qing XIE ; Zhi-wen LAI ; Wei-dong PAN ; Jie-lin LIU
Chinese Pharmacological Bulletin 2025;41(9):1680-1686
Aim To investigate the effects of the fangchinoline derivative LYY-32 on the biological prop-erties of the BLM642-1290 DNA helicase,in order to lay a foundation for further research on its antitumor activity.Methods Fluorescence polarization assay,malachite green-phosphate and ammonium molybdate colorime-try,and fluorescein-labeled DNA gel electrophoresis experiments were conducted to study the effects of fangchinoline derivative LYY-32 on the DNA binding activity,ATPase activity,and DNA unwinding activity of BLM642-1290 DNA helicase.The effects of LYY-32 on the DNA unwinding activity of DNA helicase in cells were studied using fluorescent techniques and time-lapse microscopy.Ultraviolet spectral scanning was used to investigate the effects of LYY-32 on the confor-mation of the BLM642-1290 DNA helicase.Results At a concentration of 10 μmol·L-1,the inhibition rate of LYY-32 on BLM642-1290 DNA helicase binding to dsDNA was 53.17%.At a concentration of 5 μmol·L-1,the inhibition rate of LYY-32 on BLM642-1290 DNA helicase binding to ssDNA was 88.49%.The inhibition rate of LYY-32 on the ATPase activity of BLM642-1290 DNA he-licase was 89.3%at a concentration of 50 μmol·L-1.When the concentration of LYY-32 exceeded 5μmol·L-1,its inhibition rate on the DNA unwinding activity of BLM642-1290 DNA helicase was 100%.LYY-32 also significantly inhibited the DNA unwinding ac-tivity of DNA helicase in cells.However,LYY-32 had no effect on the conformation of BLM642-1290 DNA heli-case.Conclusion The DNA binding activity,AT-Pase activity,and DNA unwinding activity of BLM642-1290 DNA helicase could be significantly inhibi-ted by the fangchinoline derivative LYY-32.
4.Effects of fangchinoline derivative LYY-32 on biological properties of BLM DNA helicase
Wang-ming ZHANG ; Qin-ying FENG ; Xiao-yu SONG ; Xin-zhong ZHOU ; Juan LU ; Wan-qing XIE ; Zhi-wen LAI ; Wei-dong PAN ; Jie-lin LIU
Chinese Pharmacological Bulletin 2025;41(9):1680-1686
Aim To investigate the effects of the fangchinoline derivative LYY-32 on the biological prop-erties of the BLM642-1290 DNA helicase,in order to lay a foundation for further research on its antitumor activity.Methods Fluorescence polarization assay,malachite green-phosphate and ammonium molybdate colorime-try,and fluorescein-labeled DNA gel electrophoresis experiments were conducted to study the effects of fangchinoline derivative LYY-32 on the DNA binding activity,ATPase activity,and DNA unwinding activity of BLM642-1290 DNA helicase.The effects of LYY-32 on the DNA unwinding activity of DNA helicase in cells were studied using fluorescent techniques and time-lapse microscopy.Ultraviolet spectral scanning was used to investigate the effects of LYY-32 on the confor-mation of the BLM642-1290 DNA helicase.Results At a concentration of 10 μmol·L-1,the inhibition rate of LYY-32 on BLM642-1290 DNA helicase binding to dsDNA was 53.17%.At a concentration of 5 μmol·L-1,the inhibition rate of LYY-32 on BLM642-1290 DNA helicase binding to ssDNA was 88.49%.The inhibition rate of LYY-32 on the ATPase activity of BLM642-1290 DNA he-licase was 89.3%at a concentration of 50 μmol·L-1.When the concentration of LYY-32 exceeded 5μmol·L-1,its inhibition rate on the DNA unwinding activity of BLM642-1290 DNA helicase was 100%.LYY-32 also significantly inhibited the DNA unwinding ac-tivity of DNA helicase in cells.However,LYY-32 had no effect on the conformation of BLM642-1290 DNA heli-case.Conclusion The DNA binding activity,AT-Pase activity,and DNA unwinding activity of BLM642-1290 DNA helicase could be significantly inhibi-ted by the fangchinoline derivative LYY-32.
5.Analysis and policy recommendations of Healthy Zhejiang assessment results based on Non-parametric Tests
Wen-jie LAI ; Jia-huan WAN ; Qian HAO ; Qing SHEN
Chinese Journal of Health Policy 2025;18(1):28-33
Objective:To analyze the results of Healthy Zhejiang assessment and reveal the weakness in the process of Healthy Zhejiang construction,in order to propose suggestions for promoting the high-quality of Healthy Zhejiang construction.Methods:Descriptive statistics were used to analyze the points-deduction rates of the assessment indicators of Healthy Zhejiang in 90 counties(county-level cities and districts)of Zhejiang Province and non-parametric tests were used to analyze the results of Healthy Zhejiang assessment from 2020 to 2023.Results:The overall effect of Healthy Zhejiang construction from 2020 to 2023 was remarkable,but there were inadequacies and weakness in four aspects:residents'proactive health awareness,primary healthcare service capacity,healthy living environments,and the construction and management of sports facilities.Conclusions:It is still necessary to formulate multifaceted health education program,strengthen the construction of the primary healthcare service system,improve the collection,transportation and disposal system of rural domestic waste,and continuously promote the high-quality development of public sports service.
6.Habitat radiomics model in predicting the early therapeutic efficacy of hepatic arterial infusion chemotherapy combined with targeted therapy or immunotherapy for advanced hepatocellular carcinoma: a multi-center retrospective study
Mingsong WU ; Zenglong QUE ; Guanhui LI ; Jie LONG ; Yuxin TANG ; Hao ZHONG ; Shujie LAI ; Qixian YAN ; Jun WANG ; Xiang LAN ; Liangzhi WEN
Chinese Journal of Digestion 2025;45(2):89-99
Objective:To develop habitat radiomics models to predict early treatment responses to the hepatic arterial infusion chemotherapy (HAIC) combined with targeted therapy or immunotherapy in advanced hepatocellular carcinoma (HCC) patients, and to guide clinical diagnosis and treatment.Methods:From October 2021 to Decemeber 2023, at Army Characteristic Medical Center of PLA (Chongqing Daping Hospital) and the First Affiliated Hospital of Chongqing Medical University, 94 patients with advanced HCC who received HAIC combined with targeted therapy or immunotherapy were retrospectively enrolled. According to the treatment results, the patients were divided into response group and non-response group. Univariate and multivariate logistic regression were performed to analyze the clinical data of the patients. Based on contrast-enhanced CT images, tumor habitats were delineated and habitat features were extracted with k-means clustering, and the imaging features of arterial and venous phases were also extracted. The least absolute shrinkage and selection operator (LASSO) was used for dimensionality reduction. Feature selection was performed using LASSO to reduce dimensions, and then the selected features were further refined through stepwise logistic regression analysis.Binary logistic regression models were conducted to develop the habitat radiomics model, arterial phase radiomics model (APRM), venous phase radiomics model (VPRM), clinical data model, as well as the combination of radiomics model and clinical data model to predict early treatment (after 2 treatment cycles) response. Receiver operating characteristic curves (ROC) were plotted, and model performance was evaluated by the area under the curve (AUC), calibration curves, and decision curve. The models were validated through Bootstrap methods (1 000 times). DeLong test was used to compare AUC values.Results:The results of cluster analysis identified 3 characteristic habitats in HCC imaging: low-, medium-, and high-enhancement tumor habitats. The proportion of high-enhancement habitats was higher than that in the non-response group. A predictive model was established based on the proportions of these 3 habitats. Based on the proportion of low-, medium-, and high-enhancement habitats within the tumor, a habitat radiomics model was constructed. After LASSO selection and logistic regression analysis, 3 arterial phase and 3 venous phase radiomic features were selected to build the APRM and VPRM, respectively. Logistic regression analysis identified the following factors for the clinical data model: comorbidities ( OR=0.275, P=0.031), maximum tumor diameter ( OR=1.149, P=0.019), red blood cell count ( OR=0.463, P=0.022), alpha fetoprotein >400 μg/L ( OR=3.452, P=0.017), and tyrosine kinase inhibitor therapy ( OR=3.072, P=0.048). Among the single predictive model′s comparison, the AUC of habitat radiomics model was 0.860 (95% confidence interval(95% CI): 0.789 to 0.932), while those of the APRM、VPRM and clinical data model were 0.850 (95% CI: 0.773 to 0.926), 0.855 (95% CI: 0.782 to 0.928), and 0.774 (95% CI: 0.681 to 0.867), respectively, and there were no statistically significant among these models (all P>0.05). Among the combination models, the AUC of the habitat rediomic-clinical data combination model was 0.881 (95% CI: 0.814 to 0.947); the AUC of arterial phase rediomic-clinical data combination model was 0.897 (95% CI: 0.833 to 0.961); and the AUC of venous phase rediomic-clinical data combination model was 0.888 (95% CI: 0.826 to 0.951), but there were no statistically significant among the 3 models (all P>0.05). The calibration curve showed that the habitat rediomic-clinical data combination model had the most accurate predictive probability. Internal validation showed that the AUC of habitat rediomic-clinical data combination model was 0.848 (95% CI: 0.772 to 0.922), and the predictive performance was better than that of the clinical-data model (0.733 (95% CI: 0.670 to 0.863)). Conclusion:The habitat radiomics model based on enhanced CT can effectively predict early treatment responses to the HAIC combined with targeted therapy or immunotherapy in advanced HCC patients, which provides theoretical basis for individualized treatment in advanced HCC.
7.Chinese expert consensus on blood support mode and blood transfusion strategies for emergency treatment of severe trauma patients (version 2024)
Yao LU ; Yang LI ; Leiying ZHANG ; Hao TANG ; Huidan JING ; Yaoli WANG ; Xiangzhi JIA ; Li BA ; Maohong BIAN ; Dan CAI ; Hui CAI ; Xiaohong CAI ; Zhanshan ZHA ; Bingyu CHEN ; Daqing CHEN ; Feng CHEN ; Guoan CHEN ; Haiming CHEN ; Jing CHEN ; Min CHEN ; Qing CHEN ; Shu CHEN ; Xi CHEN ; Jinfeng CHENG ; Xiaoling CHU ; Hongwang CUI ; Xin CUI ; Zhen DA ; Ying DAI ; Surong DENG ; Weiqun DONG ; Weimin FAN ; Ke FENG ; Danhui FU ; Yongshui FU ; Qi FU ; Xuemei FU ; Jia GAN ; Xinyu GAN ; Wei GAO ; Huaizheng GONG ; Rong GUI ; Geng GUO ; Ning HAN ; Yiwen HAO ; Wubing HE ; Qiang HONG ; Ruiqin HOU ; Wei HOU ; Jie HU ; Peiyang HU ; Xi HU ; Xiaoyu HU ; Guangbin HUANG ; Jie HUANG ; Xiangyan HUANG ; Yuanshuai HUANG ; Shouyong HUN ; Xuebing JIANG ; Ping JIN ; Dong LAI ; Aiping LE ; Hongmei LI ; Bijuan LI ; Cuiying LI ; Daihong LI ; Haihong LI ; He LI ; Hui LI ; Jianping LI ; Ning LI ; Xiying LI ; Xiangmin LI ; Xiaofei LI ; Xiaojuan LI ; Zhiqiang LI ; Zhongjun LI ; Zunyan LI ; Huaqin LIANG ; Xiaohua LIANG ; Dongfa LIAO ; Qun LIAO ; Yan LIAO ; Jiajin LIN ; Chunxia LIU ; Fenghua LIU ; Peixian LIU ; Tiemei LIU ; Xiaoxin LIU ; Zhiwei LIU ; Zhongdi LIU ; Hua LU ; Jianfeng LUAN ; Jianjun LUO ; Qun LUO ; Dingfeng LYU ; Qi LYU ; Xianping LYU ; Aijun MA ; Liqiang MA ; Shuxuan MA ; Xainjun MA ; Xiaogang MA ; Xiaoli MA ; Guoqing MAO ; Shijie MU ; Shaolin NIE ; Shujuan OUYANG ; Xilin OUYANG ; Chunqiu PAN ; Jian PAN ; Xiaohua PAN ; Lei PENG ; Tao PENG ; Baohua QIAN ; Shu QIAO ; Li QIN ; Ying REN ; Zhaoqi REN ; Ruiming RONG ; Changshan SU ; Mingwei SUN ; Wenwu SUN ; Zhenwei SUN ; Haiping TANG ; Xiaofeng TANG ; Changjiu TANG ; Cuihua TAO ; Zhibin TIAN ; Juan WANG ; Baoyan WANG ; Chunyan WANG ; Gefei WANG ; Haiyan WANG ; Hongjie WANG ; Peng WANG ; Pengli WANG ; Qiushi WANG ; Xiaoning WANG ; Xinhua WANG ; Xuefeng WANG ; Yong WANG ; Yongjun WANG ; Yuanjie WANG ; Zhihua WANG ; Shaojun WEI ; Yaming WEI ; Jianbo WEN ; Jun WEN ; Jiang WU ; Jufeng WU ; Aijun XIA ; Fei XIA ; Rong XIA ; Jue XIE ; Yanchao XING ; Yan XIONG ; Feng XU ; Yongzhu XU ; Yongan XU ; Yonghe YAN ; Beizhan YAN ; Jiang YANG ; Jiangcun YANG ; Jun YANG ; Xinwen YANG ; Yongyi YANG ; Chunyan YAO ; Mingliang YE ; Changlin YIN ; Ming YIN ; Wen YIN ; Lianling YU ; Shuhong YU ; Zebo YU ; Yigang YU ; Anyong YU ; Hong YUAN ; Yi YUAN ; Chan ZHANG ; Jinjun ZHANG ; Jun ZHANG ; Kai ZHANG ; Leibing ZHANG ; Quan ZHANG ; Rongjiang ZHANG ; Sanming ZHANG ; Shengji ZHANG ; Shuo ZHANG ; Wei ZHANG ; Weidong ZHANG ; Xi ZHANG ; Xingwen ZHANG ; Guixi ZHANG ; Xiaojun ZHANG ; Guoqing ZHAO ; Jianpeng ZHAO ; Shuming ZHAO ; Beibei ZHENG ; Shangen ZHENG ; Huayou ZHOU ; Jicheng ZHOU ; Lihong ZHOU ; Mou ZHOU ; Xiaoyu ZHOU ; Xuelian ZHOU ; Yuan ZHOU ; Zheng ZHOU ; Zuhuang ZHOU ; Haiyan ZHU ; Peiyuan ZHU ; Changju ZHU ; Lili ZHU ; Zhengguo WANG ; Jianxin JIANG ; Deqing WANG ; Jiongcai LAN ; Quanli WANG ; Yang YU ; Lianyang ZHANG ; Aiqing WEN
Chinese Journal of Trauma 2024;40(10):865-881
Patients with severe trauma require an extremely timely treatment and transfusion plays an irreplaceable role in the emergency treatment of such patients. An increasing number of evidence-based medicinal evidences and clinical practices suggest that patients with severe traumatic bleeding benefit from early transfusion of low-titer group O whole blood or hemostatic resuscitation with red blood cells, plasma and platelet of a balanced ratio. However, the current domestic mode of blood supply cannot fully meet the requirements of timely and effective blood transfusion for emergency treatment of patients with severe trauma in clinical practice. In order to solve the key problems in blood supply and blood transfusion strategies for emergency treatment of severe trauma, Branch of Clinical Transfusion Medicine of Chinese Medical Association, Group for Trauma Emergency Care and Multiple Injuries of Trauma Branch of Chinese Medical Association, Young Scholar Group of Disaster Medicine Branch of Chinese Medical Association organized domestic experts of blood transfusion medicine and trauma treatment to jointly formulate Chinese expert consensus on blood support mode and blood transfusion strategies for emergency treatment of severe trauma patients ( version 2024). Based on the evidence-based medical evidence and Delphi method of expert consultation and voting, 10 recommendations were put forward from two aspects of blood support mode and transfusion strategies, aiming to provide a reference for transfusion resuscitation in the emergency treatment of severe trauma and further improve the success rate of treatment of patients with severe trauma.
8.Wang Wen-jun's experience in the treatment of premature ovarian insufficiency complicated with infertility by integrating traditional Chinese and western medicine
Shu-Hui HUANG ; Li-Li XU ; Lai-Di QIAN ; Min-Jie TANG ; Wen-Jun WANG
Fudan University Journal of Medical Sciences 2024;51(5):784-788
Patients with premature ovarian insufficiency(POI)suffer from a significant decline in ovarian function,which severely affects their fertility.To date,there is no definitive and effective treatment for patients with POI accompanied by infertility.Professor Wang Wen-jun proposed the principles of"integrating Chinese and western medicine,precise medication""treating the root cause,adhering to the treatment rules and adjusting the prescription"and"being cautious of the subtle,preventing the gradual,and treating before changes occur"which have been effective when Chinese and western medicines are used in combination.This article also introduces three cases of patients with POI accompanied by infertility who successfully became pregnant after being treated with Professor Wang Wen-jun's integrated Chinese and western medicine treatment plan,aiming to provide ideas for the integrated treatment of POI accompanied by infertility.
9.Hepatitis C virus infection:surveillance report from China Healthcare-as-sociated Infection Surveillance System in 2020
Xi-Mao WEN ; Nan REN ; Fu-Qin LI ; Rong ZHAN ; Xu FANG ; Qing-Lan MENG ; Huai YANG ; Wei-Guang LI ; Ding LIU ; Feng-Ling GUO ; Shu-Ming XIANYU ; Xiao-Quan LAI ; Chong-Jie PANG ; Xun HUANG ; An-Hua WU
Chinese Journal of Infection Control 2024;23(1):1-8
Objective To investigate the infection status and changing trend of hepatitis C virus(HCV)infection in hospitalized patients in medical institutions,and provide reference for formulating HCV infection prevention and control strategies.Methods HCV infection surveillance results from cross-sectional survey data reported to China Healthcare-associated Infection(HAI)Surveillance System in 2020 were summarized and analyzed,HCV positive was serum anti-HCV positive or HCV RNA positive,survey result was compared with the survey results from 2003.Results In 2020,1 071 368 inpatients in 1 573 hospitals were surveyed,738 535 of whom underwent HCV test,4 014 patients were infected with HCV,with a detection rate of 68.93%and a HCV positive rate of 0.54%.The positive rate of HCV in male and female patients were 0.60%and 0.48%,respectively,with a statistically sig-nificant difference(x2=47.18,P<0.001).The HCV positive rate in the 50-<60 age group was the highest(0.76%),followed by the 40-<50 age group(0.71%).Difference among all age groups was statistically signifi-cant(x2=696.74,P<0.001).In 2003,91 113 inpatients were surveyed.35 145 of whom underwent HCV test,resulting in a detection rate of 38.57%;775 patients were infected with HCV,with a positive rate of 2.21%.In 2020,HCV positive rates in hospitals of different scales were 0.46%-0.63%,with the highest in hospital with bed numbers ranging 600-899.Patients'HCV positive rates in hospitals of different scales was statistically signifi-cant(X2=35.34,P<0.001).In 2020,12 provinces/municipalities had over 10 000 patients underwent HCV-rela-ted test,and HCV positive rates ranged 0.19%-0.81%,with the highest rate from Hainan Province.HCV posi-tive rates in different departments were 0.06%-0.82%,with the lowest positive rate in the department of pedia-trics and the highest in the department of internal medicine.In 2003 and 2020,HCV positive rates in the depart-ment of infectious diseases were the highest,being 7.95%and 3.48%,respectively.Followed by departments of orthopedics(7.72%),gastroenterology(3.77%),nephrology(3.57%)and general intensive care unit(ICU,3.10%)in 2003,as well as departments of gastroenterology(1.35%),nephrology(1.18%),endocrinology(0.91%),and general intensive care unit(ICU,0.79%)in 2020.Conclusion Compared with 2003,HCV positive rate decreased significantly in 2020.HCV infected patients were mainly from the department of infectious diseases,followed by departments of gastroenterology,nephrology and general ICU.HCV infection positive rate varies with gender,age,and region.
10.The Genetic Polymorphism and Structural Analysis of 47 Microhaplotypes in a Jiangsu Changshu Chinese Han Population
Kun-Peng PAN ; Yao-Sen FENG ; Wen-Shuai YU ; Zong-Wei LIU ; Yi-Ren YAO ; Jie ZHAO ; Ke-Lai KANG ; Chi ZHANG ; Le WANG ; Jian WU
Progress in Biochemistry and Biophysics 2024;51(2):423-434
ObjectiveTo investigate the genetic polymorphism and structure of 47 autosomal microhaplotypes in the Han population in Changshu City, Jiangsu Province, and to evaluate the forensic efficiencies and forensic parameters. MethodsThe DNA library of unrelated individual samples was prepared according to MHSeqTyper47 kit manual and sequenced on the MiSeq FGx platform. Microhaplotype genotyping and sequencing depth statistics were processed using MHTyper. The genetic information of samples was then evaluated. The fixation index and genetic distance between the Jiangsu Changshu population and the reference populations in the 1000 Genomes Project phase 3 (1KG) were calculated, and forensic parameters were evaluated. ResultsThe fixation index and genetic distance between the Han population in Changshu, Jiangsu, and the CHB (Han Chinese in Beijing, China) reference population in 1KG were the lowest. The effective allele number (Ae) of each locus is also the closest between the two populations. The combined matching probability (CMP) of the Changshu Han population is close to the 5 populations of the East Asian reference super-population in 1KG, which is 1.25×10-36, and the combined probability of exclusion reached 0.999 999 999 964 1. ConclusionThis study reported the genetic polymorphism and allele frequency of 47 microhaplotypes in a Han population in Changshu City, Jiangsu Province. This information provides a data basis for 47 microhaplotypes in forensic applications. In addition, the polymorphism differences between the 1KG reference population and the Han population in Changshu, Jiangsu were compared, and the genetic structure of 47 microhaplotypes in the Han population in Changshu, Jiangsu was revealed. In general, the reference data of the East Asian super-population in 1KG is more in line with the genetic characteristics of Han population in Changshu, Jiangsu.

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