1.Establishment and evaluation of pendulum-like modified rat abdominal heart heterotopic transplantation model
Hongtao TANG ; Caihan LI ; Xiangyun ZHENG ; Senlin HOU ; Weiyang CHEN ; Zengwei YU ; Yabo WANG ; Dong TIAN ; Qi AN
Organ Transplantation 2025;16(2):280-287
Objective To introduce the modeling method of pendulum-like modified rat abdominal heart heterotopic transplantation model and evaluate the quality of the model. Methods An operator without transplantation experience performed 15 consecutive models, recorded the time of each step, changes in body weight and modified Stanford scores, and calculated the surgical success rate, postoperative 1-week survival rate and technical success rate. Ultrasound examinations was performed in 1 week postoperatively. Results The times for donor heart acquisition, donor heart processing, recipient preparation and transplantation anastomosis were (14.3±1.4) min, (3.5±0.6) min, (13.6±2.1) min and (38.3±5.2) min respectively. The surgical success rate was 87% (13/15), and the survival rate 1 week after operative was 100% (13/13). The improved Stanford score indicated a technical success rate of 92% (12/13), and the postoperative 1-week ultrasound examination showed that grafts with Stanford scores ≥3 had detectable pulsation and blood flow signals. Conclusions The pendulum-like modified rat abdominal heart heterotopic transplantation improved model further optimizes the operational steps with a high success rate and stable quality, may be chosen as a modeling option for basic research in heart transplantation in the future.
3.Classification of bladder cancer based on immune cell infiltration and construction of a risk prediction model for prognosis.
Guicao YIN ; Shengqi ZHENG ; Wei ZHANG ; Xin DONG ; Lezhong QI ; Yifan LI
Journal of Zhejiang University. Medical sciences 2024;():1-11
OBJECTIVES:
To classify bladder cancer based on immune cell infiltration score and to construct a risk assessment model for prognosis of patients.
METHODS:
The transcriptome data and data of breast cancer patients were obtained from the TCGA database. The single sample gene set enrichment analysis was used to calculate the infiltration scores of 16 immune cells. The classification of breast cancer patients was realized by unsupervised clustering, and the sensitivity of patients with different types to immunotherapy and chemotherapy was analyzed. The key modules significantly related to the infiltration of key immune cells were identified by weighted correlation network analysis (WGCNA), and the key genes in the modules were extracted. A risk scoring model and a nomogram for risk assessment of prognosis for bladder cancer patients were constructed and verified.
RESULTS:
The immune cell infiltration scores of normal tissues and tumor tissues were calculated, and B cells, mast cells, neutrophils, T helper cells and tumor infiltrating lymphocytes were determined to be the key immune cells of bladder cancer. Breast cancer patients were clustered into two groups (Cluster 1 and Custer 2) based on immune cell infiltration scores. Compared with patients with Cluster 1, patients with Cluster 2 were more likely to benefit from immunotherapy (P<0.05), and patients with Cluster 2 were more sensitive to Enbeaten, Docetaxel, Cyclopamine, and Akadixin (P<0.05). WGCNA screened out 35 genes related to key immune cells, and 4 genes (GPR171, HOXB3, HOXB5 and HOXB6) related to the prognosis of bladder cancer were further screened by LASSO Cox regression. The areas under the ROC curve (AUC) of the bladder cancer prognosis risk scoring model based on these 4 genes to predict the 1-, 3- and 5-year survival of patients were 0.735, 0.765 and 0.799, respectively. The nomogram constructed by combining risk score and clinical parameters has high accuracy in predicting the 1-, 3-, and 5-year overall survival of bladder cancer patients.
CONCLUSIONS
According to the immune cell infiltration score, bladder cancer patients can be classified. And the bladder cancer prognosis risk scoring model and nomogram based on key immune cell-related genes have high accuracy in predicting the prognosis of bladder cancer patients.
4.Study on Traditional Chinese Medicine Syndrome Characteristics of 1676 Heart Failure Inpatients: A Cross-Sectional Survey Based on Real-World Electronic Medical Record Information
Yi DU ; Zheng LI ; Guanlin YANG ; Shuqi DONG ; Wenshuai HUANG ; Nanxing XIAN ; Puyu GUO ; Jiajie QI ; Bohang CHEN ; Xin XU ; Zhe ZHANG ; Yi YANG
Journal of Traditional Chinese Medicine 2024;65(3):299-307
ObjectiveTo analyse the clinical characteristics of different traditional Chinese medicine (TCM) syndromes in patients with heart failure based on information from electronic medical record. MethodsA cross-sectional study was conducted to collect clinical data of all inpatients with heart failure in the Department of Cardiology, Affiliated Hospital of Liaoning University of Traditional Chinese Medicine from January 1, 2019 to December 31, 2020. A database of clinical TCM data was established to explore the characteristics of clinical data of basic information, syndromes and syndrome element types, and biochemical indexes. The distribution of TCM syndromes and syndrome elements in heart failure patients were also analysed, and the basic information and biochemical indexes of the patients with top 7 different TCM syndrome types were compared. ResultsA total of 1676 inpatients with heart fai-lure were included. The top 7 TCM syndromes of heart failure were syndrome of phlegm turbidity and blood stasis (477 cases, 28.46%), syndrome of qi deficiency and blood stasis (439 cases, 26.19%), syndrome of qi deficiency and blood stasis with water retention (274 cases, 16.35%), syndrome of yang deficiency with water retention (145 cases, 8.65%), syndrome of qi and yin deficiency (104 cases, 6.21%), syndrome of qi and yin deficiency with blood stasis (80 cases, 4.77%), syndrome of heart yang deficiency (59 cases, 3.52%). Among the 1676 patients, 6 syndrome elements accounted for more than 5%. Blood stasis accounted for the highest proportion of TCM syndrome element type (1292 cases, 77.09%), followed by qi deficiency (919 cases, 54.83%), phlegm (498 cases, 29.71%), water retention (434 cases, 25.89%), yang deficiency (215 cases, 12.82%) and yin deficiency (191 cases, 11.40%). Among the 1676 patients, 1308 cases of acute heart failure mainly showed syndrome of phlegm turbidity and blood stasis (386 cases, 29.51%), and 368 of chronic heart fai-lure mainly showed syndrome of qi deficiency and blood stasis (118 cases, 32.07%). Patients with syndrome of phlegm turbidity and blood stasis had the shortest disease duration of 0.3 months, while those with syndrome of heart yang deficiency had the longest disease duration of 15 months. The proportion of syndrome of phlegm turbidity and blood stasis was the highest in patients with heart failure combined with coronary artery disease, the proportion of syndrome of qi deficiency and blood stasis with water retention was the highest in patients with heart failure combined with atrial fibrillation, and the proportion of patients with syndrome of qi deficiency and blood stasis with water retention and syndrome of yang deficiency with water retention in those applying diuretics during the hospital stay was the highest with more than 86%. The different 7 TCM syndromes showed statistically difference in patients with complications including coronary artery disease, old myocardial infarction, atrial fibrillation, pre and post-admission medication usage including intravenous vasodilators, cardiac stimulants, diuretics, and level of blood chloride, blood urea, blood creatinine, blood bicarbonate, blood albumin, and blood total bilirubin (P<0.05). ConclusionThe most common TCM syndromes in patients with heart failure are syndrome of phlegm turbidity and blood stasis and syndrome of qi deficiency and blood stasis. Different TCM syndromes have different characteristics in gender, disease complications, medication before and after admission, and blood indexes.
5.Classification of bladder cancer based on immune cell infiltration and construction of a risk prediction model for prognosis
Guicao YIN ; Shengqi ZHENG ; Wei ZHANG ; Xin DONG ; Lezhong QI ; Yifan LI
Journal of Zhejiang University. Medical sciences 2024;53(1):47-57
Objective:To classify bladder cancer based on immune cell infiltration score and to construct a prognosis assessment model of patients with bladder cancer.Methods:The transcriptome data and clinical data of breast cancer patients were obtained from the The Cancer Genome Atlas(TCGA)database.Single sample gene set enrichment analysis was used to calculate the infiltration scores of 16 immune cells.The classification of breast cancer patients was achieved by unsupervised clustering,and the sensitivity of patients with different types to immunotherapy and chemotherapy was analyzed.The key modules significantly related to the infiltration of key immune cells were identified by weighted correlation network analysis(WGCNA),and the key genes in the modules were identified.A risk scoring model and a nomogram for prognosis assessment of bladder cancer patients were constructed and verified.Results:B cells,mast cells,neutrophils,T helper cells and tumor infiltrating lymphocytes were determined to be the key immune cells of bladder cancer.The patients were clustered into two groups(Cluster 1′ and Custer 2)based on immune cell infiltration scores.Compared with patients with Cluster 1′,patients with Cluster 2 were more likely to benefit from immunotherapy(P<0.05),and patients with Cluster 2 were more sensitive to Enbeaten,Docetaxel,Cyclopamine,and Akadixin(P<0.05).35 genes related to key immune cells were screened out by WGCNA and 4 genes(GPR171,HOXB3,HOXB5 and HOXB6)related to the prognosis of bladder cancer were further screened by LASSO Cox regression.The areas under the ROC curve(AUC)of the bladder cancer prognosis risk scoring model based on these 4 genes to predict the 1-,3-and 5-year survival of patients were 0.735,0.765 and 0.799,respectively.The nomogram constructed by combining risk score and clinical parameters has high accuracy in predicting the 1-,3-,and 5-year overall survival of bladder cancer patients.Conclusions:According to the immune cell infiltration score,bladder cancer patients can be classified.Furthermore,bladder cancer prognosis risk scoring model and nomogram based on key immune cell-related genes have high accuracy in predicting the prognosis of bladder cancer patients.
6.Quantitative research on China's disability rehabilitation policy using policy modeling consistency index model
Tongtong GUO ; Xinyi ZHANG ; Lihong JI ; Zhiwei DONG ; Zongrun LI ; Liduan WANG ; Weiqin CAI ; Qianqian GAO ; Qi JING ; Wengui ZHENG
Chinese Journal of Rehabilitation Theory and Practice 2024;30(6):621-629
Objective To quantitatively analyze and evaluate the content of rehabilitation policy for people with disabilities in China. Methods This study focused on ten national policies of disability rehabilitation issued from 2021 to 2023.It employed text mining techniques to process policy texts and constructed a policy modeling consistency index model for dis-ability rehabilitation policies in China.The relevant policies were evaluated and analyzed quantitatively. Results The disability rehabilitation policies in China were relatively comprehensive in terms of policy transparency,op-erational mechanisms and policy nature.However,there was still a need for optimization in terms of policy per-spectives,target groups,incentive mechanisms,and other aspects. Conclusion The overall quality of disability rehabilitation policy texts at the national level in China is relatively good.There is a need to further enhance the predictability of policy objectives,clarify the responsibilities and division of labor among various departments,and improve policy incentive mechanisms in future policy formulation,which will further refine China's disability rehabilitation policy system and contribute to high-quality develop-ment of the disability cause.
7.National bloodstream infection bacterial resistance surveillance report (2022) : Gram-negative bacteria
Zhiying LIU ; Yunbo CHEN ; Jinru JI ; Chaoqun YING ; Qing YANG ; Haishen KONG ; Haifeng MAO ; Hui DING ; Pengpeng TIAN ; Jiangqin SONG ; Yongyun LIU ; Jiliang WANG ; Yan JIN ; Yuanyuan DAI ; Yizheng ZHOU ; Yan GENG ; Fenghong CHEN ; Lu WANG ; Yanyan LI ; Dan LIU ; Peng ZHANG ; Junmin CAO ; Xiaoyan LI ; Dijing SONG ; Xinhua QIANG ; Yanhong LI ; Qiuying ZHANG ; Guolin LIAO ; Ying HUANG ; Baohua ZHANG ; Liang GUO ; Aiyun LI ; Haiquan KANG ; Donghong HUANG ; Sijin MAN ; Zhuo LI ; Youdong YIN ; Kunpeng LIANG ; Haixin DONG ; Donghua LIU ; Hongyun XU ; Yinqiao DONG ; Rong XU ; Lin ZHENG ; Shuyan HU ; Jian LI ; Qiang LIU ; Liang LUAN ; Jilu SHEN ; Lixia ZHANG ; Bo QUAN ; Xiaoping YAN ; Xiaoyan QI ; Dengyan QIAO ; Weiping LIU ; Xiusan XIA ; Ling MENG ; Jinhua LIANG ; Ping SHEN ; Yonghong XIAO
Chinese Journal of Clinical Infectious Diseases 2024;17(1):42-57
Objective:To report the results of national surveillance on the distribution and antimicrobial resistance profile of clinical Gram-negative bacteria isolates from bloodstream infections in China in 2022.Methods:The clinical isolates of Gram-negative bacteria from blood cultures in member hospitals of national bloodstream infection Bacterial Resistant Investigation Collaborative System(BRICS)were collected during January 2022 to December 2022. Antibiotic susceptibility tests were conducted by agar dilution or broth dilution methods recommended by Clinical and Laboratory Standards Institute(CLSI). WHONET 5.6 and SPSS 25.0 software were used to analyze the data.Results:During the study period,9 035 strains of Gram-negative bacteria were collected from 51 hospitals,of which 7 895(87.4%)were Enterobacteriaceae and 1 140(12.6%)were non-fermenting bacteria. The top 5 bacterial species were Escherichia coli( n=4 510,49.9%), Klebsiella pneumoniae( n=2 340,25.9%), Pseudomonas aeruginosa( n=534,5.9%), Acinetobacter baumannii complex( n=405,4.5%)and Enterobacter cloacae( n=327,3.6%). The ESBLs-producing rates in Escherichia coli, Klebsiella pneumoniae and Proteus spp. were 47.1%(2 095/4 452),21.0%(427/2 033)and 41.1%(58/141),respectively. The prevalence of carbapenem-resistant Escherichia coli(CREC)and carbapenem-resistant Klebsiella pneumoniae(CRKP)were 1.3%(58/4 510)and 13.1%(307/2 340);62.1%(36/58)and 9.8%(30/307)of CREC and CRKP were resistant to ceftazidime/avibactam combination,respectively. The prevalence of carbapenem-resistant Acinetobacter baumannii(CRAB)complex was 59.5%(241/405),while less than 5% of Acinetobacter baumannii complex was resistant to tigecycline and polymyxin B. The prevalence of carbapenem-resistant Pseudomonas aeruginosa(CRPA)was 18.4%(98/534). There were differences in the composition ratio of Gram-negative bacteria in bloodstream infections and the prevalence of main Gram-negative bacteria resistance among different regions,with statistically significant differences in the prevalence of CRKP and CRPA( χ2=20.489 and 20.252, P<0.001). The prevalence of CREC,CRKP,CRPA,CRAB,ESBLs-producing Escherichia coli and Klebsiella pneumoniae were higher in provinicial hospitals than those in municipal hospitals( χ2=11.953,81.183,10.404,5.915,12.415 and 6.459, P<0.01 or <0.05),while the prevalence of CRPA was higher in economically developed regions(per capita GDP ≥ 92 059 Yuan)than that in economically less-developed regions(per capita GDP <92 059 Yuan)( χ2=6.240, P=0.012). Conclusions:The proportion of Gram-negative bacteria in bloodstream infections shows an increasing trend,and Escherichia coli is ranked in the top,while the trend of CRKP decreases continuously with time. Decreasing trends are noted in ESBLs-producing Escherichia coli and Klebsiella pneumoniae. Low prevalence of carbapenem resistance in Escherichia coli and high prevalence in CRAB complex have been observed. The composition ratio and antibacterial spectrum of bloodstream infections in different regions of China are slightly different,and the proportion of main drug resistant bacteria in provincial hospitals is higher than those in municipal hospitals.
8.Variation rules of main secondary metabolites in Hedysari Radix before and after rubbing strip
Xu-Dong LUO ; Xin-Rong LI ; Cheng-Yi LI ; Peng QI ; Ting-Ting LIANG ; Shu-Bin LIU ; Zheng-Ze QIANG ; Jun-Gang HE ; Xu LI ; Xiao-Cheng WEI ; Xiao-Li FENG ; Ming-Wei WANG
Chinese Traditional Patent Medicine 2024;46(3):747-754
AIM To investigate the variation rules of main secondary metabolites in Hedysari Radix before and after rubbing strip.METHODS UPLC-MS/MS was adopted in the content determination of formononetin,ononin,calycosin,calycosin-7-glucoside,medicarpin,genistein,luteolin,liquiritigenin,isoliquiritigenin,vanillic acid,ferulic acid,γ-aminobutyric acid,adenosine and betaine,after which cluster analysis,principal component analysis and orthogonal partial least squares discriminant analysis were used for chemical pattern recognition to explore differential components.RESULTS After rubbing strip,formononetin,calycosin,liquiritigenin and γ-aminobutynic acid demonstrated increased contents,along with decreased contents of ononin,calycosin-7-glucoside and vanillic acid.The samples with and without rubbing strip were clustered into two types,calycosin-7-glucoside,formononetin,γ-aminobutynic acid,vanillic acid,calycosin-7-glucoside and formononetin were differential components.CONCLUSION This experiment clarifies the differences of chemical constituents in Hedysari Radix before and after rubbing strip,which can provide a reference for the research on rubbing strip mechanism of other medicinal materials.
9.Classification of bladder cancer based on immune cell infiltration and construction of a risk prediction model for prognosis
Guicao YIN ; Shengqi ZHENG ; Wei ZHANG ; Xin DONG ; Lezhong QI ; Yifan LI
Journal of Zhejiang University. Medical sciences 2024;53(1):47-57
Objective:To classify bladder cancer based on immune cell infiltration score and to construct a prognosis assessment model of patients with bladder cancer.Methods:The transcriptome data and clinical data of breast cancer patients were obtained from the The Cancer Genome Atlas(TCGA)database.Single sample gene set enrichment analysis was used to calculate the infiltration scores of 16 immune cells.The classification of breast cancer patients was achieved by unsupervised clustering,and the sensitivity of patients with different types to immunotherapy and chemotherapy was analyzed.The key modules significantly related to the infiltration of key immune cells were identified by weighted correlation network analysis(WGCNA),and the key genes in the modules were identified.A risk scoring model and a nomogram for prognosis assessment of bladder cancer patients were constructed and verified.Results:B cells,mast cells,neutrophils,T helper cells and tumor infiltrating lymphocytes were determined to be the key immune cells of bladder cancer.The patients were clustered into two groups(Cluster 1′ and Custer 2)based on immune cell infiltration scores.Compared with patients with Cluster 1′,patients with Cluster 2 were more likely to benefit from immunotherapy(P<0.05),and patients with Cluster 2 were more sensitive to Enbeaten,Docetaxel,Cyclopamine,and Akadixin(P<0.05).35 genes related to key immune cells were screened out by WGCNA and 4 genes(GPR171,HOXB3,HOXB5 and HOXB6)related to the prognosis of bladder cancer were further screened by LASSO Cox regression.The areas under the ROC curve(AUC)of the bladder cancer prognosis risk scoring model based on these 4 genes to predict the 1-,3-and 5-year survival of patients were 0.735,0.765 and 0.799,respectively.The nomogram constructed by combining risk score and clinical parameters has high accuracy in predicting the 1-,3-,and 5-year overall survival of bladder cancer patients.Conclusions:According to the immune cell infiltration score,bladder cancer patients can be classified.Furthermore,bladder cancer prognosis risk scoring model and nomogram based on key immune cell-related genes have high accuracy in predicting the prognosis of bladder cancer patients.
10.Classification of bladder cancer based on immune cell infiltration and construction of a risk prediction model for prognosis
Guicao YIN ; Shengqi ZHENG ; Wei ZHANG ; Xin DONG ; Lezhong QI ; Yifan LI
Journal of Zhejiang University. Medical sciences 2024;53(1):47-57
Objective:To classify bladder cancer based on immune cell infiltration score and to construct a prognosis assessment model of patients with bladder cancer.Methods:The transcriptome data and clinical data of breast cancer patients were obtained from the The Cancer Genome Atlas(TCGA)database.Single sample gene set enrichment analysis was used to calculate the infiltration scores of 16 immune cells.The classification of breast cancer patients was achieved by unsupervised clustering,and the sensitivity of patients with different types to immunotherapy and chemotherapy was analyzed.The key modules significantly related to the infiltration of key immune cells were identified by weighted correlation network analysis(WGCNA),and the key genes in the modules were identified.A risk scoring model and a nomogram for prognosis assessment of bladder cancer patients were constructed and verified.Results:B cells,mast cells,neutrophils,T helper cells and tumor infiltrating lymphocytes were determined to be the key immune cells of bladder cancer.The patients were clustered into two groups(Cluster 1′ and Custer 2)based on immune cell infiltration scores.Compared with patients with Cluster 1′,patients with Cluster 2 were more likely to benefit from immunotherapy(P<0.05),and patients with Cluster 2 were more sensitive to Enbeaten,Docetaxel,Cyclopamine,and Akadixin(P<0.05).35 genes related to key immune cells were screened out by WGCNA and 4 genes(GPR171,HOXB3,HOXB5 and HOXB6)related to the prognosis of bladder cancer were further screened by LASSO Cox regression.The areas under the ROC curve(AUC)of the bladder cancer prognosis risk scoring model based on these 4 genes to predict the 1-,3-and 5-year survival of patients were 0.735,0.765 and 0.799,respectively.The nomogram constructed by combining risk score and clinical parameters has high accuracy in predicting the 1-,3-,and 5-year overall survival of bladder cancer patients.Conclusions:According to the immune cell infiltration score,bladder cancer patients can be classified.Furthermore,bladder cancer prognosis risk scoring model and nomogram based on key immune cell-related genes have high accuracy in predicting the prognosis of bladder cancer patients.

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