Marking the 110th anniversary of the Chinese Medical Association, this article commemorates the foundational contributions of Professor Wang Damei (1920-2003) for her life-long dedication to the development of plastic surgery. In 1949, she helped establish New China’s first department of plastic surgery at Peking University Hospital (now Peking University First Hospital). From 1952 to 1953, she organised and taught the inaugural national advanced courses in plastic surgery. In November 1965, when the department was incorporated into Peking University Third Hospital, she transferred with it and continued her clinical work there until retirement. Clinically, she completed several landmark procedures, including China’s first craniofacial surgery (1973), the resection of a parasitic cranial twin in Yunnan (1979), and the country’s first male-to-female surgery (1983). She also promoted copper-needle indwelling therapy for cavernous haemangioma in 1991, and supervised a modified implantation of an artificial urinary sphincter in 1999. Academically, she translated and edited key textbooks, standardised clinical pathways, and helped shaping a practice that was "teachable, learnable, and reproducible". Through initiatives such as seven missions to Yunnan, she integrated standardization with humanistic care, exemplifying the professional ideal encapsulated in "Renew Faces with Healing Hands, Passing on Excellence" .

Marking the 110th anniversary of the Chinese Medical Association, this article commemorates the foundational contributions of Professor Wang Damei (1920-2003) for her life-long dedication to the development of plastic surgery. In 1949, she helped establish New China’s first department of plastic surgery at Peking University Hospital (now Peking University First Hospital). From 1952 to 1953, she organised and taught the inaugural national advanced courses in plastic surgery. In November 1965, when the department was incorporated into Peking University Third Hospital, she transferred with it and continued her clinical work there until retirement. Clinically, she completed several landmark procedures, including China’s first craniofacial surgery (1973), the resection of a parasitic cranial twin in Yunnan (1979), and the country’s first male-to-female surgery (1983). She also promoted copper-needle indwelling therapy for cavernous haemangioma in 1991, and supervised a modified implantation of an artificial urinary sphincter in 1999. Academically, she translated and edited key textbooks, standardised clinical pathways, and helped shaping a practice that was "teachable, learnable, and reproducible". Through initiatives such as seven missions to Yunnan, she integrated standardization with humanistic care, exemplifying the professional ideal encapsulated in "Renew Faces with Healing Hands, Passing on Excellence" .

Objective To develop an intelligent model for differential diagnosis of atrioventricular nodal re-entrant tachycardia(AVNRT)and atrioventricular re-entrant tachycardia(AVRT)using 12-lead wearable electrocardiogram devices.Methods A total of 356 samples of 12-lead supraventricular tachycardia(SVT)electrocardiograms recorded by wearable devices were randomly divided into training and validation sets using 5-fold cross validation to establish the intelligent classification model,and 101 patients with the diagnosis of SVT undergoing electrophysiological studies and radiofrequency ablation from October,2021 to March,2023 were selected as the testing set.The changes in electrocardiogram parameters before and during induced tachycardia were compared.Based on multiscale deep neural network,an intelligent diagnosis model for classifying SVT mechanisms was constructed and validated.The 3-lead electrocardiogram signals from Ⅱ,Ⅲ,and V1 were extracted to build new classification models,whose diagnostic efficacy was compared with that of the 12-lead model.Results Of the 101 patients with SVT in the testing set,68 were diagnosed with AVNRT and 33 were diagnosed with AVRT by electrophysiological study.The pre-trained model achieved a high area under the precision-recall curve(0.9492)and F1 score(0.8195)for identifying AVNRT in the validation set.The total F1 scores of the lead Ⅱ,Ⅲ,V1,3-lead and 12-lead intelligent diagnostic models in the testing set were 0.5597,0.6061,0.3419,0.6003 and 0.6136,respectively.Compared with the 12-lead classification model,the lead-Ⅲ model had a net reclassification index improvement of-0.029(P=0.878)and an integrated discrimination index improvement of-0.005(P=0.965).Conclusion The intelligent diagnostic model based on multiscale deep neural network using wearable electrocardiogram devices has an acceptable accuracy for classifying SVT mechanisms.

Objective:To investigate the early predictors of respiratory depression in patients with glufosinate poisoning, and provide reference for clinicians to make decisions.Methods:In March 2022, the clinical data of patients with glufosinate poisoning admitted to the intensive care unit of the Affiliated Xiangshan Hospital of Wenzhou Medical University from March 2018 to January 2022 were retrospectively analyzed. The patients were divided into respiratory depression group and non-respiratory depression group according to the occurrence of respiratory depression during hospitalization. The clinical data such as age, gender, past history, intake, initial treatment and laboratory examination were compared between the two groups. Multivariate logistic regression was used to analyze the predictors of respiratory depression in patients with glufosinate poisoning, and its predictive value was analyzed by receiver operating characteristic (ROC) curve.Results:A total of 34 patients with glufosinate poisoning were enrolled, including 13 patients in non-respiratory depression group and 21 patients in respiratory depression group. There were significant differences in intake, blood amylase and bicarbonate radical in arterial blood gas between the two groups ( P<0.05). Respiratory depression occurred at 6.5-48.0 h after ingestion, with a median of 15.0 (9.5, 24.0) h. Multivariate logistic regression analysis showed that the intake of glufosinate ( OR=1.440, 95% CI: 1.033-2.009, P=0.032) and bicarbonate radical in arterial blood gas ( OR=0.199, 95% CI: 0.040-0.994, P=0.049) were predictors of respiratory depression in patients with glufosinate poisoning, and the area under the curve (AUC) of ROC curves were 0.936 and 0.842. The optimal cut-off values were 15.0 g (sensitivity=95.2%, specificity=76.9%) and 17.6 mmol/L (sensitivity=71.4%, specificity=84.6%), respectively. Conclusion:The intake of glufosinate and bicarbonate radical in arterial blood gas have good prediction effects on the occurrence of respiratory depression in patients with glufosinate poisoning.

Objective To develop an intelligent model for differential diagnosis of atrioventricular nodal re-entrant tachycardia(AVNRT)and atrioventricular re-entrant tachycardia(AVRT)using 12-lead wearable electrocardiogram devices.Methods A total of 356 samples of 12-lead supraventricular tachycardia(SVT)electrocardiograms recorded by wearable devices were randomly divided into training and validation sets using 5-fold cross validation to establish the intelligent classification model,and 101 patients with the diagnosis of SVT undergoing electrophysiological studies and radiofrequency ablation from October,2021 to March,2023 were selected as the testing set.The changes in electrocardiogram parameters before and during induced tachycardia were compared.Based on multiscale deep neural network,an intelligent diagnosis model for classifying SVT mechanisms was constructed and validated.The 3-lead electrocardiogram signals from Ⅱ,Ⅲ,and V1 were extracted to build new classification models,whose diagnostic efficacy was compared with that of the 12-lead model.Results Of the 101 patients with SVT in the testing set,68 were diagnosed with AVNRT and 33 were diagnosed with AVRT by electrophysiological study.The pre-trained model achieved a high area under the precision-recall curve(0.9492)and F1 score(0.8195)for identifying AVNRT in the validation set.The total F1 scores of the lead Ⅱ,Ⅲ,V1,3-lead and 12-lead intelligent diagnostic models in the testing set were 0.5597,0.6061,0.3419,0.6003 and 0.6136,respectively.Compared with the 12-lead classification model,the lead-Ⅲ model had a net reclassification index improvement of-0.029(P=0.878)and an integrated discrimination index improvement of-0.005(P=0.965).Conclusion The intelligent diagnostic model based on multiscale deep neural network using wearable electrocardiogram devices has an acceptable accuracy for classifying SVT mechanisms.

Objective:To investigate the early predictors of respiratory depression in patients with glufosinate poisoning, and provide reference for clinicians to make decisions.Methods:In March 2022, the clinical data of patients with glufosinate poisoning admitted to the intensive care unit of the Affiliated Xiangshan Hospital of Wenzhou Medical University from March 2018 to January 2022 were retrospectively analyzed. The patients were divided into respiratory depression group and non-respiratory depression group according to the occurrence of respiratory depression during hospitalization. The clinical data such as age, gender, past history, intake, initial treatment and laboratory examination were compared between the two groups. Multivariate logistic regression was used to analyze the predictors of respiratory depression in patients with glufosinate poisoning, and its predictive value was analyzed by receiver operating characteristic (ROC) curve.Results:A total of 34 patients with glufosinate poisoning were enrolled, including 13 patients in non-respiratory depression group and 21 patients in respiratory depression group. There were significant differences in intake, blood amylase and bicarbonate radical in arterial blood gas between the two groups ( P<0.05). Respiratory depression occurred at 6.5-48.0 h after ingestion, with a median of 15.0 (9.5, 24.0) h. Multivariate logistic regression analysis showed that the intake of glufosinate ( OR=1.440, 95% CI: 1.033-2.009, P=0.032) and bicarbonate radical in arterial blood gas ( OR=0.199, 95% CI: 0.040-0.994, P=0.049) were predictors of respiratory depression in patients with glufosinate poisoning, and the area under the curve (AUC) of ROC curves were 0.936 and 0.842. The optimal cut-off values were 15.0 g (sensitivity=95.2%, specificity=76.9%) and 17.6 mmol/L (sensitivity=71.4%, specificity=84.6%), respectively. Conclusion:The intake of glufosinate and bicarbonate radical in arterial blood gas have good prediction effects on the occurrence of respiratory depression in patients with glufosinate poisoning.

Objective:The epidemiological characteristics of tuberculosis deaths among Chinese residents from 2006 to 2021 were analyzed, and the tuberculosis mortality rate from 2022 to 2027 was predicted to provide a reference for tuberculosis prevention and control in China.Methods:The data set of tuberculosis deaths from 2006 to 2021 was published regularly by the China CDC, and the crude mortality rate (CMR) and age-standardized mortality rates (ASMR) were calculated according to the population structure of China in 2000. The distribution characteristics of age, sex, region, and time of tuberculosis deaths were analyzed, the Joinpoint regression analysis model was used to analyze the changing trend, and the grey model was applied to predict CMR and ASMR from 2022 to 2027.Results:From 2006 to 2021, the CMR and ASMR of tuberculosis showed a downward trend among males and females, urban and rural areas, and all age groups, in a word, all the Chinese residents. Except for the age group ≥85 years old, the mortality trend was insignificant. In the eastern, central, or western regions. CMR and ASMR were significantly higher in males than in females.CMR and ASMR were significantly lower in urban areas than in rural areas. In general, active tuberculosis patients present a higher mortality rate. The CMR and ASMR in the western region were higher than those in the eastern and central regions and lower in the eastern region than in the central region, but the differences were less obvious. The ASMR of the eastern cities was lower than that of the central and western regions, and the ASMR of the central cities was higher than that of the western region from 2006 to 2009 and 2012 and lower than that of the western region in other years. The ASMR in the western countryside was higher than that in the eastern and central regions and lower in the eastern part than in the central region, but the difference was not obvious. The grey model prediction results show that the CMR (/100 000) of Chinese residents from 2022 to 2027 is 1.585, 1.471, 1.360, 1.250, 1.143, and 1.038, and the ASMR (/100 000) is 0.779, 0.653, 0.531, 0.411, 0.295 and 0.181, respectively.Conclusions:The CMR and ASMR of tuberculosis will continue to decline, and extraordinary achievements have been made in tuberculosis prevention and control in Chinese residents from 2006 to 2021 and, presumably, from 2022 to 2027. However, tuberculosis screening and treatment programs in the western region, men, the elderly population, and rural areas should be further strengthened, and targeted prevention and control measures should be formulated to reduce mortality.

Hepatocellular carcinoma (HCC) is a kind of primary liver cancer with a high mortality rate. In China, the incidence ratio in males to females with HCC is 2:1–5:1. The difference in sex hormone pathways between males and females and the interaction between androgen/androgen receptors and HBV can lead to an incidence difference between males and females with HCC. Hence, the androgen/androgen receptor oncogenic pathway in hepatocellular carcinoma has received considerable attention. This review mainly summarizes the recent research progress on the androgen/androgen receptor oncogenic pathway in hepatocellular carcinoma.

Statistics plays an important role in medical research， and the selection of appropriate statistical methods is crucial for drawing reliable and valuable conclusions. This paper provides a brief introduction to commonly used statistical analysis methods for medical data， covering descriptive analysis， parametric test， nonparametric test， correlation analysis， regression analysis， and analysis of survival data. It focuses on discussing the assumptions of multiple linear regression， logistic regression and Cox proportional risk regression， as well as how to choose the appropriate statistical methods for analyzing and interpreting medical data based on different research objectives and data types.