Objective:To investigate the diagnostic value of different conventional ultrasound-based radiomics models and their combination with clinical ultrasound features in the pathological subtyping of renal cell carcinoma.Methods:Retrospective data from 286 patients diagnosed with renal cell carcinoma by pathology at the Tenth Affiliated Hospital of Southern Medical University between May 1，2017 and June 7，2024 were collected. Among the 286 patients，203 were clear cell carcinoma，44 were papillary renal cell carcinoma，and 39 were chromophobe renal cell carcinoma. The patients were randomly divided into a training group（201 cases）and a validation group（85 cases）in a ratio of 7 to 3. Regions of interest（ROI）were delineated on conventional ultrasound images，and the radiomics features were extracted. Feature selection was performed using Student's t-test，Pearson correlation，and the least absolute shrinkage and selection operator（LASSO）. Six different machine learning methods included category gradient boosting（CatBoost），light gradient boosting machine（LightGBM），Logistic regression（LR），random forest（RF），support vector machine（SVM）and extreme gradient boosting（XGBoost）were used to establish radiomics models. Weight balancing was applied to correct for sample imbalance，and an imaging genomics model was constructed after balancing the samples. Independent predictors of renal cell carcinoma subtyping were selected from clinical ultrasound features using univariate and multivariate logistic regression analyses，and a clinical imaging model was constructed. The best-performing radiomics model was combined with the clinical independent predictors to construct a combined model. Receiver operating characteristic curves and the obuchowski index were plotted to evaluate model performance. Results:Among the radiomics models，the model constructed using Random Forest（RS RF）after balancing the samples exhibited the best predictive performance，with area under the curve（AUCs）of 0.918（micro-average ROC）and 0.903（macro-average ROC），and the obuchowski index was 0.885 in the validation group. The long and short axes of ultrasound image tumor masses were used as imaging independent predictors to construct a clinical imaging model. In the validation group，the AUCs of the clinical model were 0.886（micro-average ROC）and 0.606（macro-average ROC），and the obuchowski index was 0.569. The combined model achieved AUCs of 0.888（micro-average ROC）and 0.967（macro-average ROC），with an obuchowski index of 0.933，outperforming any single model. Conclusions:The combination of conventional ultrasound-based radiomics models with clinical ultrasound features demonstrates high diagnostic value in differentiating clear cell carcinoma，papillary renal cell carcinoma，and chromophobe renal cell carcinoma. It may serve as an auxiliary tool for providing timely and effective clinical guidance.

Objective To develop models for predicting response to chemotherapy combined with immunotherapy in patients with esophageal squamous carcinoma with various machine learning algorithms,and then select the optimal model.Methods A retrospective study was performed for 174 patients with esophageal squamous cell carcinoma undergoing chemotherapy combined with immunotherapy admitted in Department of Thoracic Surgery of the First Affiliated Hospital of Army Medical University from January 2022 to December 2023.The CT scans and clinical information were collected before treatment.They were randomly divided into a training set(n=122)and a testing set(n=52)in a ratio of 7∶3.CT radiomic features were extracted and selected,and then 5 machine-learning algorithms were employed to establish the prediction models,including radiomics model and clinical-radiomics model.Five-fold cross-validation was conducted on the training set,and the performance of the prediction models was evaluated on the testing set using receiver operating characteristic(ROC)curve and the F1 score.The best-performing model was further explained using local interpretable model-agnostic explanations(LIME)algorithm.Results Among the 174 patients,115(66.1%)achieved clinical remission.From the clinical information and CT images,1 clinical features and 10 radiomic features were identified.The area under of ROC curve(AUC)for the radiomics and clinical-radiomics models was 0.750(95%CI:0.616～0.883),and 0.766(95%CI:0.637～0.895),respectively.The F1 score of the optimal clinical-radiomics model was 0.829.LIME algorithm indicated that this best model demonstrated reliability in predicting individual samples.Conclusion The clinical-radiomics prediction model based on machine learning algorithm performs well,and can provide a reference for doctors'clinical decision-making by predicting the response to chemotherapy combined with immunotherapy in patients with esophageal squamous cell carcinoma.

The associated specialists of The Society of Maternal and Child Health Management, China Healthy Birth Science Association and Shanghai Society for Immunology, Pediatric Branch updated the guidelines for human papilloma virus(HPV)vaccine against HPV infections in the Chinese children and adolescence based on guideline on human papilloma virus vaccine immunization schedule(shanghai) for adolescence.The guideline covers 11 recommendations on 8 aspects, including the purpose of recommending HPV vaccines for children and adolescents, the importance of early vaccination, the safety and efficacy of the vaccines, vaccination for immunocompromised children, vaccine processing technique, adverse reactions of vaccines, and combined vaccination with multiple vaccines.Although some of the guideline provisioners have controversies regarding aspects such as HPV infection leading to certain tumors and the therapeutic effects of vaccine fields, but this interpretation still provide these background literatures for reference.Now, this guideline will be interpreted from the perspective of the recommended content and the research progress of related HPV vaccines, with the aim of providing reference for pediatric clinical work.

The associated specialists of The Society of Maternal and Child Health Management, China Healthy Birth Science Association and Shanghai Society for Immunology, Pediatric Branch updated the guidelines for human papilloma virus(HPV)vaccine against HPV infections in the Chinese children and adolescence based on guideline on human papilloma virus vaccine immunization schedule(shanghai) for adolescence.The guideline covers 11 recommendations on 8 aspects, including the purpose of recommending HPV vaccines for children and adolescents, the importance of early vaccination, the safety and efficacy of the vaccines, vaccination for immunocompromised children, vaccine processing technique, adverse reactions of vaccines, and combined vaccination with multiple vaccines.Although some of the guideline provisioners have controversies regarding aspects such as HPV infection leading to certain tumors and the therapeutic effects of vaccine fields, but this interpretation still provide these background literatures for reference.Now, this guideline will be interpreted from the perspective of the recommended content and the research progress of related HPV vaccines, with the aim of providing reference for pediatric clinical work.

Objective:To investigate the diagnostic value of different conventional ultrasound-based radiomics models and their combination with clinical ultrasound features in the pathological subtyping of renal cell carcinoma.Methods:Retrospective data from 286 patients diagnosed with renal cell carcinoma by pathology at the Tenth Affiliated Hospital of Southern Medical University between May 1，2017 and June 7，2024 were collected. Among the 286 patients，203 were clear cell carcinoma，44 were papillary renal cell carcinoma，and 39 were chromophobe renal cell carcinoma. The patients were randomly divided into a training group（201 cases）and a validation group（85 cases）in a ratio of 7 to 3. Regions of interest（ROI）were delineated on conventional ultrasound images，and the radiomics features were extracted. Feature selection was performed using Student's t-test，Pearson correlation，and the least absolute shrinkage and selection operator（LASSO）. Six different machine learning methods included category gradient boosting（CatBoost），light gradient boosting machine（LightGBM），Logistic regression（LR），random forest（RF），support vector machine（SVM）and extreme gradient boosting（XGBoost）were used to establish radiomics models. Weight balancing was applied to correct for sample imbalance，and an imaging genomics model was constructed after balancing the samples. Independent predictors of renal cell carcinoma subtyping were selected from clinical ultrasound features using univariate and multivariate logistic regression analyses，and a clinical imaging model was constructed. The best-performing radiomics model was combined with the clinical independent predictors to construct a combined model. Receiver operating characteristic curves and the obuchowski index were plotted to evaluate model performance. Results:Among the radiomics models，the model constructed using Random Forest（RS RF）after balancing the samples exhibited the best predictive performance，with area under the curve（AUCs）of 0.918（micro-average ROC）and 0.903（macro-average ROC），and the obuchowski index was 0.885 in the validation group. The long and short axes of ultrasound image tumor masses were used as imaging independent predictors to construct a clinical imaging model. In the validation group，the AUCs of the clinical model were 0.886（micro-average ROC）and 0.606（macro-average ROC），and the obuchowski index was 0.569. The combined model achieved AUCs of 0.888（micro-average ROC）and 0.967（macro-average ROC），with an obuchowski index of 0.933，outperforming any single model. Conclusions:The combination of conventional ultrasound-based radiomics models with clinical ultrasound features demonstrates high diagnostic value in differentiating clear cell carcinoma，papillary renal cell carcinoma，and chromophobe renal cell carcinoma. It may serve as an auxiliary tool for providing timely and effective clinical guidance.

Objective To investigate the relationship between serum levels of nuclear factor E2-related fac-tor 2(Nrf2),advanced oxidation protein products(AOPP)and blood lipid,liver fibrosis in patients with non-alcoholic steatohepatitis(NASH).Methods A total of 104 patients with NASH in Bishan Hospital Affiliated to Chongqing Medical University were selected as the study group,and 90 healthy people were selected as the control group.The serum levels of Nrf2 and AOPP in each group were detected and compared.Pearson or Spearman correlation analysis was used to analyze the relationship between serum Nrf2,AOPP levels and blood lipid,liver fibrosis in patients with NASH.Receiver operating characteristic(ROC)curve was used to e-valuate the diagnostic value of serum Nrf2,AOPP levels for NASH.Results The levels of triglyceride(TG),total cholesterol(TC),low density lipoprotein cholesterol(LDL-C),Nrf2 and AOPP in the study group were higher than those in the control group(P＜0.05),and the level of high density lipoprotein cholesterol(HDL-C)was significantly lower than that in the control group(P＜0.05).The serum levels of Nrf2 and AOPP in severe group were higher than those in moderate group and mild group(P＜0.05),and the serum levels of Nrf2 and AOPP in moderate group were higher than those in mild group(P＜0.05).Correlation analysis showed that serum Nrf2 and AOPP levels in NASH patients were positively correlated with TG,TC,LDL-C and the degree of liver fibrosis(P＜0.05),and negatively correlated with HDL-C(P＜0.05).The area under the curve(AUC)of serum Nrf2 for NASH diagnosis was 0.830(95％CI 0.780-0.880).The AUC of serum AOPP in the diagnosis of NASH was 0.866(95％CI 0.816-0.916).The AUC of the combined diagnosis of NASH was 0.925(95％CI 0.875-0.975).Conclusion The serum levels of Nrf2 and AOPP are increased in NASH patients,and they are closely related to blood lipids and liver fibrosis,which are expected to be effective indicators for the diagnosis of NASH.

Objective:To explore the competitive attitude of students of medical college and its influencing factors.Methods:A sample of 382 first to third year medical students was selected with the method of random sampling. The questionnaire consists of three parts: demographics, Competitive Attitude Scale of Chinese Version and Jefferson Scale of Physician Empathy. SPSS 25.0 was applied to perform the statistical analysis. Concrete methods that used involved statistical interpretation, Kruskal-Wallis test, Mann-Whitney U test and Spearman rank sum correlation analysis. Results:The benign competitive attitude of students was 51.00(45.00，56.00) and the excessive competitive attitude was 35.00(31.00，38.00). There was a statistically significant difference in the score of benign competitive attitude among medical students of different grades( H=22.31, P<0.001). There were statistically significant differences in the scores of benign competitive attitudes among the medical students who enjoy teamwork ( Z=-4.53, P<0.001), who had role models to follow in the medical field ( Z=-3.99, P<0.001), and who had careful career planning ( Z=-2.19, P=0.029). There were statistically significant differences in the scores of excessive competitive attitudes among the medical students who enjoy teamwork ( Z=-2.47, P=0.014), who had role models to follow in the medical field ( Z=-2.89, P=0.004), and who had careful career planning ( Z=-2.32, P=0.021). There was a weak positive correlation between empathy and benign competitive attitude ( r=0.20, P<0.001), but a negative correlation between empathy and excessive attitude ( r=-0.36, P<0.001). Conclusions:Medical students had a good competitive attitude. Medical college should pay more attention to improving medical students' sense of teamwork, career planning ability and willingness to learn from role models, guide students to develop a benign competitive attitude and focus on improving their professional quality.

Objective To explore indicators of risks for preoperative adverse cardiovascular events(PACE)in elderly patients who underwent non-cardiac surgery(NCS).Methods The clinical data of elderly patients who had undergone NCS at Tianjin Medical University General Hospital from January 2022 to March 2024 were analyzed retrospectively,including gender,age,medical history,operation ways,and preoperative creatinine(Cr),hypersensitive C-reactive protein(hs-CRP),hypersensitivity cTnT(hs-cTnT),MB isoenzyme of creatine kinase(CK-MB),N-terminal pro-brain natriuretic peptide(NT-proBNP)and D-Dimer,prothrombin time(PT),activated partial thromboplastin time(APTT),fibrinogen(Fib).To measure the revised cardiac risk index(RCRI),the 30-day prognosis of the patients was followed up.Patients were divided into PACE group and non-PACE group according to the occurrence of PACE,and the clinical data between the two groups were compared.Multiple Logistics regression was used to identify independent risk factors for PACE,and the receiver operator characteristic curve(ROC curve)was drawn to evaluate the predictive value of each risk factor for PACE risk in elderly patients underwent NCS.Results A total of 112 elderly patients underwent NCS were enrolled,including 9 cases in the PACE group(incidence rate 8.03%)and 103 cases in the non-PACE group.Patients'age(years old:75.1±6.4 vs.69.4±7.2,P=0.002),preoperative incidence of coronary heart disease[44.4%(4/9)vs.28.2%(29/103),P=0.004],incidence of heart failure[22.2%(2/9)vs.6.8%(7/103),P=0.003],NT-proBNP[(ng/L):485.17(196.10,787.53)vs.98.41(73.81,160.35),P=0.001],hs-cTnT[(μg/L):0.043(0.023,0.087)vs.0.005(0.003,0.008),P=0.002],and D-Dimer[(μg/L):2 953(2 674,3 570)vs.514(260,1 010),P=0.001]levels in the PACE group were significantly higher than those in the non-PACE group,the proportion of RCRI 2-3 scores in PACE group were significantly higher than those in non-PACE group[2 scores:44.4%(4/9)vs.18.4%(19/103),3 scores:33.3%(3/9)vs.14.6%(15/103),P=0.047].As shown in multiple Logistic regression analysis,age,preoperative NT-proBNP levels and RCRI scores≥2 were independent risk factors affecting the prognosis for PACE occurrence[odds ratio(OR)and 95%confidence interval(95%CI)were 1.163(1.032-1.310),1.021(1.008-1.031)and 1.261(1.003-1.585),P values were 0.013,0.001,0.048,respectively].As shown in ROC curve analysis,age,NT-proBNP and RCRI had predictive value for the occurrence of PACE in NCS patients,the area under the curve(AUC)and 95%CI of age,NT-proBNP and RCRI were 0.795(0.675-0.915),0.908(0.838-0.979)and 0.735(0.556-0.912),with P=0.030,<0.001 and 0.020 respectively.The optimal cut-off values were:age≥69.5 years(sensitivity was 88.9%,specificity was 56.3%);NT-proBNP≥177.0 ng/L(sensitivity was 88.9%,specificity was 83.5%);RCRI≥1.5 points(sensitivity was 77.8%,specificity was 67.0%).Conclusions Age,NT-proBNP levels and RCRI have certain predictive values for the occurrence of PACE in NCS patients undergoing NCS,advanced age,higher NT-proBNP levels,and elevated RCRI scores were associated with increased PACE risk in elderly patients undergoing NCS.While age showed high sensitivity but limited specificity,combining NT-proBNP(with high diagnostic accuracy)and RCRI(with objective scoring)improved preoperative PACE risk prediction.

Objective To explore indicators of risks for preoperative adverse cardiovascular events(PACE)in elderly patients who underwent non-cardiac surgery(NCS).Methods The clinical data of elderly patients who had undergone NCS at Tianjin Medical University General Hospital from January 2022 to March 2024 were analyzed retrospectively,including gender,age,medical history,operation ways,and preoperative creatinine(Cr),hypersensitive C-reactive protein(hs-CRP),hypersensitivity cTnT(hs-cTnT),MB isoenzyme of creatine kinase(CK-MB),N-terminal pro-brain natriuretic peptide(NT-proBNP)and D-Dimer,prothrombin time(PT),activated partial thromboplastin time(APTT),fibrinogen(Fib).To measure the revised cardiac risk index(RCRI),the 30-day prognosis of the patients was followed up.Patients were divided into PACE group and non-PACE group according to the occurrence of PACE,and the clinical data between the two groups were compared.Multiple Logistics regression was used to identify independent risk factors for PACE,and the receiver operator characteristic curve(ROC curve)was drawn to evaluate the predictive value of each risk factor for PACE risk in elderly patients underwent NCS.Results A total of 112 elderly patients underwent NCS were enrolled,including 9 cases in the PACE group(incidence rate 8.03%)and 103 cases in the non-PACE group.Patients'age(years old:75.1±6.4 vs.69.4±7.2,P=0.002),preoperative incidence of coronary heart disease[44.4%(4/9)vs.28.2%(29/103),P=0.004],incidence of heart failure[22.2%(2/9)vs.6.8%(7/103),P=0.003],NT-proBNP[(ng/L):485.17(196.10,787.53)vs.98.41(73.81,160.35),P=0.001],hs-cTnT[(μg/L):0.043(0.023,0.087)vs.0.005(0.003,0.008),P=0.002],and D-Dimer[(μg/L):2 953(2 674,3 570)vs.514(260,1 010),P=0.001]levels in the PACE group were significantly higher than those in the non-PACE group,the proportion of RCRI 2-3 scores in PACE group were significantly higher than those in non-PACE group[2 scores:44.4%(4/9)vs.18.4%(19/103),3 scores:33.3%(3/9)vs.14.6%(15/103),P=0.047].As shown in multiple Logistic regression analysis,age,preoperative NT-proBNP levels and RCRI scores≥2 were independent risk factors affecting the prognosis for PACE occurrence[odds ratio(OR)and 95%confidence interval(95%CI)were 1.163(1.032-1.310),1.021(1.008-1.031)and 1.261(1.003-1.585),P values were 0.013,0.001,0.048,respectively].As shown in ROC curve analysis,age,NT-proBNP and RCRI had predictive value for the occurrence of PACE in NCS patients,the area under the curve(AUC)and 95%CI of age,NT-proBNP and RCRI were 0.795(0.675-0.915),0.908(0.838-0.979)and 0.735(0.556-0.912),with P=0.030,<0.001 and 0.020 respectively.The optimal cut-off values were:age≥69.5 years(sensitivity was 88.9%,specificity was 56.3%);NT-proBNP≥177.0 ng/L(sensitivity was 88.9%,specificity was 83.5%);RCRI≥1.5 points(sensitivity was 77.8%,specificity was 67.0%).Conclusions Age,NT-proBNP levels and RCRI have certain predictive values for the occurrence of PACE in NCS patients undergoing NCS,advanced age,higher NT-proBNP levels,and elevated RCRI scores were associated with increased PACE risk in elderly patients undergoing NCS.While age showed high sensitivity but limited specificity,combining NT-proBNP(with high diagnostic accuracy)and RCRI(with objective scoring)improved preoperative PACE risk prediction.

Cow′s milk protein allergy (CMPA) is one of the most common presentations of food allergy seen in early childhood.It is an abnormal immune response caused by cow′s milk protein.CMPA can be clinically subdivided into either immediate-onset IgE mediated or delayed onset non-IgE mediated, or both.At present, concerns regarding the early and timely diagnosis of CMPA have been high-lighted over the years and there are many expert consensus on CMPA in China, but these consensus did not distinguish IgE mediated or non-IgE mediated CMPA.In view of the obvious clinical differences between the two type of CMPA and non-IgE mediated CMPA is more common in infancy, experts focus on pediatric gastroenterology, allergy/immunology, dermatology, nutrition and child healthcare convened by the Allergy Prevention and Control Professional Committee of Chinese Preventive Medicine Association present this guideline to help practitioners in primary care settings to early recognize and make suitable management of non-IgE mediated CMPA in China.The guideline incorporates the cutting-edge international guidance and the actual situation of Chinese children describing in detail the types, clinical features, diagnosis and nutritional intervention of non-IgE mediated CMPA.There are 42 recommendations in 7 categories in total referring to the common questions related to non-IgE mediated CMPA.