Objective:To explore the relationship between suicide attempts,school bullying,and psychological resilience in children and adolescents with major depressive disorder(MDD)and school bullying and psychological resilience.Methods:A total of 784 patients with MDD aged 10 to 18 years were included.The Chinese version of the Olweus Bullying Victimization Questionnaire,Adolescent Psychological Resilience Scale,and a suicide attempt assessment were utilized to evaluate school bullying,psychological resilience,and suicide attempt.Stepwise logistic regression was applied to identify the associated factors of suicide attempts.Results:The occurrence of suicide at-tempts in children and adolescents with MDD was positively associated with physical bullying(OR=1.85,95％CI:1.14-3.02)and indirect bullying(OR=1.48,95％CI:1.06-2.04),and negatively associated with higher levels of goal focus(OR=0.62,95％CI:0.45-0.85)and positive cognition(OR=0.62,95％CI:0.45-0.85)at higher levels.Conclusion:Bullying significantly increases the risk of suicide attempts in children and adolescents with MDD,while higher psychological resilience could mitigate this risk.

Purpose To develop an integrate model combining deep learning features from contrast-enhanced lung ultrasonography with clinical characteristics for predicting epidermal growth factor receptor mutation status in peripheral non-small cell lung cancer.Materials and Methods This retrospective study included 117 patients with pathologically confirmed non-small cell lung cancer from the First Affiliated Hospital of Guangxi Medical University(July 2021 to February 2024).Patients were randomly divided into training(n=93)and test(n=24)sets at an 8∶2 ratio.Regions of interest were delineated at the peak enhancement phase of contrast-enhanced lung ultrasonography.Various deep learning convolutional neural networks were pretrained,with ResNet18 selected as optimal for feature extraction.Deep learning,clinical,and integrated models were constructed using naive Bayesian algorithm.Performance was evaluated via receiver operating characteristic and calibration curves,while class activation mapping and Shapley additive explanation values provided model interpretability.Results In the training set,the deep learning,clinical and integrated models achieved area under the curve of 0.93(95%CI 0.88-0.98),0.86(95%CI 0.68-1.00),and 0.91(95%CI 0.85-0.97),respectively.Corresponding test set area under the curve were 0.81(95%CI 0.72-0.90),0.56(95%CI 0.33-0.80),and 0.87(95%CI 0.72-1.00).Both deep learning and integrated models significantly outperformed the clinical model in training(Z=2.380,P=0.017;Z=2.597,P=0.009)and test sets(Z=2.034,P=0.042;Z=2.577,P=0.010).The integrated model demonstrated excellent calibration and predictive performance.Conclusion The integrated model combining deep learning features from contrast-enhanced lung ultrasonography with clinical characteristics effectively predicts epidermal growth factor receptor mutation status in peripheral non-small cell lung cancer.

Purpose To develop an integrate model combining deep learning features from contrast-enhanced lung ultrasonography with clinical characteristics for predicting epidermal growth factor receptor mutation status in peripheral non-small cell lung cancer.Materials and Methods This retrospective study included 117 patients with pathologically confirmed non-small cell lung cancer from the First Affiliated Hospital of Guangxi Medical University(July 2021 to February 2024).Patients were randomly divided into training(n=93)and test(n=24)sets at an 8∶2 ratio.Regions of interest were delineated at the peak enhancement phase of contrast-enhanced lung ultrasonography.Various deep learning convolutional neural networks were pretrained,with ResNet18 selected as optimal for feature extraction.Deep learning,clinical,and integrated models were constructed using naive Bayesian algorithm.Performance was evaluated via receiver operating characteristic and calibration curves,while class activation mapping and Shapley additive explanation values provided model interpretability.Results In the training set,the deep learning,clinical and integrated models achieved area under the curve of 0.93(95%CI 0.88-0.98),0.86(95%CI 0.68-1.00),and 0.91(95%CI 0.85-0.97),respectively.Corresponding test set area under the curve were 0.81(95%CI 0.72-0.90),0.56(95%CI 0.33-0.80),and 0.87(95%CI 0.72-1.00).Both deep learning and integrated models significantly outperformed the clinical model in training(Z=2.380,P=0.017;Z=2.597,P=0.009)and test sets(Z=2.034,P=0.042;Z=2.577,P=0.010).The integrated model demonstrated excellent calibration and predictive performance.Conclusion The integrated model combining deep learning features from contrast-enhanced lung ultrasonography with clinical characteristics effectively predicts epidermal growth factor receptor mutation status in peripheral non-small cell lung cancer.

Objective:To explore the relationship between suicide attempts,school bullying,and psychological resilience in children and adolescents with major depressive disorder(MDD)and school bullying and psychological resilience.Methods:A total of 784 patients with MDD aged 10 to 18 years were included.The Chinese version of the Olweus Bullying Victimization Questionnaire,Adolescent Psychological Resilience Scale,and a suicide attempt assessment were utilized to evaluate school bullying,psychological resilience,and suicide attempt.Stepwise logistic regression was applied to identify the associated factors of suicide attempts.Results:The occurrence of suicide at-tempts in children and adolescents with MDD was positively associated with physical bullying(OR=1.85,95％CI:1.14-3.02)and indirect bullying(OR=1.48,95％CI:1.06-2.04),and negatively associated with higher levels of goal focus(OR=0.62,95％CI:0.45-0.85)and positive cognition(OR=0.62,95％CI:0.45-0.85)at higher levels.Conclusion:Bullying significantly increases the risk of suicide attempts in children and adolescents with MDD,while higher psychological resilience could mitigate this risk.

Tumor is the result of long-term and unlimited proliferation of cells. Tumor cells adjust various metabolic fluxes to meet increased bioenergy and biosynthetic requirements. Serine is one of the eight non-essential amino acids in the human body. It plays an important role in a variety of physiological activities and can provide one carbon unit, glycine, etc. for cell proliferation. Serine hydroxymethyltransferase (SHMT) is a key enzyme that catalyzes the conversion of glycine and serine. It is highly expressed in a variety of tumors and is a potential target for anti-tumor drugs. This article focuses on the potential of SHMT as a new target for cancer treatment and the preliminary application of its inhibitors in preclinical studies of tumors, providing reference for the development of new targeted drugs for tumors.

Objective:To investigate the effect of baicalin on Th17/Treg cell balance in autoimmune myocarditis(AM)rats and its possible mechanism.Methods:AM rats were grouped into control group,model group,low-dose baicalin group[20 mg/(kg·d)],high-dose baicalin group[100 mg/(kg·d)],and high mobility group B1(HMGB1)inhibitor group;the cardiac ultrasound diagnostic instrument was applied to detect the cardiac function of rats in each group,the cardiac mass index,hematoxylin eosin(HE)staining was applied to detect myocardial pathology,ELISA was applied to detect serum levels of cytokines such as IFN-γ,IL-4,IL-17 and TGF-β,Western blot was applied to detect the expression levels of myocardial HMGB1 and receptor for advanced glycation end product(RAGE)proteins,flow cytometry was applied to detect the proportions of Th17 and Treg cells in the spleen.Results:Com-pared with the control group,the rats in the model group showed myocarditis symptoms and diffuse inflammatory cell infiltration under the endocardium,the left ventricular end diastolic diameter(LVEDd),left ventricular end systolic diameter(LVEDs),IFN-γ,IL-4,IL-17,TGF-β,the proportion of Th17 cells and the expression levels of HMGB1 and RAGE proteins increased,the left ventricular short axis shortening fraction(FS%),ejection fraction(LVEF%),cardiac mass index,and the proportion of Treg cell decreased(P<0.05);compared with the model group,the myocardial interstitial edema and inflammatory cell infiltration were reduced in the ba-icalin low and high dose groups and HMGB1 inhibitor groups,and the proportion of LVEDd,LVEDs,serum IFN-γ and IL-17,spleen Th17 cells and the expression levels of myocardial HMGB1 and RAGE protein were decreased.FS%,LVEF%,heart mass index,IL-4,TGF-β,Treg cell proportion increased;compared with the low-dose baicalin group,the symptoms of myocarditis in the high-dose baicalin group and the HMGB1 inhibitor group were obviously improved,the LVEDd,LVEDs,IFN-γ,IL-17,the proportion of Th17 cells,and the expression levels of HMGB1 and RAGE proteins decreased,the FS%,LVEF%,cardiac mass index,IL-4,TGF-β,and the proportion of Treg cells increased(P<0.05);there was no statistically obvious difference in each index between the high-dose baicalin group and the HMGB1 inhibitor group(P>0.05).Conclusion:The protective effect of baicalin on AM rats is related to the inhibition of HMGB1/RAGE axis activation.

Objective:To assess the clinicopathological characteristics of non-muscle-invasive bladder cancers (NMIBC) with high expression of human epidermal growth factor receptor 2 (HER2) and to examine the prognostic values of HER2 expression in NMIBC patients with intravesical anthracycline instillation.Methods:A total of 221 NMIBC samples diagnosed between January 1, 2017 and April 15, 2024 were collected. Their clinical, diagnostic and treatment features were analyzed. The expression of HER2 protein and the Ki-67 proliferation index were assessed using immunohistochemistry (IHC). For the patients with HER2 high-expression (IHC 3+), the clinical pathological features (age, gender, tumor grade, Ki-67 expression level, tumor size, and tumor number) were compared with those without (i.e., HER2 IHC 0/1+/2+). The impact of HER2 expression on the recurrence-free survival (RFS) of patients with intravesical anthracycline (epirubicin or pirarubicin) instillation after transurethral resection of bladder tumor (TURBT) was evaluated.Results:Among the 221 NMIBC patients, 30 (13.6%) were HER2 IHC 3+, 142 (64.3%) HER 2+, 46 (20.8%) HER2 1+, and 3 (1.4%) HER2 IHC 0. The proportion of high-grade tumors in patients with HER2 high-expression was higher than that in patients without (83.3% versus 44.5%, P<0.001). Additionally, a high Ki-67 index (≥20%) was more commonly noted in HER2 high-expression tumors ( P=0.003). In the patients treated with intravesical anthracycline instillation, HER2 high-expression was associated with a shorter RFS ( P<0.001). Conclusion:HER2 high-expression seems to be not only associated with worse clinicopathological features of NMIBC but also a poor RFS in NMIBC patients treated with anthracycline instillation after TURBT.

Objective To explore the application of cognitive interview in the development of Experience Scale of Patient Sense of Gain.Methods Purposive sampling method were used to select 13 inpatients of a tertiary grade A hospital in Xi'an,14 outpatients from a community health service center in Guangzhou,and 14 inpatients of a tertiary hospital in Guangzhong from February to April 2023 as the interviewees.The measurement tool was the preliminary version of Experience Scale of Patient Sense of Gain which was preliminary phase of research results of the group.Three rounds of cognitive interviews were conducted to explore the respondent'sunderstanding towards the items of the scale.The collected data were analyzed by thematic coding and the items of the scale were revised by the results of interviews and group discussion.Results After the first round of interviews,eight doubtful items were discussed and revised;after the second round of interviews,three doubtful entries were discussed and revised;and after the third round of interviews,all the interviewees were able to correctly understand the scale.Conclusion Cognitive interviews can provide insights into the target population's understanding of Experience Scale of Patient Sense of Gain,effectively addressing the problems of understanding differences between the developer and respondent in the process of the scale development,thus improving the accuracy of the scale.

Objective:To investigate the effect of baicalin on Th17/Treg cell balance in autoimmune myocarditis(AM)rats and its possible mechanism.Methods:AM rats were grouped into control group,model group,low-dose baicalin group[20 mg/(kg·d)],high-dose baicalin group[100 mg/(kg·d)],and high mobility group B1(HMGB1)inhibitor group;the cardiac ultrasound diagnostic instrument was applied to detect the cardiac function of rats in each group,the cardiac mass index,hematoxylin eosin(HE)staining was applied to detect myocardial pathology,ELISA was applied to detect serum levels of cytokines such as IFN-γ,IL-4,IL-17 and TGF-β,Western blot was applied to detect the expression levels of myocardial HMGB1 and receptor for advanced glycation end product(RAGE)proteins,flow cytometry was applied to detect the proportions of Th17 and Treg cells in the spleen.Results:Com-pared with the control group,the rats in the model group showed myocarditis symptoms and diffuse inflammatory cell infiltration under the endocardium,the left ventricular end diastolic diameter(LVEDd),left ventricular end systolic diameter(LVEDs),IFN-γ,IL-4,IL-17,TGF-β,the proportion of Th17 cells and the expression levels of HMGB1 and RAGE proteins increased,the left ventricular short axis shortening fraction(FS%),ejection fraction(LVEF%),cardiac mass index,and the proportion of Treg cell decreased(P<0.05);compared with the model group,the myocardial interstitial edema and inflammatory cell infiltration were reduced in the ba-icalin low and high dose groups and HMGB1 inhibitor groups,and the proportion of LVEDd,LVEDs,serum IFN-γ and IL-17,spleen Th17 cells and the expression levels of myocardial HMGB1 and RAGE protein were decreased.FS%,LVEF%,heart mass index,IL-4,TGF-β,Treg cell proportion increased;compared with the low-dose baicalin group,the symptoms of myocarditis in the high-dose baicalin group and the HMGB1 inhibitor group were obviously improved,the LVEDd,LVEDs,IFN-γ,IL-17,the proportion of Th17 cells,and the expression levels of HMGB1 and RAGE proteins decreased,the FS%,LVEF%,cardiac mass index,IL-4,TGF-β,and the proportion of Treg cells increased(P<0.05);there was no statistically obvious difference in each index between the high-dose baicalin group and the HMGB1 inhibitor group(P>0.05).Conclusion:The protective effect of baicalin on AM rats is related to the inhibition of HMGB1/RAGE axis activation.

OBJECTIVE To construct a predictive model for timely medication retrieval of outpatients， accurately identify high-risk patients with delayed medication retrieval， and provide data support for the development of differentiated registration strategies and resource optimization allocation in smart pharmacies. METHODS Based on 680 568 valid outpatient prescription records from January to March 2025 at the First Affiliated Hospital of Xi’an Jiaotong University， a dual-clustering analysis was conducted using K-means algorithm and Gaussian mixture model （GMM）. An adaptive threshold for medication retrieval time difference was determined by combining contour coefficients， and “timely medication retrieval” and “delayed medication retrieval” were divided to construct binary objective variables； six types of features were screened through a multi-method fusion strategy； the performance of 6 kinds of base learners and 4 kinds of ensemble learning models were evaluated from three dimensions： discrimination， overall performance， and calibration， and explanatory analysis of the models were conducted. RESULTS The results of the dual-clustering analysis showed that the silhouette coefficient of GMM was better than K-means （0.702 4 vs. 0.698 8）， and the final adaptive threshold was determined to be 49.82 min. Among the prescriptions included， 74.99% were for timely medication retrieval and 25.01% were for delayed medication retrieval. Among the 10 candidate models， the Stacking model performed the best， with an area under the test set curve of 0.954 4， F1 score of 0.942 4， accuracy of 0.911 5， Brier score of 0.066， and good discrimination and calibration. The explanatory analysis results of the model showed that its predictions were driven by multiple factors such as patient historical behavior， and diagnostic related characteristics. CONCLUSION This study constructed a timely medication retrieval prediction model for outpatients based on a two-stage adaptive threshold ensemble learning algorithm， which has high accuracy and stability， and can achieve dynamic judgment of patient medication retrieval behavior.