OBJECTIVE To confirm trigger items for adverse drug reaction （ADR） induced by bevacizumab， to identify and analyze the occurrence of related ADR， and to establish a predictive model for severe adverse reaction （SAR） caused by this drug. METHODS Based on the global trigger tool （GTT） theory， and referencing the GTT White Paper， drug package inserts and relevant literature， trigger items for bevacizumab-related ADR were confirmed using a single-round Delphi method. Utilizing these established items， electronic medical records of relevant patients at Guilin People’s Hospital from January 2020 to September 2024 were actively screened via the China Hospital Pharmacovigilance System. Pharmacists then identified and tallied the occurrence of bevacizumab-induced ADR. Data from patients with any positive trigger item served as the study subjects （divided into training and test sets at a ratio of 7∶3）， candidate feature variables were selected from 39 related variables using the Boruta algorithm， and the multivariable Logistic regression analysis was performed with the occurrence of SAR as the dependent variable. Based on these candidate features， Logistic Regression， Extreme Gradient Boosting， Light Gradient Boosting Machine， Random Forest， and Categorical Boosting models were constructed. Model performance was evaluated using metrics including the area under the curve （AUC） of receiver operating characteristic curve and recall rate. The Shapley Additive exPlanations （SHAP） method was applied to analyze and interpret the contribution of each variable. A nomogram was constructed based on the optimal model. RESULTS A total of 38 trigger items for active monitoring of bevacizumab-related ADR were determined， comprising 17 laboratory indicators， 13 clinical manifestations， and 8 intervention measures. In total， 483 patients with positive trigger items were included， and 318 patients with bevacizumab-induced ADR were identified， including 83 SARs. The positive predictive values for the trigger items and cases were 43.57% （708/1 625） and 63.84% （318/483）， respectively. Bevacizumab-induced ADR involved 7 systems/organs， with the hematological system being the most frequently involved （64.15%）. The Boruta algorithm selected 7 vari ables： serum potassium， hematocrit， albumin-to-globulin ratio， prealbumin， hypertension history， age and red blood cell count. Multivariable Logistic regression showed that elevated serum potassium levels were associated with a decreased risk of bevacizumab-induced SAR （OR＝0.234， P ＝0.002）， while a history of hypertension （OR＝2.642， P ＝0.006） and increased age （OR＝1.040， P ＝0.025） were associated with an increased risk. The Logistic Regression model demonstrated superior performance with higher AUC， F1 score and recall rate （0.761， 0.447， 0.607）， compared to other models. SHAP evaluation results indicated that variables such as serum potassium， hematocrit， and age ranked highest in importance. CONCLUSIONS Totally 38 trigger entries have been successfully identified for active screening of bevacizumab-related ADR. Elevated serum potassium levels are a protective factor against bevacizumab-induced SAR， whereas the hypertension history and increased age are risk factors. The Logistic Regression model is the optimal predictive model.

OBJECTIVE To evaluate the effect of clinical pharmacists participating in the treatment of chronic heart failure （CHF） based on the clinical pharmacy pathway （CPP）. METHODS Totally 226 CHF patients recruited from August 24th， 2024 to March 14th， 2025， were divided into an observation group and a control group based on the random number table method， with 113 cases in each group. All patients were treated with conventional therapy. The observation group was additionally given CPP management （including pharmaceutical care during hospitalization， the formulation of individualized discharge medication regimens， and pharmaceutical follow-up after discharge）. The cardiac function parameters at admission， at discharge， at 3 and 6 months after discharge， drug use at 6 months after discharge， economic indicators， as well as the readmission rate and mortality rate at 6 months after discharge were compared between the two groups. Morisky Medication Adherence Scale-8 Items （MMAS-8）， Somatic Self-rating Scale （SSS） and Patient Health Questionnaire-9 （PHQ-9） scores were compared at admission， at discharge and at 3 and 6 months after discharge. RESULTS Six months after discharge， 24 patients dropped out. Eventually， 104 patients in the observation group and 98 patients in the control group completed the study. Compared with at admission， New York Heart Association （NYHA） cardiac functional classification， left ventricular ejection fraction （LVEF） and N -terminal pro-B-type natriuretic peptide （NT-proBNP） of both groups of patients at discharge as well as at 3 and 6 months after discharge were significantly improved； moreover， the improvements at 3 and 6 months after discharge were significantly better than those at discharge. Meanwhile， the above indexes （except for NYHA cardiac functional classification at discharge， NT-proBNP and NYHA cardiac functional classification at 3 months after discharge） of the observation group at discharge， at 3 and 6 months after discharge were significantly better than the control group （ P ＜0.05）. The utilization rates of angiotensin converting enzyme inhibitor （ACEI）/angiotensin Ⅱ receptor blocker （ARB）/angiotensin receptor neprilysin inhibitor （ARNI）， the proportion of β-blockers reaching the target dose， the utilization rate of sodium-glucose linked transporter 2 inhibitor （SGLT2i）， and the proportion of SGLT2i reaching the target dose in the observation group were significantly higher than the control group （ P ＜0.05）， and the proportion of drugs and readmission rate were significantly lower than the control group （ P ＜0.05）. Compared with at admission， MMAS-8 scores of the patients in the observation group at discharge， at 3 and 6 months after discharge were significantly increased， while SSS and PHQ-9 scores were significantly lowered （ P ＜0.05）. And all the above scores gradually decreas ed with the extension of discharge time （ P ＜0.05）. CONCLUSIONS Clinical pharmacists can utilize CPP to significantly improve patients’ cardiac function， medication adherence， somatic symptoms and depression. Additionally， they can significantly improve the utilization rates of ACEI/ARB/ARNI and SGLT2i， as well as the proportion of target doses of β-blockers and SGLT2i， while simultaneously reducing readmission rates.

Objective: To systematically evaluate the therapeutic efficacy and maintenance effects of digital psychological therapies on depressive symptoms among adolescents, so as to provide a reference for clinical practice. Methods: Randomized controlled trial(RCT) investigating digital psychological therapies to improve depressive symptoms among adolescents were searched across databases, including PubMed, Embase, Cochrane Library, Web of Science, China National Knowledge Infrastructure (CNKI), Wanfang database, VIP database, and SinoMed, from database inception to November 20, 2025. Following literature screening, quality assessment, and data extraction, a Meta analysis was performed using Stata 18.0 software. Results: A total of 20 studies involving 2 042 adolescents aged 11-19 were included. The Meta analysis revealed that digital psychological therapies significantly alleviated depressive symptoms in adolescents ( SMD =-0.59, 95% CI =-0.85 to -0.32, P <0.01). The therapeutic effect was sustained at long term follow up ( SMD =-0.21, 95% CI =-0.34 to -0.09, P <0.01). Furthermore, depression scores in the intervention group showed a continued decrease from post intervention to long term follow up ( SMD =-0.28, 95% CI =-0.41 to -0.14, P <0.01). Egger s linear regression test indicated possible publication bias (Kendall s tall=0.28, P <0.01). Conclusions Digital psychological therapies can effectively improve depressive symptoms among adolescents, with stable long term efficacy. However, current evidence remains limited and exhibits substantial heterogeneity. Therefore, further large sample, high quality RCTs are warranted to validate the effectiveness of this intervention.

Objective To investigate the correlation of solute carrier protein 17 family member 1 (SLC17A1) gene single nucleotide polymorphisms (SNPs) and susceptibility to hyperuricemia (HUA) in automotive manufacturing workers. Methods A total of 192 Han male workers diagnosed with HUA were selected as the case group, 192 Han male workers without HUA from the same enterprises were selected as the control group. These workers were determined by the matching factor of age, total length of service, and body mass index by the 1∶1 case-control study method. Peripheral venous blood from the workers was collected for DNA extraction. Two SNPs of SLC17A1 were genotyped by MassArray system. Results The gene frequency distributions of SLC17A1 rs2096386 and rs1183201 of workers in the control group were in consistent with the Hardy-Weinberg equilibrium test (both P>0.05). The allele frequency distribution of rs2096386, and the genotype and allele frequency distribution of rs1183201 were significantly different between workers in the two groups (all P<0.05). There was no significant difference in genotype frequency distribution of rs2096386 between workers in the two groups (P>0.05). The results of conditional logistic regression analysis showed that workers with G allele at rs2096386 increased the risk of HUA [odds ratio (OR)=1.43, 95% confidence interval (CI)=1.01-2.04], workers with T allele at rs1183201 increased the risk of HUA (OR=2.03, 95%CI =1.29-3.19), after adjusting for confounding factors such as serum creatinine, blood urea nitrogen, alanin aminotransferase and aspartate aminotransferase. While workers with TA and TA+AA genotypes at rs1183201 had a lower risk of HUA than those with TT genotype (OR=0.51, 95%CI =0.30-0.85; OR=0.50, 95%CI =0.30-0.83), workers with TA genotype at rs1183201 had a lower risk of HUA than those with TT+AA genotype (OR=0.53, 95%CI =0.32-0.88). Conclusion The polymorphisms at rs2096386 and rs1183201 of SLC17A1 gene may be correlated with HUA susceptibility among automobile manufacturing workers in Guangzhou City.

Objective: To investigate the regional differences in the incidence of urothelial carcinoma among kidney transplant recipients between northern and southern China，so as to provide reference for early diagnosis of this disease. Methods: A comprehensive search was conducted across multiple databases，including CNKI，Wanfang，CBM，and PubMed，using the keywords “kidney transplantation” and “tumor” to collect clinical data from qualified kidney transplant centers.The latest and most complete literature data published by 17 transplant centers in northern China and 14 in southern China were included.Statistical analyses were performed to compare the incidence of post-transplant urothelial carcinoma and non-urothelial malignancies. Results: A total of 37 475 kidney transplant recipients were included，among whom 837 (2.23%) developed post-transplant malignancies，including urothelial carcinoma (366/837，43.73%)，non-urothelial carcinoma (444/837，53.05%)，and malignancies with unspecified pathology (27/837，3.23%).The incidence of malignancies was significantly higher in northern China than in southern China [(2.82±1.39)% vs. (1.67±0.83)%，P=0.011]，with a particularly pronounced difference in the incidence of urothelial carcinoma [(1.68±1.12)% vs. (0.32±0.32)%，P<0.001].No significant difference was observed in the incidence of non-urothelial carcinoma between the two regions [(1.11±0.56)% vs. (1.35±0.65)%，P=0.279].Additionally，female transplant recipients exhibited a higher incidence of malignancies than males in both regions (southern China：2.38% vs. 1.80%; northern China：8.93% vs. 2.52%). Conclusion: The incidence of urothelial carcinoma following kidney transplantation is significantly higher in northern China than in southern China，underscoring the importance of implementing regular tumor screening for kidney transplant recipients，particularly for female patients in northern China，to facilitate early diagnosis and timely intervention.

Objective: To analyze the occupational health literacy (OHL) level and its influencing factors among key populations in the tertiary industry in Lu'an City, Anhui Province, so as to provide a basis for developing targeted health interventions and improving regional occupational health policies. Methods: A stratified cluster random sampling method was employed to select five categories of key populations from the tertiary industry in Lu'an City as study subjects from August to September 2024. Data on gender, age, education level, and OHL were collected through the National OHL Monitoring Questionnaire for Key Populations. The OHL levels were analyzed, and influencing factors of OHL levels among key populations were analyzed using a multivariable logistic regression model. Results: A total of 1 243 individuals were surveyed, comprising 700 (56.32%) males and 543 (43.68%) females. The median age was 42.00 (interquartile range, 17.00) years. There were 609 individuals with OHL, and the OHL level was 48.99%. The OHL levels in fundamental knowledge of occupational health protection, healthy work styles and behaviors, knowledge of occupational health laws, and basic skills for occupational health protection were 84.71%, 60.34%, 43.93%, and 37.09%, respectively. Multivariable logistic regression analysis showed that educational level (primary school and below, OR=0.149, 95%CI: 0.064-0.344; junior high school, OR=0.340, 95%CI: 0.184-0.629; high school, OR=0.408, 95%CI: 0.230-0.723), average monthly personal income (3 000-<5 000 yuan, OR=1.655, 95%CI: 1.092-2.508; 5 000-<7 000 yuan, OR=2.195, 95%CI: 1.302-3.699; ≥7 000 yuan, OR=2.062, 95%CI: 1.016-4.183), employer nature (private enterprises, OR=2.992, 95%CI: 1.569-5.443), and industry category (education, OR=3.423, 95%CI: 1.407-8.327; courier / food delivery services, OR=0.459, 95%CI: 0.268-0.787; healthcare, OR=7.539, 95%CI: 3.255-17.461) were statistically associated with the OHL level among key population. Conclusion The OHL level among key population in the tertiary industry of Lu'an City can be further enhanced, with educational level, average monthly personal income, employer nature, and industry category identified as the primary influencing factors.

Objective To analyze the epidemiological and pathogenic characteristics and incidence trend of hand-foot-mouth disease (HFMD) in Jinshan District, Shanghai, and to provide data support for the prevention and control of HFMD． Methods Case information and etiological data of HFMD in Jinshan District from 2018 to 2023 were collected. Descriptive epidemiological methods were used to analyze the temporal, spatial and population distribution of HFMD cases and their etiological composition and changes. An autoregressive integrated moving average (ARIMA) model was established to predict the incidence trend of HFMD in 2024. Results From 2018 to 2023, a total of 5,979 cases of HFMD were reported in Jinshan District, with an average annual incidence rate of 123.00/100,000. There were no reports of severe cases or deaths. The incidence of HFMD showed unimodal distribution in 2018 and 2023, bimodal distribution in 2019, and there was no obvious peaks in 2020—2022. The town with the highest average annual incidence rate was Jinshanwei Town, and the town with the lowest average annual incidence rate was Fengjing Town. The male-to-female ratio of the cases was 1.42:1. Most of the cases were under 5 years old, and scattered children were the most common occupation. CVA6 was the predominant pathogen, but EV-A71 was not detected. The optimal fitting prediction model was SARIMAX (2, 0, 0) × (1, 0, 0, 12), and the model predicted a trend of decline after rising first in the incidence of HFMD in Jinshan District in 2024. Conclusion There are obvious temporal, spatial and population differences in HFMD incidence in Jinshan District, and the dominant pathogen of HFMD is CVA6. Prediction data can be used to further strengthen epidemic monitoring, timely detect new variants, and provide the basis for timely adjustment of prevention and control measures of HFMD.

This paper reviews the published articles of recent years on the application of deep learning methods in automatic localization of acupoint, and summarizes it from 3 key links, i.e. the dataset construction, the neural network model design, and the accuracy evaluation of acupoint localization. The significant progress has been obtained in the field of deep learning for acupoint localization, but the scale of acupoint detection needs to be expanded and the precision, the generalization ability, and the real-time performance of the model be advanced. The future research should focus on the support of standardized datasets, and the integration of 3D modeling and multimodal data fusion, so as to increase the accuracy and strengthen the personalization of acupoint localization.
Deep Learning ; Acupuncture Points ; Humans ; Neural Networks, Computer