Objective: To investigate the impact of disposable plastic food container usage on the executive function among primary school students, so as to provide the evidence for the formulation of relevant health policies. Methods: From November 2023 to May 2024, a convenience sampling method was employed to select 1 118 grade 1-3 students from three primary schools in a central district of Chongqing. A self developed questionnaire was used to collect demographic characteristics and data on disposable plastic food container usage. Executive function of primary school students was assessed using the Childhood Executive Functioning Inventory (CHEXI). Multivariate linear regression analysis was conducted to explore the associations between disposable plastic food container usage and heating with executive function among primary school students. Results: Median scores for working memory, inhibition and total executive function among primary school students were 32 (26, 39), 33 (28, 38), and 66 (54, 75), respectively. Multivariate linear regression analysis showed that among girls, higher frequencies of eating meals from plastic lunchboxes were associated with higher CHEXI working memory scores ( β =1.29), inhibition scores ( β =1.57), and total executive function scores ( β =2.85) ( P <0.05). Compared to girls who did not use plastic cups or drank bottled water, those who used plastic cups for drinking or drank bottled water had higher scores in working memory ( β =2.63), inhibition ( β =2.10), and total executive function ( β =4.73); compared to girls who did not eat canned food from metal cans, those who ate such food had higher scores in working memory ( β =3.62), inhibition ( β =1.89), and total executive function ( β =5.50) ( P <0.05).Among boys, higher frequencies of eating meals from plastic lunchboxes were associated with higher inhibition scores ( β =1.13) ( P <0.05). Compared to girls who ate with a plastic lunch box and did not heat it when they ate,girls who more frequent heating plastic lunchboxes with food inside had higher working memory scores ( β = 5.39), inhibition scores ( β =4.29), and total executive function scores ( β = 9.68) ( P <0.05). Conclusions The use of disposable plastic food containers may adversely affect executive function of primary school students, with a more pronounced effect observed in girls. Strengthened regulation of disposable plastic products and health education are urgently needed.

Primary biliary cholangitis （PBC） is a chronic progressive autoimmune liver disease that is often comorbid with other connective tissue diseases （CTDs）， and such comorbidity can significantly alter the natural course or clinical phenotype of PBC or CTDs， limiting available therapeutic drugs and complicating clinical decision-making. Due to the involvement of the interdisciplinary subjects of hepatology， rheumatology， and clinical immunology and a paucity of large-scale cohort data and in-depth basic research， there is a limited understanding of such comorbidity in clinical practice， which increases the complexity of clinical diagnosis and treatment. This article summarizes the comorbidity of PBC with common CTDs such as Sjögren’‍s syndrome， systemic sclerosis， systemic lupus erythematosus， and idiopathic inflammatory myopathies， and analyzes related immune mechanisms， clinical manifestations， diagnostic challenges， treatment strategies， and prognosis. It is expected to establish PBC-CTD comorbidity cohorts through future multidisciplinary collaborations， focus on genetic background， immune mechanisms， and multi-omics approaches， elucidate pathogenesis and novel therapeutic targets， and improve the prognosis of patients by optimizing treatment strategies through precision medicine and artificial intelligence.

Primary biliary cholangitis(PBC)is a chronic progressive autoimmune liver disease that is often comorbid with other connective tissue diseases(CTDs),and such comorbidity can significantly alter the natural course or clinical phenotype of PBC or CTDs,limiting available therapeutic drugs and complicating clinical decision-making.Due to the involvement of the interdisciplinary subjects of hepatology,rheumatology,and clinical immunology and a paucity of large-scale cohort data and in-depth basic research,there is a limited understanding of such comorbidity in clinical practice,which increases the complexity of clinical diagnosis and treatment.This article summarizes the comorbidity of PBC with common CTDs such as Sj?gren's syndrome,systemic sclerosis,systemic lupus erythematosus,and idiopathic inflammatory myopathies,and analyzes related immune mechanisms,clinical manifestations,diagnostic challenges,treatment strategies,and prognosis.It is expected to establish PBC-CTD comorbidity cohorts through future multidisciplinary collaborations,focus on genetic background,immune mechanisms,and multi-omics approaches,elucidate pathogenesis and novel therapeutic targets,and improve the prognosis of patients by optimizing treatment strategies through precision medicine and artificial intelligence.

Objective:To understand the characteristics of faculty in high-level public health schools in China, and analyze the differences in age, area and school level.Methods:Based on the internet information, the faculty information of 18 high-level public health schools was collected for a descriptive analysis on faculty characteristics.Results:There were 1 642 faculty members in the schools of public health in China, in whom 51.8% were women, 92.8% had doctorate, 32.4% had postdoctoral experience and 56.8% were former students staying to teach. The average age of the faculty members was (45.6±9.8) years. Meanwhile the top three study subjects were epidemiology and health statistics (31.0%), occupational health and environmental sanitation (16.5%), and health toxicology (16.3%). In the faculty members aged >40 years, 90.2% had doctorate, 62.6% were former students staying to teach, and 24.7% had no educational background of public health. The proportions of faculty members aged ≤40 years in the three groups mentioned above were 98.2%, 45.8% and 39.1% respectively. In terms of study subject, big data study were mainly conducted in the schools with top subject ranking and the schools in developed areas.Conclusions:The public health faculty was characterized by cross education background and high capability. The study subjects and sub-disciplines varied with schools and areas.

Objective:To systematically review the progress in the method development and application of distributed learning in the estimation of epidemiological effect and provide methodological reference for multi-center studies.Methods:We conducted a literature retrieval for English papers published up to December 31, 2023 by using keywords of "health/medical big data" and "distributed/federated learning". After consulting experts, we set criteria of paper inclusion and exclusion and created a framework for data extraction. We collected information about basic study details, including method, application, and evaluation. Two researchers independently screened the papers and extracted information. We used EndNote 20 for the management of literatures and EpiData for the management of data.Results:A total of 3 444 papers were collected, and 29 papers were included in the final analysis. Most of the papers (25, 86.2%) were published in or after 2019, and the papers were mainly from the United States (21/29, 72.4%). For the estimation of epidemiological effects, 22 distributed learning methods had been developed, including methods for logistic regression (8), Cox regression (8), Poisson regression (2), and generalized linear mixed model (GLMM) (4), as well as three platforms for distributed analysis (VLP, Vantage6, AusCAT). The 29 papers described 45 applications, with 20 (44.4%) focusing on the establishment of prediction model and 25 (55.6%) on association analysis. Importantly, except for GLMM, current distributed learning methods can estimate effects with little bias in 1-3 rounds of communication. These methods show less bias compared with meta-analysis, especially in the address of data heterogeneity and rare outcomes. However, less studies examined how differences in data structure and sparse data affect results, an area that requires further research.Conclusion:While distributed learning shows promise in epidemiological effect estimation, it is still in early development, requiring further research on data heterogeneity handling and communication efficiency improvement.

Evidence-based public health, as the forefront of modern public health practice, has increasingly important in public health field. However, a significant gap remains between the available evidence and its practical application. Effectively disseminating and implementing evidence-based public health practice in real-world settings has become a key challenge in contemporary public health research. In this context, Implementation Science has emerged as a vital discipline. This paper explores the critical role of Implementation Science in public health, reviews the origins and core components of the Consolidated Framework for Implementation Research (CFIR), and analyzes the current application of CFIR in public health through bibliometric methods. Additionally, it discusses specific examples to further elucidate the steps involved in using the CFIR and its application contexts. The findings indicate that since 2015, research on CFIR in public health has progressively increased, showing a continuous upward trend. CFIR applications mainly address context-specific facilitators, health decision-making, barrier and facilitator identification, and community-based participatory evaluation, predominantly employing qualitative and mixed-methods research. This paper not only reviews and analyzes the current use of CFIR in public health but also provides a detailed discussion on its application. The goal is to offer valuable insights for the development of Implementation Science research within China's public health sector.

With the rise of personalized medicine and the rapid development of big data technology, medical prediction models have become increasingly important in disease diagnosis, prognosis assessment, and risk stratification. However, class imbalance is a common problem in medical data, which can result in models being overly trained toward the majority class rather than the minority class, influencing the detection power and clinical application value. This paper systematically summarizes traditional methods in addressing class imbalance, including data pre-processing and algorithm level strategies, and introduces the applications of new technologies such as generative adversarial networks and transfer learning and suggests key considerations and potential research focus for addressing class imbalance to provide reference for researchers to select appropriate strategies.

Objective:To describe the hotspots and application trends of artificial intelligence (AI) in epidemiology in the past decade and analyze its advantages and challenges.Methods:The literatures with AI and epidemiology related keywords were systematically retrieved from Web of Science and China National Knowledge Infrastructure from 2014 to 2024. CiteSpace was used for bibliometric analysis of publication volume, keyword co-occurrence, clustering, emergence and cited literature co-occurrence analysis.Results:A total of 5 389 English papers and 1 659 Chinese papers were included, showing an increasing publication trend. High-frequency Chinese keywords included prediction, influencing factor, and machine learning, while English keywords frequently used were machine learning, prediction, and artificial intelligence. The Chinese keywords formed 14 clusters such as epidemiological characteristic, dietary pattern, and elderly individual, and the English keywords formed 21 clusters including prediction model, risk factor, and adult. In international studies, health policy, COVID-19, and digital health were the emerging frontier keywords. Eleven core papers were selected, covering key areas like traffic accident risk assessment, public health big data application, and deep learning in medical diagnosis.Conclusions:This study systematically summarized the research hotspots and development trends of AI applications in epidemiology over the past decade by using bibliometric methods, which indicated that current AI-based epidemiological studies are still in the exploratory phase, with the coexisting of both advantages and challenges. Continued attention should be paid to the future development of this field.

Dupilumab is a fully human monoclonal antibody that exerts its effects by targeting and binding to the interleukin (IL)-4 receptor alpha subunit, thereby blocking IL-4/13 signaling and downregulating Type 2 inflammation. It has been approved in China for the treatment of moderate-to-severe atopic dermatitis (AD) in patients aged 6 months and older, as well as prurigo nodularis (PN) and bullous pemphigoid in adults. Given its approval age and safety profile, the application of Dupilumab in children has unique advantages, and it has been widely used in the treatment of various Type 2 inflammation-associated and other pediatric dermatologic conditions.Early and proactive management of type 2 inflammatory dermatoses may have preventive and therapeutic implications for modulating the atopic march and type 2 inflammatory comorbidities. This article reviews and summarizes the recent applications of dupilumab in pediatric dermatological diseases.

Objective To develop and validate a prediction model to identify high-risk individuals who are at-risk to develop acute liver injury(ALI)within 180 days in new statin users,and to support early clinical intervention.Methods Data were sourced from the Yinzhou Regional Health Information Platform,covering statin initiators aged 18 years and older from January 1,2010,to October 31,2021.The dataset was divided into a derivation cohort and a temporal validation cohort based on the time of statin initiation.Predictors were selected using LASSO regression,and the model was constructed using the extreme gradient boosting(XGBoost)algorithm combined with cost-sensitive learning.Model performance was evaluated using Brier scores,Harrell's C-index,and calibration curves.Results A total of 126,440 statin initiators were included,with 90,542 in the derivation cohort and 35,898 in the validation cohort.Within 180 days of initial statin use,412(0.33％)patients developed ALI,including 305(0.34％)in the derivation cohort and 107(0.30％)in the validation cohort.The final model incorporated 16 predictors,which included demographic characteristics,lifestyle factors,family history,medical history,statin use,and concomitant medication use.The model demonstrated excellent overall performance[Brier score=0.0043,95％CI(0.0038,0.0049)],discrimination[Harrell's C-index=0.761,95％CI(0.725,0.794)],and calibration in internal validation.In temporal validation,the model also performed well[Brier score=0.0044,95％CI(0.0036,0.0052),Harrell's C-index=0.703,95％CI(0.614,0.781)].Conclusion This study develope and validate a prediction model for ALI in statin users,providing clinicians with a reliable tool for individualized risk assessment.This model can help achieve risk stratification and reduce the occurrence of ALI.