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.

Objective:To analyze the current management status of real-world studies (RWS) in the medical institutions in China and suggest improvement focus for the management optimization.Methods:Surveys were conducted in 81 medical institutions nationwide. Convenience sampling was used to recruit survey subjects, and data were collected through self-administered questionnaires, followed by statistical analysis using descriptive methods.Results:The survey results indicated that 92.6% (75/81) of the medical institutions surveyed had undertaken RWS projects, with electronic medical records being the primary data source (89.3%, 67/75). Retrospective and prospective observational studies were the main types of study designs. Additionally, 96.3% (78/81) of the research subjects indicated that their medical institution expressed willingness to participate in or undertake RWS projects in the future. In terms of management, all types of RWS projects were managed by clinical trial center (24.0, 18/75), but differences existed in the management practices among medical institutions. Moreover, the challenges in data quality and standardization, study design and staff training, data and privacy protection and information technology support appeared in the management of RWS projects.Conclusions:It suggests to optimize the management processes of RWS projects in medical institutions and improve relevant laws and regulations to promote the development of RWS in China.

In the past decade, real-world data (RWD) research has undergone significant transformations due to data aggregation and processing technologies. However, there is still a lack of consensus regarding the scope of RWD applications and the value of real-world evidence (RWE). This study briefly outlined the origins of the concept of RWD study and its early research scope to promote further development in this area. We also reviewed the understanding of RWD applications and research models from the five perspectives of healthcare professionals, medical institutions, decision-making departments, cross-regional cooperation model, and the practice of the One-Health model. Finally, we systematically summarized the renewed understanding of the value of RWE while looking ahead to the challenges and future developments in this field.

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.

The selection of an appropriate control group is a critical component of pharmacoepidemiologic research.This article provides an interpretation of the control selection methods outlined in the Guide on Methodological Standards in Pharmacoepidemiology in China(2nd edition).According to the 2nd edition,studies are categorized into interventional and non-interventional research.In interventional research,control group options include placebo controls,no-treatment controls,active controls,and dose-response controls.For non-interventional research,the gold standard design is the active comparator new user(ACNU)design.When the ACNU design is not feasible,alternative control group strategies should be selected based on the research objective,data sources,exposure characteristics,and potential confounding.These alternatives may include non-user comparators,prevalent user comparators,self-controlled comparators,and external controls.Finally,this article compares the applicability,strengths,and limitations of various control group types.It aims to provide methodological guidance for the scientific selection of control groups in pharmacoepidemiologic studies and to support the conduct of high-quality research.

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 explore whether pachymic acid （Pac） regulates mitochondrial autophagy mediated by the PTEN- induced kinase 1 （PINK1）/Parkin RBR E3 ubiquitin-protein ligase （Parkin） signaling pathway to alleviate myocardial injury in coronary heart disease （CHD） rats. METHODS SD rats were divided into control （Con） group， CHD group， Pac low-dose group （Pac-L group）， Pac high-dose group （Pac-H group）， Pac-H+PINK1/Parkin signaling pathway inhibitor group （Pac-H+3-MA group）， with 10 rats in each group. Except for the Con group， CHD models were established in the remaining groups of rats. After successful modeling， the rats in each group were intraperitoneally injected with the corresponding drugs or normal saline. After continuous intervention for 4 weeks， the left ventricular ejection fraction （LVEF）， left ventricular end-diastolic volume （LVEDV）， left ventricular end-systolic volume （LVESV）， and mean arterial pressure （MAP） of the rats were detected. The levels of creatine kinase isoenzyme （CK-MB）， lactate dehydrogenase （LDH）， cardiac troponin I （cTnI）， and cardiac troponin T （cTnT） in the serum， as well as the levels of tumor necrosis factor-α （TNF-α）， interleukin-10 （IL-10）， IL-1β， reactive oxygen species （ROS）， malondialdehyde （MDA） in the myocardial tissue， and the activities of catalase （CAT） and superoxide dismutase （SOD）， as well as the expression levels of p62， cleaved caspase-3， Parkin， PINK1 proteins and the ratio of microtubule-associated protein 1 light chain 3 Ⅱ （LC3Ⅱ）/LC3Ⅰ ratio were measured. The morphology of myocardial tissue and mitochondrial autophagic vesicles were observed， and the number of mitochondrial autophagic vesicles per unit area and the rate of cardiomyocyte apoptosis were counted. RESULTS Compared with CHD group， LVEF， MAP， IL-10 levels， CAT and SOD activities， p62， Parkin， PINK1 protein expressions， LC3Ⅱ/LC3Ⅰ ratio， the numbers of mitochondrial autophagic vesicles per unit area in the Pac-L and Pac-H E-mail：hzdpft@163.com groups were increased significantly （P＜0.05）； the levels of LVEDV， LVESV， CK-MB， LDH， cTnI， cTnT， TNF-α， IL-1β， ROS and MDA， cell apoptosis rates， and protein expression of cleaved caspase-3 were all decreased significantly （P＜0.05）； and the changes in various indicators were more pronounced in the Pac-H group （P＜0.05）； both groups showed varying degree of improvement in myocardial histopathological morphology. Compared with the Pac-H group， the aforementioned indicators in rats from the Pac-H+3-MA group were all significantly reversed （P＜0.05）. CONCLUSIONS Pac may promote mitochondrial autophagy in cardiomyocytes of CHD rats by activating the PINK1/ Parkin signaling pathway， thereby reducing inflammatory responses and oxidative stress and improving myocardial injury.

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 explore the mediating role of anxiety/depression emotions between sleep quality and cognitive function in adolescents with attention deficit hyperactivity disorder(ADHD).Methods:A cross-sectional study design was adopted, involving 204 ADHD adolescents aged 12-18 who were treated between January 2021 and November 2023.All participants were assessed using the Pittsburgh sleep quality index(PSQI)for sleep quality, the self-rating anxiety scale(SAS) and self-rating depression scale(SDS) for emotional states.Four cognitive information processing processes(planning, simultaneous processing, attention, and successive processing) were evaluated by the Das-Naglieri cognitive assessment system(DN: CAS). Data were statistically analyzed using SPSS 23.0 and Zstats software, including descriptive statistics, Spearman correlation analysis, and mediation effect analysis.Results:Among ADHD adolescents, girls exhibited significantly higher rates of sleep disturbance(71.59%(63/88) vs 43.97%(51/116), χ2=15.490, P<0.001)and depressive emotion(47.73%(42/88) vs 33.62%(39/116), χ2=4.159, P=0.041)compared to boys.High school students had a significantly higher rate of sleep disturbance than middle school students(64.84%(59/91) vs 48.67%(55/113), χ2=5.341, P=0.021). Spearman correlation analysis revealed that sleep quality(8.00(6.00, 11.00)) in ADHD adolescents was significantly and positively correlated with anxiety(38.00(32.00, 46.00); r=0.504, P<0.01) and depression(46.00(39.00, 54.00); r=0.427, P<0.01). And sleep quality, anxiety and depression were significantly and negatively correlated with the DN: CAS total score(109.5(91.25, 123.75); r=-0.158--0.237, P<0.05). Mediation analysis indicated that anxiety mediated the relationship between sleep quality and attention function(indirect effect β=-0.159, Bootstrap 95% CI=-0.287--0.046). Conclusion:ADHD adolescents exhibit complex interactions among sleep quality, emotion, and cognitive function, sleep quality indirectly affects attention function through anxiety.

The selection of an appropriate control group is a critical component of pharmacoepidemiologic research.This article provides an interpretation of the control selection methods outlined in the Guide on Methodological Standards in Pharmacoepidemiology in China(2nd edition).According to the 2nd edition,studies are categorized into interventional and non-interventional research.In interventional research,control group options include placebo controls,no-treatment controls,active controls,and dose-response controls.For non-interventional research,the gold standard design is the active comparator new user(ACNU)design.When the ACNU design is not feasible,alternative control group strategies should be selected based on the research objective,data sources,exposure characteristics,and potential confounding.These alternatives may include non-user comparators,prevalent user comparators,self-controlled comparators,and external controls.Finally,this article compares the applicability,strengths,and limitations of various control group types.It aims to provide methodological guidance for the scientific selection of control groups in pharmacoepidemiologic studies and to support the conduct of high-quality research.