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.

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 emergence of artificial intelligence(AI)has had a significant impact on medical research and practice,both in terms of the number of studies and research paradigms,and has become an important tool for the development of pharmacoepidemiology.However,traditional AI has faced many challenges,while facilitating pharmacoepidemiology research,such as complex data processing,difficulty in identifying drug exposures and potential outcomes,and time-consuming and laborious study design and implementation.The rapid development of generative AI,represented by large language models(LLMs),has demonstrated a unique potential to enhance research efficiency,shift research paradigms,and facilitate knowledge discovery.LLMs are equipped with natural language understanding and generation capabilities.Through deep mining of multi-dimensional data resources,LLMs can quickly and accurately extract,analyze,summarize,and present the required information,which can not only help drug discovery,drug repurposing,pharmacovigilance and other pharmacoepidemiological tasks,but also provide powerful support for the whole process of research protocol design,data analysis,result interpretation and paper publication.Driven by LLMs,pharmacoepidemiology research is gradually moving into a new stage based on big data and automated analysis.Of course,LLMs also have problems of data bias,"illusion"of results,and ethical and legal regulation.By strengthening interdisciplinary cooperation,establishing a standardized evaluation system,improving ethical and regulatory guidance,enhancing data quality,strengthening practitioner training and capacity building,and promoting human-machine collaborative research modes,it is expected that the potential of LLMs in pharmacoepidemiology will be fully released,and it will provide a more scientific,rapid,and efficient technological support for drug regulation and public health decision-making.

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.

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.

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.

The emergence of artificial intelligence(AI)has had a significant impact on medical research and practice,both in terms of the number of studies and research paradigms,and has become an important tool for the development of pharmacoepidemiology.However,traditional AI has faced many challenges,while facilitating pharmacoepidemiology research,such as complex data processing,difficulty in identifying drug exposures and potential outcomes,and time-consuming and laborious study design and implementation.The rapid development of generative AI,represented by large language models(LLMs),has demonstrated a unique potential to enhance research efficiency,shift research paradigms,and facilitate knowledge discovery.LLMs are equipped with natural language understanding and generation capabilities.Through deep mining of multi-dimensional data resources,LLMs can quickly and accurately extract,analyze,summarize,and present the required information,which can not only help drug discovery,drug repurposing,pharmacovigilance and other pharmacoepidemiological tasks,but also provide powerful support for the whole process of research protocol design,data analysis,result interpretation and paper publication.Driven by LLMs,pharmacoepidemiology research is gradually moving into a new stage based on big data and automated analysis.Of course,LLMs also have problems of data bias,"illusion"of results,and ethical and legal regulation.By strengthening interdisciplinary cooperation,establishing a standardized evaluation system,improving ethical and regulatory guidance,enhancing data quality,strengthening practitioner training and capacity building,and promoting human-machine collaborative research modes,it is expected that the potential of LLMs in pharmacoepidemiology will be fully released,and it will provide a more scientific,rapid,and efficient technological support for drug regulation and public health decision-making.

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.