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.

Objective To investigate the association between the variability of remnant cholesterol inflammatory index(RCII),a novel composite biomarker,and the risk of stroke,in order to provide a theoretical basis for stroke prevention.Methods A prospective cohort study was conducted on 38 659 Kailuan individuals who took annual physical examinations in 2006,2008,and 2010.These subjects were grouped based on the quartiles of RCII variability,which was represented by standard deviation(SD)and average real variability(ARV),and were followed up every 2 years,with the occurrence of stroke(including ischemic and hemorrhagic strokes),death,or the end of follow-up on December 31,2022 as the endpoints.Kaplan-Meier method was used to calculate the cumulative incidence rate of endpoint events across different groups,and log-rank test was used to compare the difference of cumulative incidence of endpoint events in each group.Multivariate Cox proportional hazards regression model was adopted to analyze the association between RCII variability and risk of stroke.Results Among the 38 659 participants,a total of 2 539 strokes occurred during a mean follow-up period of 11.22±2.26 years.After adjusting confounding factors,when the participants were grouped by the quartiles of RCII-SD,the hazard ratio(HR)for stroke was 1.034(95%CI:0.917～1.167,P=0.584),1.146(95%CI:1.018～1.290,P=0.025),and 1.209(95%CI:1.066～1.370,P=0.003),respectively in the Q2,Q3,and Q4 groups,when compared with the Q1 group(Ptrend<0.05).When they were grouped by the quartiles of RCII-ARV,the HR for stroke was 1.008(95%CI:0.894～1.136,P=0.901),1.109(95%CI:0.986～1.248,P=0.085),and 1.152(95%CI:1.018～1.303,P=0.025),respectively,in the Q2,Q3,and Q4 groups,when compared with the Q1 group.Furthermore,both sensitivity and stratified analyses yielded similar results.Conclusion RCII variability is significantly associated with stroke,and the risk of stroke is gradually increasing with increment of the variability.Countermeasures Relevant authorities can focus on reducing RCII variability as a central objective by establishing regular monitoring mechanism,strengthening lifestyle interventions,and standardizing dietary,exercise,and weight management in order to suppress the index fluctuations.The principle of stable lipid-lowering in medication and optimization of therapeutic regimens with stable efficacy should be emphasized to prevent the risk of additional vascular damage.

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 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.

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.

Objective:To investigate the eye movement characteristics of non-emotional pictures and emotion-al pictures in college students with different self-rating levels of depression.Methods:The Baker Depression Ques-tionnaire(BDI-2)was used to select 20 college students in high score group(BDI-2 score≥ 18 points)and 20 col-lege students in low score group(BDI-2 score ≤2 points).In experiment 1 the different finding tasks were used to investigate the difference of eye movement features between the two groups when viewing non-emotional pic-tures.In experiment 2 the expression recognition tasks were used to investigate the difference of the eye movement features between the two groups when viewing emotional pictures.Results:The results of experiment 1 showed that when completing the judgment task,the fixation times of each interest area of non-emotional images was smaller in the high score group than in the low score group(P＜0.01).The results of experiment 2 showed that the reaction time was longer to sad faces than to happy faces in the high score group(P＜0.05),and the correct rate to sad faces was higher in the high score group than in the low score group(P＜0.05).For the eye interest area,the first fixation arrival time was earlier in the high score group than in the low score group(P＜0.001),and the first fixa-tion time and total fixation time were shorter in the high score group than in the low score group(Ps＜0.01).Con-clusion:College students with high self-rating level of depression show a decrease of interest when viewing non-e-motional pictures,and show an advantage in processing negative emotional information and avoiding eye gaze when viewing emotional pictures.

Oromucosal drug delivery preparations offer advantages such as convenient administration,suitability for patients with dysphagia,rapid onset of action,and avoidance of first-pass metabolism in the liver.The 2020 edition of the Chinese Pharmacopoeia,EP11.0,BP2022,USP44-NF39,and JP18 all include relevant standards for the quality control of different oromucosal drug delivery systems.This article compares the differences in general re-quirements for oromucosal formulations among different countries and provides an overview of inspection items for marketed oral mucosal formulations and those documented in pharmacopoeias both domestically and internationally.Foreign pharmacopoeias include a wide range of oromucosal drug delivery formulations,with more refined quality control measures for systemic action.These findings can serve as a reference for the improvement and enhancement of standards for oromucosal drug delivery systems in China.

Objective:To investigate the relationship between the in vivo metabolic ratio (MR) of Aripiprazole (ARI) and CYP2D6 gene polymorphism in children with tic disorders (TD) and its effect on dose-exposure (DE), so as to promote precision drug use. Methods:In this study, a real-world observational study design was used to collect 81 children with TD who visited the Department of Neurology of Wuhan Children′s Hospital from January 2021 to January 2024, the concentration of the prototype drug and the main metabolite Dehydroaripiprazole (DARI), the detection data of CYP2D6 single nucleotide gene polymorphism (SNP) and clinical data were collected, and the relationship between the DARI/ARI metabolic ratio (MR) and CYP2D6 metabolic type was analyzed by the receiver operating characteristic (ROC) curve.A DE model between ARI dose and steady-state trough concentration was established by population modeling, and the effects of CYP2D6 metabolic type, MR and body weight on DE were analyzed.Goodness-of-fit diagram (GOF), visual predictive check (VPC) and prediction error analysis were used to verify the prediction performance of the DE model.Results:ROC analysis showed that there was a correlation between MR and CYP2D6 metabolic type, the MR sensitivity cut-point of CYP2D6 ultrafast metabolic (UM) patients was 0.399, and the cut-point of MR in intermediate metabolic (IM) patients was 0.252.Based on this, the patients were divided into three categories: MR TYPE Ⅰ: MR≥0.399, MR TYPE Ⅱ: 0.252

Objective:To analyze the status and influencing factors of empathy fatigue in hospice nurses based on ABC-X model (A: stressor event; B:resources available to a family; C: family sperceptions of the stressor; X: likelihood of crisis), so as to provide a reliable basis for developing comprehensive intervention strategies.Methods:A total of 325 nurses engaged in hospice care in China from April 2022 to June 2022 were selected by convenient sampling method. The influencing factors of empathy fatigue of hospice care nurses were analyzed by ABC-X model (working environment, resilience and coping style). The hospice care nurses were investigated by self-made general questionnaire, Chinese version of Empathy Fatigue Scale, Simplified Coping Style Questionnaire, Coping Style Resilience Scale and Nursing Work Environment Scale. The statistical analysis was performed by SPSS.26.0 statistical software.Results:There were 316 females and 9 males with age of (33.0 ± 7.9) years old. The total score of empathy fatigue in 325 hospice nurses was (91.16 ± 9.60) points, the scores of empathy satisfaction, ocupational burnout and secondary traumatic stress were (31.35 ± 6.01), (28.43 ± 5.86), (31.38 ± 5.76) points respectively. The scores of positive coping style, negative coping style, psychological resilience and nursing working environment were (37.46 ± 5.69), (21.28 ± 6.90), (89.84 ± 16.46), (117.13 ± 19.95) points respectively. The negative predictive factor for empathy satisfaction among nurses with the professional title of palliative care ( t=-4.22, P<0.05), and the positive predictive factors for simple coping strategies, psychological resilience, and nursing work environment ( t=4.52, 3.05, 9.03, all P<0.05), could explain 56.7% of the total variation. Psychological resilience, simplified coping strategies, nursing work environment were negative predictive factors for occupational burnout among hospice nurses ( t=-6.93, -3.54, -2.51, all P<0.05), while work nature was a positive predictive factor ( t=2.36, P<0.05), which could explain 49.4% of the total variation. Simplified coping strategies, psychological resilience, and nursing work environment were all negative predictors of secondary traumatic stress in hospice nurses ( t=-5.40, -3.25, -5.95, all P<0.05), which could explain 48.8% of the total variation. Conclusions:Based on the ABC-X model, it is found that the empathic fatigue of hospice nurses is mainly affected by the nursing work environment, mental resilience and coping styles. It is necessary for nursing managers to actively take measures to improve the working environment and coping styles of nurses, enhance their mental resilience and reduce their empathic fatigue.

Objective To investigate the infection of Chlamydia pneumoniae and mycoplasma pneumoniae in adults and their association with atherosclerosis,and to provide theoretical guidance for the prevention of such diseases. Methods A case-control study was used to collect 362 patients who were diagnosed with atherosclerosis from January 2019 to December 2021 in Department of Sichuan Bazhong Central Hospital, and 370 cases who were admitted to the hospital during the same period of physical examination without any cardiovascular disease were selected as the control group, and whole blood samples of the two groups of study subjects were collected, and the infection of Chlamydia pneumoniae and mycoplasma pneumoniae was detected by PCR. Results The infection rate of Chlamydia pneumoniae was 35.49%, the infection rate of mycoplasma was 40.37%, and the co-infection rate was 11.37%；The infection rate of Chlamydia pneumoniae in the control group was 12.04%, the infection rate of mycoplasma was 15.83%, and the coinfection rate was 3.14%, and the difference between the two groups was statistically significant ( χ²=10.926, P=0.023). The effects of mycoplasma, chlamydia, and co-infection on atherosclerotic patients have sex differences, mainly manifested as higher infection rates in men; In addition, the effects of mycoplasma, chlamydia, and co-infection on atherosclerosis patients varied by age, mainly in the 55-70 years age group (P<0.05). Multivariate logistic regression results showed that Chlamydia pneumoniae infection was a risk factor for atherosclerosis (OR=1.303, 95%CI: 1.043-1.677) in the whole population, and chlamydia pneumoniae (OR=1.472, 95% CI: 1.037-1.556), mycoplasma (OR=2.003, 95%CI: 1.637-3.842) and co-infection in men (OR=1.937, 95%CI: 1.380-2.184) were risk factors for atherosclerosis, while co-infection in women (OR=1.699, 95%CI: 1.263-1.765) was a risk factor for atherosclerosis. Conclusion Chlamydia pneumoniae and mycoplasma infection are risk factors for atherosclerosis, and their impact on male groups is greater, and more attention needs to be paid to them.