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Abstract Purpose Previous studies have reported the potential impact of immune cells on kidney stone disease (KSD), but definitive causal relationships have yet to be established. The purpose of this paper is to elucidate the potential causal association between immune cells and KSD by Mendelian randomization (MR) analysis. Methods In our study, a thorough two-sample Mendelian randomization (MR) analysis was performed by us to determine the potential causal relationship between immune cell traits and kidney stone disease. We included a total of four immune traits (median fluorescence intensity (MFI), relative cellular (RC), absolute cellular (AC), and morphological parameters (MP)), which are publicly available data. GWAS summary data related to KSD (9713 cases and 366,693 controls) were obtained from the FinnGen consortium. The primary MR analysis method was Inverse variance weighted. Cochran’s Q test, MR Egger, and MR-Pleiotropy RESidual Sum and Outlier (MR-PRESSO) were used to assess the stability of the results. Results After FDR correction, the CD8 on HLA DR + CD8br (OR = 0.95, 95% CI = 0.93–0.98, p-value = 7.20 × 10− 4, q-value = 0.088) was determined to be distinctly associated with KSD, and we also found other 25 suggestive associations between immune cells and KSD, of which 13 associations were suggested as protective factors and 12 associations were suggested as risk factors. There was no horizontal pleiotropy or significant heterogeneity in our MR analysis, as determined by the p-value results of our Cochrane Q-test, MR Egger’s intercept test, and MR-PRESSO, which were all > 0.05. Conclusions Our study has explored the potential causal connection between immune cells and KSD by Mendelian randomization analysis, thus providing some insights for future clinical studies.

ObjectiveCollege students are currently grappling with severe mental health challenges, and research on artificial intelligence (AI) related to college students mental health, as a crucial catalyst for promoting psychological well-being, is rapidly advancing. Employing bibliometric methods, this study aim to analyze and discuss the research on AI in college student mental health.MethodsPublications pertaining to AI and college student mental health were retrieved from the Web of Science core database. The distribution of publications were analyzed to gage the predominant productivity. Data on countries, authors, journal, and keywords were analyzed using VOSViewer, exploring collaboration patterns, disciplinary composition, research hotspots and trends.ResultsSpanning 2003 to 2023, the study encompassed 1722 publications, revealing notable insights: (1) a gradual rise in annual publications, reaching its zenith in 2022; (2) Journal of Affective Disorders and Psychiatry Research emerged were the most productive and influential sources in this field, with significant contributions from China, the United States, and their affiliated higher education institutions; (3) the primary mental health issues were depression and anxiety, with machine learning and AI having the widest range of applications; (4) an imperative for enhanced international and interdisciplinary collaboration; (5) research hotspots exploring factors influencing college student mental health and AI applications.ConclusionThis study provides a succinct yet comprehensive overview of this field, facilitating a nuanced understanding of prospective applications of AI in college student mental health. Professionals can leverage this research to discern the advantages, risks, and potential impacts of AI in this critical field.

ObjectiveWith its high prevalence, depression's pathogenesis remains unclear. Recent attention has turned to the interplay between depression and epigenetic modifications. However, quantitative bibliometric analyses are lacking. This study aims to visually analyze depression epigenetics trends, utilizing bibliometric tools, while comprehensively reviewing its epigenetic mechanisms.MethodsUtilizing the Web of Science core dataset, we collected depression and epigenetics-related studies. Employing VOSViewer software, we visualized data on authors, countries, journals, and keywords. A ranking table highlighted field leaders.ResultsAnalysis encompassed 3,469 depression epigenetics studies published from January 2002 to June 2023. Key findings include: (1) Gradual publication growth, peaking in 2021; (2) The United States and its research institutions leading contributions; (3) Need for enhanced collaborations, spanning international and interdisciplinary efforts; (4) Keyword clustering revealed five main themes—early-life stress, microRNA, genetics, DNA methylation, and histone acetylation—highlighting research hotspots; (5) Limited focus on adolescent depression epigenetics, warranting increased attention.ConclusionTaken together, this study revealed trends and hotspots in depression epigenetics research, underscoring global collaboration, interdisciplinary fusion, and multi-omics data's importance. It discussed in detail the potential of epigenetic mechanisms in depression diagnosis and treatment, advocating increased focus on adolescent research in this field. Insights aid researchers in shaping their investigative paths toward understanding depression's epigenetic mechanisms and antidepressant interventions.

Abstract In this paper, a novel co‐operative mean field type game based task offloading strategy is proposed for multi‐access edge computing networks. The objective is to optimally determine the tasks offloaded to each edge computing node (ECN) in the network so that to balance the overall system performance and individual revenue of each ECN. To do so, the utility function of each ECN is first formulated, which is the payback of his time and energy cost for processing the tasks, tailed with the penalty from the network for not processing the tasks on time. A mean field type game is then formulated, where each ECN can either cooperate or compete with each other to maximize their own utility. With respect to the proposed game, the direct method is applied to find the final solutions. Simulation results reveal that the proposed strategy can well balance the utility of each ECN and the overall efficiency of the system, leading to a better performance compared with its cooperative‐ and competitive‐counterparts.

BackgroundDepression is a serious psychological disorder that college students are experiencing. College students’ depression problems, which can be caused by various factors, have been easily ignored and untreated. In recent years, exercise, as a low-cost and easily accessible method for treating depression, has attracted widespread attention. The purpose of this study is to use bibliometrics to explore the hotspots and trends in the field of exercise therapy of college students in depression from 2002 to 2022.MethodsWe retrieved relevant literature from the Web of Science (WoS), PubMed, and Scopus databases, and generated a ranking table to describe the core productivity in the field. We used VOSViewer software to generate network maps of authors, countries, co-cited journals, and co-occurring keywords to help us better understand the scientific collaboration patterns, potential disciplinary foundations, as well as research hotspots and trends in this field.ResultsFrom 2002 to 2022, a total of 1,397 articles related to exercise therapy of college students in depression were selected. The key findings of this study are as follows: (1) the number of publications has gradually increased, especially after 2019; (2) United States and its affiliated higher education institutions have made significant contributions to the development of this field; (3) there are multiple research groups in this field, but their connections are relatively limited; (4) the field is relatively interdisciplinary, primarily a convergence of behavioral science, public health, and psychology; (5) based on co-occurring keyword analysis, six main themes were summarized: health-promoting factors, body image, negative behaviors, increased stress, depression coping strategies, and diet.ConclusionOur study illustrates the research hotspots and trends for the research of exercise therapy of college students in depression, presents some challenges and new insights, and provides valuable information for further research.