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Background: Osteoarthritis, a common degenerative disease of articular cartilage, is characterized by degeneration of articular cartilage, changes in subchondral bone structure, and formation of osteophytes, with main clinical manifestations including increasingly serious swelling, pain, stiffness, deformity, and mobility deficits of the knee joints. With the advent of the big data era, the processing of mass data has evolved into a hot topic and gained a solid foundation from the steadily developed and improved machine learning algorithms. Aiming to provide a reference for the diagnosis and treatment of osteoarthritis, this paper using machine learning identifies the key feature genes of osteoarthritis and explores its relationship with immune infiltration, thereby revealing its pathogenesis at the molecular level. Methods: From the GEO database, GSE55235 and GSE55457 data were derived as training sets and GSE98918 data as a validation set. Differential gene expressions of the training sets were analyzed, and the LASSO regression model and support vector machine model were established by applying machine learning algorithms. Moreover, their intersection genes were regarded as feature genes, the receiver operator characteristic (ROC) curve was drawn, and the results were verified using the validation set. In addition, the expression spectrum of osteoarthritis was analyzed by immunocyte infiltration and the co-expression correlation between feature genes and immunocytes was construed. Conclusion: EPYC and KLF9 can be viewed as feature genes for osteoarthritis. The silencing of EPYC and the overexpression of KLF9 are associated with the occurrence of osteoarthritis and immunocyte infiltration. [ABSTRACT FROM AUTHOR]

Background: The growing popularity and frequency of online game use have resulted in a large number of studies reporting various mental problems associated with game abuse in adolescents. In this article, we examined the prevalence of gaming disorder (GD) and explored the associations of GD with anxiety and insomnia symptoms in minority youth in China. Methods: A total of 1494 students completed the Problematic Online Gaming Questionnaire Short-Form (POGQ-SF), the Generalized Anxiety Disorder 7-item questionnaire (GAD-7), and Athens Insomnia Scale (AIS). Chi-square and binary logistic regression analyses were used to explore the associations between gaming disorder and anxiety/insomnia. Results: A total of 356 (23.83%) respondents reported that they had gaming disorder. Chi-square analysis showed that gender, grade, marital status of parents and exercise situation were significantly associated with GD. Binary logistic regression analysis showed that those who had GD were at significantly higher risk for anxiety and insomnia than those without GD. Conclusion: We found a high incidence of GD and a positive association among anxiety, insomnia and GD. Thus, special attention should be paid to those who have suffered from GD. It is worth addressing the adverse effects of GD on anxiety and insomnia. [ABSTRACT FROM AUTHOR]

Passionfruit (Passiflora edulis) is a significant fruit crop in the commercial sector, owing to its high nutritional and medicinal value. The advent of high-throughput genomics sequencing technology has led to the publication of a vast amount of passionfruit omics data, encompassing complete genome sequences and transcriptome data under diverse stress conditions. To facilitate the efficient integration, storage, and analysis of these large-scale datasets, and to enable researchers to effectively utilize these omics data, we developed the first passionfruit genome database (PGD). The PGD platform comprises a diverse range of functional modules, including a genome browser, search function, heatmap, gene expression patterns, various tools, sequence alignment, and batch download, thereby providing a user-friendly interface. Additionally, supplementary practical tools have been developed for the PGD, such as gene family analysis tools, gene ontology (GO) terms, a pathway enrichment analysis, and other data analysis and mining tools, which enhance the data's utilization value. By leveraging the database's robust scalability, the intention is to continue to collect and integrate passionfruit omics data in the PGD, providing comprehensive and in-depth support for passionfruit research. The PGD is freely accessible via http://passionfruit.com.cn. [ABSTRACT FROM AUTHOR]