Purpose: The genomic characteristics of uterine sarcomas have not been fully elucidated. This study aimed to explore the genomic landscape of the uterine sarcomas (USs). Materials and Methods: Comprehensive genomic analysis through RNA-sequencing was conducted. Gene fusion, differentially expressed genes (DEGs), signaling pathway enrichment, immune cell infiltration, and prognosis were analyzed. A deep learning model was constructed to predict the survival of US patients. Results: A total of 71 US samples were examined, including 47 endometrial stromal sarcomas (ESS), 18 uterine leiomyosarcomas (uLMS), three adenosarcomas, two carcinosarcomas, and one uterine tumor resembling an ovarian sex-cord tumor. ESS (including high-grade ESS [HGESS] and low-grade ESS [LGESS]) and uLMS showed distinct gene fusion signatures; a novel gene fusion site, MRPS18A–PDC-AS1 could be a potential diagnostic marker for the pathology differential diagnosis of uLMS and ESS; 797 and 477 uterine sarcoma DEGs (uDEGs) were identified in the ESS vs. uLMS and HGESS vs. LGESS groups, respectively. The uDEGs were enriched in multiple pathways. Fifteen genes including LAMB4 were confirmed with prognostic value in USs; immune infiltration analysis revealed the prognositic value of myeloid dendritic cells, plasmacytoid dendritic cells, natural killer cells, macrophage M1, monocytes and hematopoietic stem cells in USs; the deep learning model named Max-Mean Non-Local multi-instance learning (MMN-MIL) showed satisfactory performance in predicting the survival of US patients, with the area under the receiver operating curve curve reached 0.909 and accuracy achieved 0.804. Conclusion USs harbored distinct gene fusion characteristics and gene expression features between HGESS, LGESS, and uLMS. The MMN-MIL model could effectively predict the survival of US patients.

Purpose: The genomic characteristics of uterine sarcomas have not been fully elucidated. This study aimed to explore the genomic landscape of the uterine sarcomas (USs). Materials and Methods: Comprehensive genomic analysis through RNA-sequencing was conducted. Gene fusion, differentially expressed genes (DEGs), signaling pathway enrichment, immune cell infiltration, and prognosis were analyzed. A deep learning model was constructed to predict the survival of US patients. Results: A total of 71 US samples were examined, including 47 endometrial stromal sarcomas (ESS), 18 uterine leiomyosarcomas (uLMS), three adenosarcomas, two carcinosarcomas, and one uterine tumor resembling an ovarian sex-cord tumor. ESS (including high-grade ESS [HGESS] and low-grade ESS [LGESS]) and uLMS showed distinct gene fusion signatures; a novel gene fusion site, MRPS18A–PDC-AS1 could be a potential diagnostic marker for the pathology differential diagnosis of uLMS and ESS; 797 and 477 uterine sarcoma DEGs (uDEGs) were identified in the ESS vs. uLMS and HGESS vs. LGESS groups, respectively. The uDEGs were enriched in multiple pathways. Fifteen genes including LAMB4 were confirmed with prognostic value in USs; immune infiltration analysis revealed the prognositic value of myeloid dendritic cells, plasmacytoid dendritic cells, natural killer cells, macrophage M1, monocytes and hematopoietic stem cells in USs; the deep learning model named Max-Mean Non-Local multi-instance learning (MMN-MIL) showed satisfactory performance in predicting the survival of US patients, with the area under the receiver operating curve curve reached 0.909 and accuracy achieved 0.804. Conclusion USs harbored distinct gene fusion characteristics and gene expression features between HGESS, LGESS, and uLMS. The MMN-MIL model could effectively predict the survival of US patients.

Purpose: The genomic characteristics of uterine sarcomas have not been fully elucidated. This study aimed to explore the genomic landscape of the uterine sarcomas (USs). Materials and Methods: Comprehensive genomic analysis through RNA-sequencing was conducted. Gene fusion, differentially expressed genes (DEGs), signaling pathway enrichment, immune cell infiltration, and prognosis were analyzed. A deep learning model was constructed to predict the survival of US patients. Results: A total of 71 US samples were examined, including 47 endometrial stromal sarcomas (ESS), 18 uterine leiomyosarcomas (uLMS), three adenosarcomas, two carcinosarcomas, and one uterine tumor resembling an ovarian sex-cord tumor. ESS (including high-grade ESS [HGESS] and low-grade ESS [LGESS]) and uLMS showed distinct gene fusion signatures; a novel gene fusion site, MRPS18A–PDC-AS1 could be a potential diagnostic marker for the pathology differential diagnosis of uLMS and ESS; 797 and 477 uterine sarcoma DEGs (uDEGs) were identified in the ESS vs. uLMS and HGESS vs. LGESS groups, respectively. The uDEGs were enriched in multiple pathways. Fifteen genes including LAMB4 were confirmed with prognostic value in USs; immune infiltration analysis revealed the prognositic value of myeloid dendritic cells, plasmacytoid dendritic cells, natural killer cells, macrophage M1, monocytes and hematopoietic stem cells in USs; the deep learning model named Max-Mean Non-Local multi-instance learning (MMN-MIL) showed satisfactory performance in predicting the survival of US patients, with the area under the receiver operating curve curve reached 0.909 and accuracy achieved 0.804. Conclusion USs harbored distinct gene fusion characteristics and gene expression features between HGESS, LGESS, and uLMS. The MMN-MIL model could effectively predict the survival of US patients.

Objective: To examine the relationship among physical education core literacy, exercise self efficacy, physical self esteem and subjective exercise experience among middle school students, and to analyze the latent classes of exercise self efficacy, so as to provide evidence for enhancing adolescents subjective exercise experience. Methods: Using stratified cluster random sampling, 2 569 students from 12 provinces, autonomous regions or municipality directly under the central govement (Jiangxi, Guangdong, Hunan, Guizhou, Henan, Guangxi, Yunnan, Chongqing, Sichuan, Shandong, Hubei, Hebei) were surveyed from September to November in 2024 with Core Competency Scale of Physical Education, Subjective Exercise Experiences Scale, Exercise Self Efficacy Scale, and Physical Self esteem Scale. Pearson correlation analysis was conducted to explore the relationships among physical education core literacy, exercise self efficacy, physical self esteem, and subjective exercise experience. Mediation models with Bootstrap testing were employed to examine the mediating roles of exercise self efficacy and physical self esteem in the relationship between physical education core literacy and subjective exercise experience. Latent profile analysis (LPA) of exercise self efficacy was performed using Mplus 8.3. Results: Pearson correlation analysis revealed positive associations between physical education core literacy and exercise self efficacy ( r =0.21), physical self esteem ( r =0.38), and subjective exercise experience ( r =0.40); exercise self efficacy was positively correlated with physical self esteem ( r =0.25) and subjective exercise experience ( r =0.45); and physical self esteem was positively correlated with subjective exercise experience ( r =0.34) (all P <0.01). Mediation analysis indicated that physical education core literacy positively predicted subjective exercise experience ( β =0.41, P <0.05), with exercise self efficacy and physical self esteem serving as partial mediators (effect size=0.14, P <0.01), accounting for 34% of the total effect. LPA identified three latent classes of exercise self efficacy:low (14.71%, n =378), moderate (65.51%, n =1 683), and high (19.78%, n =508) exercise self efficacy groups. Conclusion Adolescents exercise self efficacy demonstrates heterogeneity, and both exercise self efficacy and physical self esteem mediate the relationship between physical education core literacy and subjective exercise experience.

Objective: To understand the dynamic temporal evolution pathways of Internet addiction among university students and to identify the core driving nodes, so as to provide theoretical evidences for the precise implementation of targeted interventions. Methods: Using a convenient cluster sampling method, a total of 1 066 full time freshmen and sophomores were recruited from three universities in Guizhou, Jiangxi, and Guangdong Provinces for a follow up survey (T1:January-March 2024; T2:January-March 2025). The Revised Chen Internet Addiction Scale (CIAS-R) was employed to assess the status of Internet addiction among university students, and cross sectional as well as cross lagged panel network models were constructed to analyze Internet addiction and its multidimensional influencing factors. Results: The T1 network comprised 19 nodes and 114 non zero edges, while the T2 network comprised 19 nodes and 126 non zero edges. Cross sectional network analysis revealed the strongest association between "insufficient sleep" and "daytime fatigue"; the core nodes were "first thought upon waking for going online" and "feeling low after disconnection" (characteristics of psychological dependence) at T1, while the core nodes shifted to "impaired health" and "excitement when online" (characteristics of functional impairment and addictive psychodynamic features) at T2. Cross lagged network analysis further indicated that "reduced leisure" directly predicted "sleep compression", and a bidirectional relationship was observed between "needing more time to achieve satisfaction" and "academic decline". Conclusions Internet addiction among university students exhibits dynamic evolutionary characteristics. Stage specific targeted interventions focusing on core driving nodes are needed, integrating behavioral regulation and academic support to break the vicious cycle and enhancing the ability to cope with real life demands.

Objective: To explore the longitudinal relationship between regulatory emotional self-efficacy and the persistence of exercise, so as to provide a reference for promoting the development of exercise habits among college students. Methods: Using a cluster random sampling method, one undergraduate institution was selected from each of Jiangxi, Hunan, Guizhou, Yunnan, Guangdong, Anhui, and Fujian provinces. A total of 2 576 college students were recruited. The Regulatory Emotional Self-efficacy Scale and Exercise Persistence Scale were distributed to college students through the questionnaire star platform. Three rounds of questionnaire surveys were completed in September 2023 (T1), December 2023 (T2), and March 2024 (T3). Crosslagged analysis was employed to explore the relationship between regulatory emotional selfefficacy and the persistence of college students exercise. Results: Repeated measures analysis of variance indicated that the main effects of regulatory emotional self-efficacy on time and gender were statistically significant ( F =102.15, η 2=0.07; F =34.80, η 2=0.01), and the interaction effects between time and gender, as well as between time and academic stage, were also statistically significant ( F =3.81, η 2=0.00; F = 25.54 , η 2=0.02)( P ＜ 0.05). The main effect of exercise persistence on time was statistically significant ( F =111.28, η 2=0.05) and the interaction effect between time and academic stage was statistically significant ( F =27.13, η 2=0.02)( P <0.01). Cross lagged analysis revealed that regulatory emotional self-efficacy at T1 positively predicted exercise persistence at T2 ( β =0.068), and regulatory emotional self-efficacy at T2 positively predicted exercise persistence at T3 ( β =0.368)( P ＜0.01). Prior exercise persistence positively predicted subsequent exercise persistence, with path coefficients of 0.298 and 0.240 ( P <0.01). Exercise persistence at T1 negatively predicted regulatory emotional self-efficacy at T2 ( β =-0.068), and exercise persistence at T2 positively predicted regulatory emotional self-efficacy at T3 ( β =0.061) ( P <0.01). Prior regulatory emotional self-efficacy positively predicted subsequent regulatory emotional self-efficacy, with path coefficients of 0.271 and 0.639 ( P <0.01). Conclusions There is a longitudinal causal relationship between regulatory emotional self-efficacy and college students exercise persistence. In daily physical activities, the significant role of emotional factors in sports practices should be emphasized to promote exercise behaviors among college students.