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.

ObjectiveTo observe the effect of neural mobilization based on shoulder control training on shoulder pain and upper limb function in stroke patients with hemiplegia. MethodsFrom January, 2020 to November, 2021, 43 patients with hemiplegia after stroke in the Second People's Hospital of Nantong were randomly divided into control group (n = 21) and treatment group (n = 22). The control group received shoulder control training, while the treatment group received neural mobilization in addition. Before and after four weeks of treatment, they were evaluated with the Numeric Rating Scale (NRS) of pain and Fugl-Meyer Assessment-Upper Extremities (FMA-UE). ResultsOne case dropped off in the control group and two cases dropped off in the treatment group. After treatment, the NRS score and FMA-UE score improved in both groups (|t| >7.898, P < 0.001), and they were better in the treatment group than in the control group (|t| >2.337, P < 0.05). ConclusionNeural mobilization based on shoulder control training can significantly alleviate shoulder pain and improve upper limb motor function in stroke patients with hemiplegia.