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To improve students’ understanding of physical education teaching concepts and help teachers analyze students’ cognitive patterns, the study proposes an association learning-based method for understanding physical education teaching concepts using deep learning algorithms, which extracts image features related to teaching concepts using convolutional neural networks. Moreover, a neurocognitive diagnostic model based on hypergraph convolution is constructed to mine the data of students’ long-term learning sequences and identify students’ cognitive outcomes. The findings revealed that the highest accuracy of the association graph convolutional neural network was 0.84 when the number of training samples was 90,000. In each of the three datasets, the cognitive diagnostic model’s accuracy was 0.76, 0.77, and 0.75, respectively. The use of the association graph convolutional neural network model resulted in an increase of 29% in the mastery of students in the concepts and knowledge of sports. The predictive accuracy of the cognitive schema diagnostic model ranged from 0.6 to 1.0 with a mean value of 0.81. The study reveals that the model proposed in the study has high accuracy and stability in predicting cognitive patterns, which can better identify students’ cognitive states and provide strong support for instructional guidance and personalized learning.

It has been reported that 1,25 dihydroxyvitamin D [1,25(OH)2D] deficiency leads to the loss of mandibular bone, however the mechanism is unclear. We investigated whether the Sirt1/FOXO3a signaling pathway is involved in this process. Using a 1,25(OH)2D deficiency model induced by genetic deletion in mice of 25-hydroxyvitamin D-1α hydroxylase [1α(OH)ase-/- mice]. We first documented a sharp reduction of expression levels of Sirt1 in the 1α(OH)ase-/- mice in vivo. Next, we demonstrated dose-dependent upregulation of Sirt1 by treatment with exogenous 1,25(OH)2D3 in vitro. We then identified a functional VDR binding site in the Sirt1 promoter. By crossing Prx1-Sirt1 transgenic mice with 1α(OH)ase-/- mice we demonstrated that the overexpression of Sirt1 in mesenchymal stem cells (MSCs) greatly improved the 1α(OH)ase-/- mandibular bone loss phenotype by increasing osteoblastic bone formation and reducing osteoclastic bone resorption. In mechanistic studies, we showed, in 1α(OH)ase-/- mice, decreases of Sirt1 and FoxO3a, an increase in oxidative stress as reflected by a reduction of the antioxidant enzymes peroxiredoxin1 (Prdx1), SOD1 and SOD2 expression, and an increase of markers for osteocyte senescence and senescence associated secretory phenotypes (SASP), including β-galactosidase (β-gal), p16, p53 and p21. The targeted overexpression of Sirt1 in the 1α(OH)ase-/- mice restored the expression levels of these molecules. Finally, we demonstrated that a Sirt1 agonist can upregulate FOXO3a activity by increasing deacetylation and nuclear translocation. Overall, results from this study support the concept that targeted increases in Sirt1/FOXO3a signaling levels can greatly improve the bone loss caused by 1,25(OH)2D deficiency.

Objective This study aims to analyse the content of internet medical policies, grasp the inherent laws of the development of internet medical policies and provide references for improving the policy system. Methods On the basis of web crawler technology, 436 internet medical policies issued by the central and local governments after the Healthy China Strategy was proposed were collected. The BERTopic model was used to extract topics, and a comprehensive analysis of China’s internet medical policy texts was conducted through the analysis of topic content, topic hierarchy and topic clusters. Results A total of 27 topics were identified in China’s internet medical policies, mainly focussing on five topic clusters: medical service regulation, elderly nursing and children’s healthcare using traditional Chinese medicine, user safety guarantees, health education and communication and infectious disease treatment and recovery guidance. Conclusions Different topic contents play a role in promoting the comprehensive and standardized development of internet medical services. However, there is still room for further improvement in policy integrity, consideration of multiple types of users, and implementation effectiveness. Continuous efforts are needed to optimize the effectiveness of policies.

Skin wound healing is a complex process which involves multiple molecular events and the underlying mechanism is not fully understood. We presented a comparative transcriptomic analysis of skin wound healing in humans and mice to identify shared molecular mechanisms across species. We analyzed transcriptomes from three distinct stages of the healing process and constructed protein-protein interaction networks to elucidate commonalities in the healing process. A substantial number of differentially expressed genes (DEGs) were identified in human transcriptomes, particularly upregulated genes before and after wound injury, and enriched in processes related to extracellular matrix organization and leukocyte migration. Similarly, the mouse transcriptome revealed thousands of DEGs, with shared biological processes and enriched KEGG pathways, highlighting a conserved molecular signature in skin wound healing. A total of 21 common DEGs were found across human comparisons, and 591 in mouse comparisons, with four genes (KRT2, MARCKSL1, MMP1, and TNC) consistently differentially expressed in both species, suggesting critical roles in mammalian skin wound healing. The expression trends of these genes were consistent, indicating their potential as therapeutic targets. The molecular network analysis identified five subnetworks associated with collagen synthesis, immunity, cell-cell adhesion, and extracellular matrix, with hub genes such as COL4A1, TLR7, TJP3, MMP13, and HIF1A exhibited significant expression changes before and after wound injury in humans and mice. In conclusion, our study provided a detailed molecular network for understanding the healing process in humans and mice, revealing conserved mechanisms that could help the development of targeted therapies across species.

Background Distal radius fractures (DRFs) have become a public health problem for all countries, bringing a heavier economic burden of disease globally, with China’s disease economic burden being even more acute due to the trend of an aging population. This study aimed to explore the influencing factors of hospitalization cost of patients with DRFs in traditional Chinese medicine (TCM a ) hospitals to provide a scientific basis for controlling hospitalization cost. Methods With 1306 cases of DRFs patients hospitalized in 15 public TCM a hospitals in two cities of Gansu Province in China from January 2017 to 2022 as the study object, the influencing factors of hospitalization cost were studied in depth gradually through univariate analysis, multiple linear regression, and path model. Results Hospitalization cost of patients with DRFs is mainly affected by the length of stay, surgery and operation, hospital levels, payment methods of medical insurance, use of TCM a preparations, complications and comorbidities, and clinical pathways. The length of stay is the most critical factor influencing the hospitalization cost, and the longer the length of stay, the higher the hospitalization cost. Conclusions TCM a hospitals should actively take advantage of TCM b diagnostic modalities and therapeutic methods to ensure the efficacy of treatment and effectively reduce the length of stay at the same time, to lower hospitalization cost. It is also necessary to further deepen the reform of the medical insurance payment methods and strengthen the construction of the hierarchical diagnosis and treatment system, to make the patients receive reasonable reimbursement for medical expenses, thus effectively alleviating the economic burden of the disease in the patients with DRFs.

Simplification of 3D building models is an important way to improve rendering efficiency. When existing algorithms are directly applied to simplify multi-component models, generally composed of independent components with strong topological dependence, each component is simplified independently. The consequent destruction of topological dependence can cause unreasonable separation of components and even result in inconsistent conclusions of spatial analysis among different levels of details (LODs). To solve these problems, a novel simplification method, which considers the topological dependence among components as constraints, is proposed. The vertices of building models are divided into boundary vertices, hole vertices, and other ordinary vertices. For the boundary vertex, the angle between the edge and component (E–C angle), denoting the degree of component separation, is introduced to derive an error metric to limit the collapse of the edge located at adjacent areas of neighboring components. An improvement to the quadratic error metric (QEM) algorithm was developed for the hole vertex to address the unexpected error caused by the QEM’s defect. A series of experiments confirmed that the proposed method could effectively maintain the overall appearance features of building models. Compared with the traditional method, the consistency of visibility analysis among different LODs is much better.

Schizophrenia (SCZ), bipolar disorder (BD), and major depressive disorder (MDD) share common genetic and environmental risk factors, with immune-inflammatory dysregulation playing a crucial role in their pathophysiology. However, further research is needed to clarify causal relationships. We conducted a Mendelian randomization (MR) study using summary statistics from large-scale genome-wide association studies (GWAS) of European ancestry as instrumental variables to assess the association between 95 circulating inflammatory proteins and the risk of SCZ, BD, and MDD. Initially, a bidirectional two-sample MR analysis was performed to clarify the direction of causality. Subsequently, multivariable MR analysis was employed to investigate whether the effects of different circulating proteins on psychiatric disorders exhibit complex overlap or interrelated effects. Finally, colocalization analysis was applied to determine whether circulating proteins share causal genetic variants with psychiatric disorders. To enhance the robustness of our findings, a series of sensitivity analyses were conducted, including tests for horizontal pleiotropy, heterogeneity, and rigorous quality control procedures. Discovery analyses were based on data from the Psychiatric Genomics Consortium (PGC), while validation was performed using data from the FinnGen biobank. Meta-analysis was used to integrate results from multiple data sources. Evidence strength was evaluated based on consistency across MR, replication, and colocalization. Forward univariable MR and meta-analysis revealed that elevated circulating C-reative protein (CRP) (OR = 0.93, P meta  = 0.013) and fractalkine(CX3CL1) (OR = 0.92, P IVW  = 0.040) significantly reduced the risk of SCZ, whereas Delta and Notch-like epidermal growth factor-related receptor (DNER) (OR = 1.07, P meta  = 0.007) and eukaryotic translation initiation factor 4E-binding protein 1 (EIF4EBP1)(OR = 1.09, P IVW  = 0.015) was associated with an increased risk of SCZ. For MDD, fibroblast growth factor 23 (FGF23) (OR = 0.97, P meta  = 0.015) and interleukin-20 (IL20) (OR = 0.96, P IVW  = 0.028) significantly reduced the risk, whereas tumor necrosis factor ligand superfamily member 12 (TNFSF12) was associated with an increased risk of MDD (OR = 1.02, P meta  = 0.039). In BD, elevated circulating levels of C-X-C motif chemokine 5 (CXCL5) (OR = 0.95, P IVW  = 0.024), TRANCE (OR = 0.95, P meta  = 0.016), and CC motif chemokine ligand 7 (CCL7) (OR = 0.94, P meta  = 0.023) were significantly associated with reduced risk, suggesting potential protective effects. In multivariable MR analysis, after adjusting for other inflammatory protein levels, CRP showed a significant protective effect on SCZ (OR = 0.91, P IVW  = 0.008), FGF23 demonstrated a significant protective effect on MDD (OR = 0.96, P IVW  = 0.038), and TNFSF12 significantly increased the risk of MDD (OR = 1.03, P IVW  = 0.032). Colocalization analysis provided strong evidence for shared genetic variants between DNER and SCZ (PH4 = 0.89), with the rs35975053 locus being the most significant. This MR study suggests potential causal links between inflammatory proteins and psychiatric disorders. CRP and CX3CL1 were protective for SCZ, while DNER and EIF4EBP1 increased risk. FGF23 and IL20 were protective for MDD, whereas TNFSF12 increased risk. CXCL5, TRANCE, and CCL7 were suggestively protective for BD. The shared causal genetic variants between DNER and SCZ suggest a mechanism underlying neuropsychiatric pathogenesis. Further experimental studies are warranted to elucidate the underlying biological mechanisms and identify novel diagnostic biomarkers and therapeutic targets.

Mercury, particularly in its methylated form, poses a significant environmental and health risk in aquatic ecosystems. While the toxicity and bioaccumulation of mercury are well documented, there remains a critical gap in our understanding of the mechanisms governing mercury methylation and demethylation in aquatic environments. This review systematically examines the complex interplay of chemical, biological, and physical factors that influence mercury speciation and transformation in natural water systems. We provide a comprehensive analysis of methylation and demethylation processes, specifically focusing on the dominant role of methanogenic bacteria. Our study highlights the crucial function of hgcAB genes in facilitating mercury methylation by anaerobic microorganisms, an area that represents a frontier in current research. By synthesizing the existing knowledge and identifying key research priorities, this review offers novel insights into the intricate dynamics of mercury cycling in aquatic ecosystems. Our findings provide a theoretical framework to inform future studies and guide pollution management strategies for mercury and its compounds in aquatic environments.

Salinity stress severely limits rice productivity and grain quality worldwide. Although exogenous foliar application of titanium dioxide nanoparticles (nano-TiO2) has been reported to enhance crop stress tolerance, its regulatory roles in yield formation and grain quality in rice varieties with differing salt tolerance are not well understood. In the present study, two contrasting rice varieties, viz., Jingliangyou 3261 (JLY3261; salt-tolerant) and Yuxiangyouzhan (YXYZ; salt-sensitive), were applied with five nano-TiO2 foliar application treatments—viz., CK: water spray; Ti1: 15 mg L−1; Ti2: 30 mg L−1; Ti3: 45 mg L−1; and Ti4: 60 mg L−1—at the jointing and panicle initiation stages. Plants were irrigated with 0.3% saltwater to simulate salt stress. The results showed that Ti2 and Ti3 treatments led to 8.59% and 14.80% increases in grain yield in JLY3261 and YXYZ, respectively, compared with CK. Ti2 and Ti3 treatments significantly increased the leaf area index, net photosynthetic rate, and aboveground biomass of both varieties at the heading stage. Meanwhile, the activities of antioxidant enzymes such as superoxide dismutase and peroxidase, as well as nitrogen metabolism enzymes including nitrate reductase and glutamine synthetase, were improved with a substantial reduction in malondialdehyde contents. Application of nano-TiO2 upregulated the expression of ion transport-related genes such as OsSOSs, OsNHXs and OsHKTs, thus improving leaf K+ accumulation and reducing Na+ content to optimize the K+/Na+ ratio. In addition, Ti2 and Ti3 treatments improved the milled rice rate, head rice rate, and protein content, while they decreased the chalkiness degree of both rice cultivars. Principal component analysis showed that the aboveground biomass at the heading stage was a core evaluation index for both varieties. Overall, foliar application of 30–45 mg L−1 nano-TiO2 was found to be effective regarding growth and yield improvement in rice under saline conditions. This study provides a theoretical basis for agro-management strategies for rice cultivation in saline–alkaline soils.

Patients with schizophrenia (SCZ) face multiple health challenges due to the complication of chronic diseases and psychiatric disorders. Among these, cardiovascular comorbidities are the leading cause of their life expectancy being 15–20 years shorter than that of the general population. Identifying comorbidity patterns and uncovering differences in immune and metabolic function are crucial steps toward improving prevention and management strategies. A retrospective cross-sectional study was conducted using electronic medical records of inpatients discharged between 2015 and 2024 from a municipal psychiatric hospital in China. The study included patients diagnosed with Schizophrenia, Schizotypal, and Delusional Disorders (SSDs) (ICD-10: F20–F29). Comorbidity patterns were identified through latent class analysis (LCA) based on the 20 most common comorbid conditions among SSD patients. To investigate differences in peripheral blood metabolic and immune function, linear regression or generalized linear models were applied to 44 laboratory test indicators collected during the acute episode. The Benjamini-Hochberg method was used for p-value correction, and the false discovery rate (FDR) was calculated, with statistical significance set at FDR < 0.05. Among 3,697 inpatients with SSDs, four distinct comorbidity clusters were identified: SSDs only (Class 1), High-Risk Metabolic Multisystem Disorders (Class 2, n = 39), Low-Risk Metabolic Multisystem Disorders (Class 3, n = 573), and Sleep Disorders (Class 4, n = 205). Compared to Class 1, Class 2 exhibited significantly elevated levels of apolipoprotein A (ApoA; β = 90.62), apolipoprotein B (ApoB; β = 0.181), mean platelet volume (MPV; β = 0.994), red cell distribution width–coefficient of variation (RDW-CV; β = 1.182), antistreptolysin O (ASO; β = 276.80), and absolute lymphocyte count (ALC; β = 0.306), along with reduced apolipoprotein AI (ApoAI; β = –0.173) and hematocrit (HCT; β = –35.13). Class 3 showed moderate increases in low-density lipoprotein cholesterol (LDL-C; β = 0.113), MPV (β = 0.267), white blood cell count (WBC; β = 0.476), and absolute neutrophil count (ANC; β = 0.272), with decreased HCT (β = –9.81). Class 4 was characterized by elevated aggregate index of systemic inflammation (AISI; β = 81.07), neutrophil-to-lymphocyte ratio (NLR; β = 0.465), and systemic inflammation response index (SIRI; β = 0.346), indicating a heightened inflammatory state. The comorbidity patterns of patients with SCZ can be distinctly classified. During the acute episode, those with comorbid metabolic disorders exhibit a higher risk of cardiovascular diseases and immune system abnormalities, while patients with comorbid sleep disorders present a pronounced systemic inflammatory state and immune dysfunction. This study provides a basis for the chronic disease management and anti-inflammatory treatment, while also offering objective biomarker insights for transdiagnostic research.