Objective: To explore the association of outdoor activity time and sleep duration with screening myopia in primary school students, so as to provide strategies for myopia prevention. Methods: Through a convenience sampling method, a survey was conducted among 4 248 primary school students aged 7-13 years from three primary schools in Xihu District, Nanchang City, Jiangxi Province from May to July, 2023. The average daily outdoor activity time and sleep duration on both weekdays and weekends were investigated in primary school students by using a self designed questionnaire. Uncorrected visual acuity tests and non cycloplegic autorefraction were measured by professional optometrists. Inter group comparisons were conducted using the Chi square test. Logistic regression model was used to analyze the association of outdoor activity time and sleep duration with screening myopia. Results: The detection rate of screening myopia in primary school students was 33.6%, with the rate in boys (32.0%) lower than that in girls (35.3%), and the difference was statistically significant ( χ 2=5.11, P =0.02). The analysis results of Logistic regression showed that after adjusting for factors such as gender, grade and parental education level, both average daily outdoor activity time ＜2 h on both weekdays and weekends ( OR =1.27, 95% CI =1.11-1.46) and sleep duration <10 h ( OR =1.17, 95% CI =1.01- 1.35 ), as well as their combined effect ( OR =1.57, 95% CI =1.25-1.98), were associated with an increased risk of screening myopia in primary school students(all P <0.05). Subgroup analysis results indicated that compared to boys ( OR =1.46, 95% CI = 1.07 -1.99), girls( OR =1.73, 95% CI =1.22-2.44) with insufficient outdoor activity time and sleep duration had a higher risk of screening myopia(both P <0.05). Conclusions There is a negative correlation of outdoor activity time and sleep duration with screening myopia in primary school students. Outdoor activity time and extending sleep duration should be increased to reduce the risk of myopia in primary school students.

Objective: To determine the association between exposure to entertainment screen content on mobile phones and symptoms of anxiety-depression co-morbidity among college students,so as to provide evidence for mental health interventions. Methods: A baseline survey was conducted from April to May 2019. A total of 1 135 college students were selected from one university each in Shangrao City, Jiangxi Province and Hefei City, Anhui Province using cluster random sampling method. A follow up study was conducted in November 2019, resulting in 1 110 matched valid responses. Self rating questionnaires were used to assess the exposure of entertainment screen content. The Depression Anxiety Stress Scale-21(DASS-21) and the Patient Health Questionnaire-9 (PHQ-9) were used to evaluate the anxiety symptoms, depressive symptoms, and symptoms of anxiety-depression co-morbidity among college students. A multivariate binary Logistic regression model was constructed following initial intergroup comparisons with Chi-square test to determine the association between baseline exposure to mobile entertainment screen content and the risk of symptoms of anxiety depression co-morbidity at baseline and the 6 month follow up. Results: The prevalence rates of symptoms of anxiety-depression co-morbidity among college students were 25.4% and 20.6% at baseline and follow up, respectively.After adjusting for confounding factors such as gender, self rated family economic status and self rated health status, the results of multivariate binary Logistic regression analysis showed that compared with the appropriate exposure level group, the exposure of entertainment screen content on mobile phones at baseline, including frequent exposure to reading( OR =1.65,95% CI =1.14-2.39), occasional exposure to other entertainment screen content ( OR =1.46,95% CI =1.01-2.10)and frequent exposure to other entertainment screen content( OR =1.76,95% CI =1.20-2.60), increased the co-occurrence risk of symptoms of anxiety-depression co-morbidity among college students during the follow up period (all P <0.05). Conclusion Occasional or frequert exposure to mobile entertainment screen content can increase the risk of symptoms of anxiety depression co-morbidity among college students.

Myelodysplastic syndrome (MDS) is a chronic hematologic disorder characterized by ineffective hematopoiesis, dysplasia of one or more cell lines with or without definite genetic changes. Its diagnosis requires a comprehensive analysis combining morphology, immunology, cytogenetics, and molecular biology findings. In recent years, the development of second-generation sequencing (NGS) has provided great assistance in exploring the molecular pathogenesis of hematological malignancies and guidance for clinical practice. Mutations of a series of gene involved in RNA splicing, DNA methylation, transcriptional regulation, signal transduction, chromatin modification and cohesin complex have been identified as important mechanisms for the development of MDS, among which some mutations have been found to play important roles in the diagnosis, treatment, and prognosis of MDS. This article has provided a comprehensive review the the common molecular genetic abnormalities involved in MDS.
Humans ; Myelodysplastic Syndromes/diagnosis* ; Mutation ; DNA Methylation ; RNA Splicing ; High-Throughput Nucleotide Sequencing

ObjectiveTo compare the clinical efficacy of traditional Chinese medicine （TCM） manipulative reduction combined with small splint fixation versus surgical treatment for type A distal radius fracture （DRF） and to explore the factors influencing the choice of treatment. MethodsA multi-center retrospective study was conducted， collecting data from 1237 type A DRF patients treated in 11 hospitals in Jiangsu province from September， 2023 to April， 2025. Among them， 851 patients in the TCM group received manipulative reduction combined with small splint fixation， and 386 patients in the surgical group underwent open reduction and internal fixation. Visual analog scale （VAS） scores for pain and radiographic indicators including palmar tilt， ulnar deviation， and radial height were compared before treatment， 5-7 days after treatment， and 4-6 weeks after treatment. The wrist joint function scores including Dienst and Gartland-Werley scores at 12 weeks after treatment were recorded. Subgroup analysis was conducted for the excellent rate of Dienst and Gartland-Werley scores， stratified by age （＜50， 50-59， 60-69， ≥70 years old） and AO subtypes （A1， A2， A3）. A multivariate logistic regression model was used to identify independent factors influencing treatment choice. ResultsOn 5-7 days after treatment， the surgical group had lower VAS scores than the TCM group， while 4-6 weeks after treatment， the TCM group showed lower VAS scores than the surgical group （P＜0.01）. In terms of radiographic indicators， except for the palmar tilt before treatment being higher in the surgical group than in the TCM group （P＜0.01）， there were no significant differences in palmar tilt， ulnar deviation， and radial height at other timepoints （P＞0.05）. Twelve weeks after treatment， the surgical group had a higher average Gartland-Werley score and the excellent rate than the TCM group （P＜0.01）. Subgroup analysis showed that in patients with A2 type DRF aged 50-59 and 60-69 years old， the excellent rates of Dienst and Gartland-Werley scores in the TCM group were higher than those in the surgical group （P＜0.05）. Multivariate logistic regression analysis revealed that age， palmar tilt， ulnar deviation， and the degree of swelling on the affected side were independent factors influencing the choice of treatment （P＜0.05）. ConclusionBoth TCM manipulative reduction combined with small splint fixation and surgical treatment for type A DRF can achieve good therapeutic effects. TCM manipulative reduction combined with small splint fixation has certain advantages in medium- and long-term pain relief， especially in elderly patients， where wrist joint function recovery is more stable. Age， palmar tilt， ulnar deviation， and swelling degree are the main factors influencing the treatment choice.

The initiation of adaptive immune responses relies on the precise recognition and interpretation of antigenic information. In this process, the specific binding of T cell receptors (TCRs) to peptide-major histocompatibility complex (pMHC) molecules represents one of the key molecular events in the initiation of adaptive immune responses. Accordingly, the structural features of TCR-pMHC complexes provide a fundamental basis for dissecting antigen recognition mechanisms and support rational vaccine design, therapeutic target discovery in TCR-based immunotherapy, and TCR identification and optimization. However, experimental determination of TCR-pMHC structures remains costly, time-consuming, and limited in coverage, making computational approaches essential for rapidly obtaining reliable structural information. Computational methods for predicting the structures of TCR-pMHC complexes have advanced rapidly in recent years, driven by progress in deep learning-based modeling frameworks and the increasing availability of structural and sequence resources. Despite these developments, most existing tools do not adequately distinguish the key structural and biophysical differences between MHC class I (MHC-I) and MHC class II (MHC-II) complexes during model construction. As a consequence, their predictive performance differs substantially between class I and class II complexes. In general, structural predictions for class I complexes outperform those for class II complexes. This discrepancy may be related to several fundamental differences between the two systems, including the architecture of the peptide-binding groove, the distribution of peptide lengths, and the properties of peptide flanking residues (PFRs). Compared with MHC-I molecules, MHC-II molecules usually bind longer antigenic peptides, which typically range from 13 to 25 amino acids in length. PFRs at both termini of these peptides participate in regulating the overall conformation of TCR-pMHC class II complexes and exert a pronounced effect on the geometric and physicochemical characteristics of the TCR-pMHC binding interface. Furthermore, within the TCR recognition interface, the complementarity-determining regions (CDRs) consist of segments that differ markedly in conformational behavior. They commonly include regions that are relatively rigid and structurally stable, together with highly flexible segments exhibiting substantial conformational plasticity. These rigidity-flexibility features constitute an essential structural basis enabling TCRs to recognize diverse peptide-MHC ligands and to accommodate conformational heterogeneity at the interface. However, many current modeling tools, in an effort to enforce global conformational stability or reduce structural noise, tend to over-constrain intrinsically flexible regions. Such oversimplification may lead to inappropriate rigidification of flexible CDR loops, resulting in local structural distortions, compromised interface geometry, or even complete modeling failure for specific complexes. Against this background, the review approaches the field from the perspective of computational differences between MHC-I and MHC-II complexes. We first systematically organize and summarize available resources related to TCRs and pMHCs, including structural datasets, sequence databases, prediction tools, and benchmarking studies. We then focus on five representative tools capable of predicting both class I and class II complexes—AlphaFold2, AlphaFold3, TCRmodel2, tFold-TCR, and TCR-pHLA_ModellerS. After excluding structures present in the training sets of these tools, we constructed a benchmark dataset comprising 25 class I and 10 class II TCR-pMHC complexes in the bound state and conducted a systematic evaluation using this dataset. We first employ widely used general evaluation metrics, including All-Atom Root Mean Square Deviation (All-Atom RMSD), Backbone RMSD, Template Modeling score (TM-score), and DockQ, to assess the global conformational accuracy and interface modeling quality of class I and class II complexes. For class II complexes, we propose for the first time a peptide flanking residue deviation index, including the PFRs-Deviation Index (PFRs-DI), N-PFR-Deviation Index (N-PFR-DI), and C-PFR-Deviation Index (C-PFR-DI), to quantitatively characterize conformational deviations in PFRs. In addition, we propose the CDR conformational consistency index (CCC) designed to qualitatively evaluate the ability of prediction tools to capture TCR CDR conformational flexibility. These metrics collectively assess a tool’s ability to model both overall conformation and critical functional regions, thereby addressing the limitations of existing evaluation criteria that overemphasize global structure while inadequately capturing modeling quality in key functional areas. This establishes a unified analytical framework for MHC-I and MHC-II complexes to guide data resource selection, modeling strategy formulation, and evaluation system development. The framework further advances computational modeling and provides crucial support for multi-scale analysis of TCR-pMHC recognition mechanisms and their biological functions.

The initiation of adaptive immune responses relies on the precise recognition and interpretation of antigenic information. In this process, the specific binding of T cell receptors (TCRs) to peptide-major histocompatibility complex (pMHC) molecules represents one of the key molecular events in the initiation of adaptive immune responses. Accordingly, the structural features of TCR-pMHC complexes provide a fundamental basis for dissecting antigen recognition mechanisms and support rational vaccine design, therapeutic target discovery in TCR-based immunotherapy, and TCR identification and optimization. However, experimental determination of TCR-pMHC structures remains costly, time-consuming, and limited in coverage, making computational approaches essential for rapidly obtaining reliable structural information. Computational methods for predicting the structures of TCR-pMHC complexes have advanced rapidly in recent years, driven by progress in deep learning-based modeling frameworks and the increasing availability of structural and sequence resources. Despite these developments, most existing tools do not adequately distinguish the key structural and biophysical differences between MHC class I (MHC-I) and MHC class II (MHC-II) complexes during model construction. As a consequence, their predictive performance differs substantially between class I and class II complexes. In general, structural predictions for class I complexes outperform those for class II complexes. This discrepancy may be related to several fundamental differences between the two systems, including the architecture of the peptide-binding groove, the distribution of peptide lengths, and the properties of peptide flanking residues (PFRs). Compared with MHC-I molecules, MHC-II molecules usually bind longer antigenic peptides, which typically range from 13 to 25 amino acids in length. PFRs at both termini of these peptides participate in regulating the overall conformation of TCR-pMHC class II complexes and exert a pronounced effect on the geometric and physicochemical characteristics of the TCR-pMHC binding interface. Furthermore, within the TCR recognition interface, the complementarity-determining regions (CDRs) consist of segments that differ markedly in conformational behavior. They commonly include regions that are relatively rigid and structurally stable, together with highly flexible segments exhibiting substantial conformational plasticity. These rigidity-flexibility features constitute an essential structural basis enabling TCRs to recognize diverse peptide-MHC ligands and to accommodate conformational heterogeneity at the interface. However, many current modeling tools, in an effort to enforce global conformational stability or reduce structural noise, tend to over-constrain intrinsically flexible regions. Such oversimplification may lead to inappropriate rigidification of flexible CDR loops, resulting in local structural distortions, compromised interface geometry, or even complete modeling failure for specific complexes. Against this background, the review approaches the field from the perspective of computational differences between MHC-I and MHC-II complexes. We first systematically organize and summarize available resources related to TCRs and pMHCs, including structural datasets, sequence databases, prediction tools, and benchmarking studies. We then focus on five representative tools capable of predicting both class I and class II complexes—AlphaFold2, AlphaFold3, TCRmodel2, tFold-TCR, and TCR-pHLA_ModellerS. After excluding structures present in the training sets of these tools, we constructed a benchmark dataset comprising 25 class I and 10 class II TCR-pMHC complexes in the bound state and conducted a systematic evaluation using this dataset. We first employ widely used general evaluation metrics, including All-Atom Root Mean Square Deviation (All-Atom RMSD), Backbone RMSD, Template Modeling score (TM-score), and DockQ, to assess the global conformational accuracy and interface modeling quality of class I and class II complexes. For class II complexes, we propose for the first time a peptide flanking residue deviation index, including the PFRs-Deviation Index (PFRs-DI), N-PFR-Deviation Index (N-PFR-DI), and C-PFR-Deviation Index (C-PFR-DI), to quantitatively characterize conformational deviations in PFRs. In addition, we propose the CDR conformational consistency index (CCC) designed to qualitatively evaluate the ability of prediction tools to capture TCR CDR conformational flexibility. These metrics collectively assess a tool’s ability to model both overall conformation and critical functional regions, thereby addressing the limitations of existing evaluation criteria that overemphasize global structure while inadequately capturing modeling quality in key functional areas. This establishes a unified analytical framework for MHC-I and MHC-II complexes to guide data resource selection, modeling strategy formulation, and evaluation system development. The framework further advances computational modeling and provides crucial support for multi-scale analysis of TCR-pMHC recognition mechanisms and their biological functions.

Background: The tight junction protein claudin18.2 (CLDN18.2) has been implicated in poor prognosis and suboptimal immunotherapy response in gastric cancer (GC). This study investigates the clinicopathological relevance of CLDN18.2 expression and its association with molecular subtypes in GC patients from a high-incidence region, combining transcriptomic and proteomic approaches to explore how CLDN18.2 contributes to progression and metastasis. Methods: A retrospective cohort of 494 GC patients (2019–2024) underwent immunohistochemical analysis for CLDN18.2, Epstein-Barr virus (Epstein–Barr virus–encoded RNA), p53, human epidermal growth factor receptor 2 (HER2), and mismatch repair proteins (MLH1, MSH2, PMS2, and MSH6). CLDN18.2 positivity was defined as moderate to strong (2+/3+) membranous staining in ≥75% of tumor cells. Clinicopathological correlations, biomarker associations, and survival outcomes were evaluated. Transcriptomic and proteomic sequencing was performed to explore molecular mechanisms. Results: CLDN18.2 positivity was observed in 26.9% (133/494) of gastric adenocarcinomas. CLDN18.2-positive tumors correlated with TNM stage (p = .003) and shorter overall survival (p = .018). No associations were identified with age, sex, HER2 status, microsatellite instability, or Epstein-Barr virus infection. Transcriptomic profiling revealed CLDN18.2-high tumors enriched in pathways involving cell junction disruption, signaling regulation, and immune modulation. Proteomic profiling showed that tumors with high CLDN18.2 were enriched in multiple mechanism-related pathways such as integrated metabolic reprogramming, cytoskeletal recombination, immune microenvironment dysregulation, and pro-survival signaling. These mechanisms may collectively contribute to tumor progression and metastasis. Conclusions CLDN18.2 overexpression is associated with poor prognosis in GC patients. Transcriptomic and proteomic analyses demonstrate that CLDN18.2 promotes tumor progression and metastasis, underscoring its potential as an independent prognostic factor in regions with a high incidence of GC.

Objectives: . This study aimed to explore the role of the Pcdh15 gene in hearing maintenance and to examine the effects of its deficiency on cochlear structure, cochlear function, and hearing loss in mice. Methods: . CRISPR/Cas9 technology was used to generate Pcdh15 knockout (KO) mice. Auditory function was evaluated using hearing tests, while histological approaches were employed to assess morphological changes in cochlear hair cells and spiral ganglion neurons (SGNs). RNA sequencing (RNA-seq) was performed on cochlear tissue from postnatal day 18 (P18) wild-type (WT) and Pcdh15 KO mice to identify differentially expressed genes (DEGs). These DEGs were subjected to functional annotation and pathway analysis, with particular emphasis on oxidative phosphorylation (OXPHOS). Reactive oxygen species (ROS) levels were quantified using flow cytometry and DCFH-DA assays. Results: . Pcdh15 KO mice exhibited progressive sensorineural hearing loss, which worsened to profound deafness by P18. Notably, cochlear hair cells and SGNs showed substantial loss and pronounced morphological abnormalities, particularly within the basal high-frequency region. RNA-seq analysis identified 1,359 DEGs, including 1,024 downregulated and 335 upregulated genes. Pathway analysis indicated that Pcdh15 deficiency was associated with inhibition of the OXPHOS pathway, potentially disrupting mitochondrial respiratory chain complexes I, III, IV, and V and thereby impairing energy production and ATP metabolism. In addition, early-stage cochleae from KO mice demonstrated elevated ROS levels and increased oxidative stress, suggesting that redox imbalance may contribute to hearing damage. Conclusion . Pcdh15 plays a critical role in hearing maintenance. Its deficiency is associated with severe hearing loss and marked cochlear abnormalities, which are linked to inhibition of OXPHOS, disruption of energy metabolism, and exacerbation of oxidative stress. Together, these findings provide new insights into the mechanisms underlying hearing loss and suggest potential targets for therapeutic intervention.

Objective To develop albumin-binding indocyanine green (ICG) and assess its potential for near-infrared fluorescence imaging and intraoperative navigation in lung cancer. Methods ABD-tri was recombinantly expressed by genetic engineering. Its albumin-binding capability was determined using size-exclusion chromatography, and its albumin-dependent binding to lung cancer cells was evaluated by flow cytometry. ICG was conjugated to ABD-tri to generate the fluorescent probe ABD-tri-ICG. The potential of ABD-tri-ICG for near-infrared fluorescence imaging and imaging-guided tumor resection was evaluated in mice bearing subcutaneous tumor grafts of lung cancer. Results ABD-tri was highly expressed in Escherichia coli (E. coli) and was purified to homogeneity via a simple affinity chromatography. ABD-tri bound both human and murine serum albumin, contributing to its binding to lung cancer cells. ICG was effectively conjugated to ABD-tri to produce the fluorescence probe ABD-tri-ICG after mixing and incubation at room temperature for 1 h. In mice bearing lung cancer tumor grafts, intravenously injected ABD-tri-ICG enabled clear visualization of tumors with diameters ranging from 5 to 7 mm within 0.5-24 h post-injection. The tumor grafts were resected under the guidance of ABD-tri-ICG-mediated near-infrared fluorescence imaging. Conclusion Intravenous injection of ABD-tri-ICG allows rapid and sustained visualization of lung cancer tumor grafts and enables intraoperative navigation in mice, warranting further evaluation on the clinical translation of ABD-tri-ICG.

Abstract To clarify the service targets, service contents, and safeguard mechanisms of the basic public service program for student vision health management, the study grounded in the current status of child and adolescent myopia prevention and control in China and integrated with the practical experience and innovative practices of Wuhan National Demonstration Area for Student Vision Health Management, takes education as the main thread and schools as the base, consolidating resources from multiple stakeholders including education administrative departments, schools, families, and professional technical service institutions. Centering on four core components—vision health education, monitoring and record keeping, risk prevention and control, and dynamic management, the study constructs a multi tiered basic public service system for vision health management of primary and secondary school students. It further proposes the use of an intelligent management platform and a "three tier color coded early warning" mechanism (red, yellow, blue) to prevent and control student myopia at the source, providing schools with replicable and operable implementation pathways to enhance the level of student vision health management.