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.

This study aims to explore whether Naoxintong Capsules improve multiple cerebral infarctions and myocardial injury via promoting angiogenesis, thereby exerting a simultaneous treatment effect on both the brain and heart. Male SD rats were randomly divided into six groups: sham-operated group, model group, high-dose, medium-dose, and low-dose groups of Naoxintong Capsules(440, 220, and 110 mg·kg~(-1)), and nimodipine group(10.8 mg·kg~(-1)). Rat models of multiple cerebral infarctions were established by injecting autologous thrombus, and samples were collected and tested seven days after modeling. Evaluations included multiple cerebral infarction model assessments, neurological function scores, grip strength tests, and rotarod tests, so as to evaluate neuromotor functions. Morphological structures of brain and heart tissue were observed using hematoxylin-eosin(HE) staining, Nissl staining, and Masson staining. Network pharmacology was employed to screen the mechanisms of Naoxintong Capsules in improving multiple cerebral infarctions and myocardial injury. Neuronal and myocardial cell ultrastructures were observed using transmission electron microscopy. Apoptosis rate in brain neuronal cells was detected by TdT-mediated dUTP nick end labeling(TUNEL) staining, and reactive oxygen species(ROS) levels in myocardial cells were measured. Immunofluorescence was used to detect the expression of platelet endothelial cell adhesion molecule-1(CD31), antigen identified by monoclonal antibody Ki67(Ki67), hematopoietic progenitor cell antigen CD34(CD34), and hypoxia inducible factor-1α(HIF-1α) in brain and myocardial tissue. Western blot, and real-time quantitative polymerase chain reaction(RT-qPCR) were used to detect the expression of HIF-1α, vascular endothelial growth factor(VEGF), vascular endothelial growth factor receptor 2(VEGFR2), sarcoma(Src), basic fibroblast growth factor(bFGF), angiopoietin-1(Ang-1), and TEK receptor tyrosine kinase(Tie-2). Compared with the model group, the medium-dose group of Naoxintong Capsules showed significantly lower neurological function scores, increased grip strength, and prolonged time on the rotarod. Pathological damage in brain and heart tissue was reduced, with increased and more orderly arranged mitochondria in neurons and cardiomyocytes. Apoptosis in brain neuronal cells was decreased, and ROS levels in cardiomyocytes were reduced. The microvascular density and endothelial cells of new blood vessels in brain and heart tissue increased, with increased overlapping regions of CD31 and Ki67 expression. The relative protein and mRNA expression levels of HIF-1α, VEGF, VEGFR2, Src, Ang-1, Tie-2, and bFGF were elevated in brain tissue and myocardial tissue. Naoxintong Capsules may improve multiple cerebral infarctions and myocardial injury by mediating HIF-1α/VEGF expression to promote angiogenesis.
Animals ; Male ; Drugs, Chinese Herbal/administration & dosage* ; Rats, Sprague-Dawley ; Rats ; Cerebral Infarction/genetics* ; Hypoxia-Inducible Factor 1, alpha Subunit/genetics* ; Vascular Endothelial Growth Factor A/genetics* ; Capsules ; Signal Transduction/drug effects* ; Humans ; Brain/metabolism* ; Myocardium/metabolism* ; Apoptosis/drug effects*

The holistic view is the key in the study of traditional Chinese medicine(TCM). The component structure theory is based on the holistic view to investigate the correlation between material basis and efficiency, which enriches the holistic "component-effect" research of TCM. Gintonin is a newly isolated non-saponin component of ginseng. Compared to ginsenosides, gintonin has many different pharmacological activities, and it provides new knowledge for the holistic research of ginseng. Thus, taking the discovery of gintonin from ginseng as an example, this paper explored the linkage between ginsenosides and gintonin from the perspective of "component-effect" correlations and systematically sorted out the similarities and differences between them in terms of structural characteristics, modes of action, and pharmacological activities. Starting from the collaborative interaction of TCM compounds, the study discussed the application and value of the holistic view in TCM "component-effect" research in the light of the component structure theory to provide new thoughts for the development of modern TCM research.
Panax/chemistry* ; Drugs, Chinese Herbal/pharmacology* ; Medicine, Chinese Traditional ; Humans ; Ginsenosides/pharmacology* ; Animals

OBJECTIVE: This study aimed to evaluate the relationships of white blood cell (WBC) count, platelet (PLT) count, and PLT-to-WBC ratio (PWR) with muscle mass in Chinese older adults. METHODS: This cross-sectional analysis involved 4,033 Chinese older adults aged ≥ 65 years from the Healthy Ageing and Biomarkers Cohort Study. Muscle mass and total skeletal muscle mass index (TSMI) were measured by bioelectric impedance analysis. WBC, PLT, and PWR were measured using standard methods. Multivariate linear regression was used to examine the associations of WBC count, PLT count, and PWR with TSMI. RESULTS: High WBC count, PLT count, and PWR were associated with low TSMI, with coefficients of -0.0091 (95% confidence interval [ CI]: -0.0142 to -0.0041), -0.0119 (95% CI: -0.0170 to -0.0068), and -0.0051 (95% CI: -0.0102 to -0.0001). The associations between the three inflammatory indices and TSMI were linear. Stratified analyses indicated that the relationship between inflammatory markers and TSMI was more evident in male participants and in individuals aged < 80 years than in their counterparts. CONCLUSION Elevated WBC count, PLT count, and PWR correlated with muscle mass loss. This study highlights the importance of regular monitoring of inflammatory markers as a potential strategy for the screening and management of sarcopenia in older adults.
Humans ; Aged ; Male ; Female ; China ; Leukocyte Count ; Cross-Sectional Studies ; Platelet Count ; Aged, 80 and over ; Muscle, Skeletal/anatomy & histology* ; Independent Living ; Blood Platelets ; Leukocytes ; Sarcopenia

OBJECTIVE: Pneumoconiosis, a lung disease caused by irreversible fibrosis, represents a significant public health burden. This study investigates the causal relationships between gut microbiota, gene methylation, gene expression, protein levels, and pneumoconiosis using a multi-omics approach and Mendelian randomization (MR). METHODS: We analyzed gut microbiota data from MiBioGen and Esteban et al. to assess their potential causal effects on pneumoconiosis subtypes (asbestosis, silicosis, and inorganic pneumoconiosis) using conventional and summary-data-based MR (SMR). Gene methylation and expression data from Genotype-Tissue Expression and eQTLGen, along with protein level data from deCODE and UK Biobank Pharma Proteomics Project, were examined in relation to pneumoconiosis data from FinnGen. To validate our findings, we assessed self-measured gut flora from a pneumoconiosis cohort and performed fine mapping, drug prediction, molecular docking, and Phenome-Wide Association Studies to explore relevant phenotypes of key genes. RESULTS: Three core gut microorganisms were identified: Romboutsia ( OR = 0.249) as a protective factor against silicosis, Pasteurellaceae ( OR = 3.207) and Haemophilus parainfluenzae ( OR = 2.343) as risk factors for inorganic pneumoconiosis. Additionally, mapping and quantitative trait loci analyses revealed that the genes VIM, STX8, and MIF were significantly associated with pneumoconiosis risk. CONCLUSIONS This multi-omics study highlights the associations between gut microbiota and key genes ( VIM, STX8, MIF) with pneumoconiosis, offering insights into potential therapeutic targets and personalized treatment strategies.
Humans ; Male ; East Asian People/genetics* ; Europe ; Gastrointestinal Microbiome ; Lung ; Macrophage Migration-Inhibitory Factors/metabolism* ; Mendelian Randomization Analysis ; Multiomics ; Pneumoconiosis/microbiology* ; Intramolecular Oxidoreductases

OBJECTIVE: The study aim was to investigate the effects of exposure to multiple environmental organic pollutants on cardiopulmonary health with a focus on the potential mediating role of oxidative stress. METHODS: A repeated-measures randomized crossover study involving healthy college students in Beijing was conducted. Biological samples, including morning urine and venous blood, were collected to measure concentrations of 29 typical organic pollutants, including hydroxy polycyclic aromatic hydrocarbons (OH-PAHs), bisphenol A and its substitutes, phthalates and their metabolites, parabens, and five biomarkers of oxidative stress. Health assessments included blood pressure measurements and lung function indicators. RESULTS: Urinary concentrations of 2-hydroxyphenanthrene (2-OH-PHE) ( β = 4.35% [95% confidence interval ( CI): 0.85%, 7.97%]), 3-hydroxyphenanthrene ( β = 3.44% [95% CI: 0.19%, 6.79%]), and 4-hydroxyphenanthrene (4-OH-PHE) ( β = 5.78% [95% CI: 1.27%, 10.5%]) were significantly and positively associated with systolic blood pressure. Exposures to 1-hydroxypyrene (1-OH-PYR) ( β = 3.05% [95% CI: -4.66%, -1.41%]), 2-OH-PHE ( β = 2.68% [95% CI: -4%, -1.34%]), and 4-OH-PHE ( β = 3% [95% CI: -4.68%, -1.29%]) were negatively associated with the ratio of forced expiratory volume in the first second to forced vital capacity. These findings highlight the adverse effects of exposure to multiple pollutants on cardiopulmonary health. Biomarkers of oxidative stress, including 8-hydroxy-2'-deoxyguanosine and extracellular superoxide dismutase, mediated the effects of multiple OH-PAHs on blood pressure and lung function. CONCLUSION Exposure to multiple organic pollutants can adversely affect cardiopulmonary health. Oxidative stress is a key mediator of the effects of OH-PAHs on blood pressure and lung function.
Humans ; Oxidative Stress/drug effects* ; Male ; Cross-Over Studies ; Female ; Young Adult ; Environmental Pollutants/toxicity* ; Environmental Exposure/adverse effects* ; Biomarkers/blood* ; Adult ; Blood Pressure/drug effects* ; Polycyclic Aromatic Hydrocarbons/urine* ; Beijing

The high content of sucrose and raffinose reduces the prebiotic value of soybean oligosaccharides. Fructan sucrases can catalyze the conversion of sucrose and raffinose to high-value products such as fructooligosaccharides and melibiose. To obtain a fructan sucrase that can efficiently convert soybean oligosaccharides, we first mined the fructan sucrase gene from microorganisms in the coastal areas of Xisha Islands and Bohai Bay and then characterized the enzymatic and catalytic properties of the enzyme. Finally, recombinant extracellular expression of this gene was carried out in Bacillus subtilis. The results showed that a novel fructan sucrase, BhLS 39, was mined from Bacillus halotolerans. With sucrose and raffinose as substrates, BhLS 39 showed the optimal temperatures of 50 ℃ and 55 ℃, optimal pH 5.5 for both, and K_cat/K_m ratio of 3.4 and 6.6 L/(mmol·s), respectively. When 400 g/L raffinose was used as the substrate, the melibiose conversion rate was 84.6% after 30 min treatment with 5 U BhLS 39. Furthermore, BhLS 39 catalyzed the conversion of sucrose to produce levan-type-fructooligosaccharide and levan. Then, the recombinant extracellular expression of BhLS 39 in B. subtilis was achieved. The co-expression of the intracellular chaperone DnaK and the extracellular chaperone PrsA increased the extracellular activity of the recombinant BhLS 39 by 5.2 folds to 17 U/mL compared with that of the control strain. BhLS 39 obtained in this study is conducive to improving the quality and economic benefits of soybean oligosaccharides. At the same time, the strategy used here to enhance the extracellular expression of BhLS 39 will also promote the efficient recombinant expression of other proteins in B. subtilis.
Oligosaccharides/metabolism* ; Glycine max/metabolism* ; Bacillus subtilis/metabolism* ; Sucrase/biosynthesis* ; Raffinose/metabolism* ; Fructans/metabolism* ; Sucrose/metabolism* ; Bacillus/genetics* ; Recombinant Proteins/biosynthesis* ; Bacterial Proteins/biosynthesis*

Erectile dysfunction (ED) is prevalent among men, but its relationship with dietary habits is uncertain. The aim of our study was to assess whether dietary patterns enhance erectile function by reviewing the literature published before August 1, 2022, via PubMed, Web of Science, and EMBASE databases. The data compiled included author details; publication dates, countries, treatments, patient numbers, ages, follow-ups, and clinical trial outcomes, such as ED cases, odds ratios (ORs), confidence intervals (CIs), and International Index of Erectile Function-5 (IIEF-5) scores with means and standard deviations. An analysis of 14 studies with 27 389 participants revealed that plant-based diets (OR = 0.71, 95% CI: 0.66-0.75; P < 0.00001), low-fat diets (OR = 0.27, 95% CI: 0.13-0.53; P = 0.0002), and alternative diets such as intermittent fasting and organic diets (OR = 0.54, 95% CI: 0.36-0.80; P = 0.002) significantly reduced ED risk. High-protein low-fat diets (hazard ratio [HR] = 1.38, 95% CI: 1.12-1.64; P < 0.00001) and high-carb low-fat diets (HR = 0.79, 95% CI: 0.55-1.04; P < 0.00001) improved IIEF-5 scores. Combined diet and exercise interventions decreased the likelihood of ED (OR = 0.49, 95% CI: 0.28-0.85; P = 0.01) and increased the IIEF-5 score (OR = 3.40, 95% CI: 1.69-5.11; P < 0.0001). Diets abundant in fruits and vegetables (OR = 0.97, 95% CI: 0.96-0.98; P < 0.00001) and nuts (OR = 0.54, 95% CI: 0.37-0.80; P = 0.002) were also correlated with lower ED risk. Our meta-analysis underscores a strong dietary-ED association, suggesting that low-fat/Mediterranean diets rich in produce and nuts could benefit ED management.
Humans ; Male ; Erectile Dysfunction/epidemiology* ; Diet ; Diet, Fat-Restricted ; Feeding Behavior ; Penile Erection/physiology* ; Diet, Vegetarian

OBJECTIVES: To investigate the chain mediating role of family care and emotional management in the relationship between social support and anxiety among rural primary school students. METHODS: A questionnaire survey was conducted among students in grades 4 to 6 from four counties in Hunan Province. Data were collected using the Social Support Rating Scale, Family Care Index Scale, Emotional Intelligence Scale, and Generalized Anxiety Disorder -7. Logistic regression analysis was used to explore the influencing factors of anxiety symptoms. Mediation analysis was conducted to assess the chain mediating effects of family care and emotional management between social support and anxiety. RESULTS: A total of 4 141 questionnaires were distributed, with 3 874 valid responses (effective response rate: 93.55%). The prevalence rate of anxiety symptoms among these students was 9.32% (95%CI: 8.40%-10.23%). Significant differences were observed in the prevalence rates of anxiety symptoms among groups with different levels of social support, family functioning, and emotional management ability (P<0.05). The total indirect effect of social support on anxiety symptoms via family care and emotional management was significant (β=-0.137, 95%CI: -0.167 to -0.109), and the direct effect of social support on anxiety symptoms remained significant (P<0.05). Family care and emotional management served as significant chain mediators in the relationship between social support and anxiety symptoms (β=-0.025,95%CI:-0.032 to -0.018), accounting for 14.5% of the total effect. CONCLUSIONS Social support can directly affect anxiety symptoms among rural primary school students and can also indirectly influence anxiety symptoms through the chain mediating effects of family care and emotional management. These findings provide scientific evidence for the prevention of anxiety in primary school students from multiple perspectives.
Humans ; Female ; Male ; Social Support ; Anxiety/etiology* ; Child ; Students/psychology* ; Emotions ; Logistic Models