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/Aims: Liver transplantation (LT) following total hepatectomy is a life-saving treatment for hepatocellular carcinoma (HCC). The HCC recurrence after LT hinders the effectiveness of the procedure. The objective of this study is to develop a pre-operative risk stratification model based on a liquid biopsy. Methods: We conducted a comprehensive multi-omics study of 260 HCC patients from three centers, including clinical data, low-coverage whole-genome sequencing of cell-free DNA (cfDNA) from plasma, as well as whole-exome, single-nucleus RNA, and spatial transcriptomics from matched tumor and non-tumor tissues. Results: We identified cfDNA-derived copy number alteration (CNA) signatures associated with post-transplant recurrence. By integrating cfDNA-derived CNA profiles with single-cell transcriptomic data, we traced recurrence-associated cfDNA to a distinct subpopulation of malignant cells within the primary tumor. These cells were embedded in a pro-metastatic microenvironment of specialized endothelial subtypes and cancer-associated fibroblasts. Notably, most recurrence-associated lesions were detectable in cfDNA prior to liver transplantation (LT). Building on these insights, we developed the ZJU Criteria based on CNA fragments and tumor markers, a pre-LT risk prediction tool that integrates conventional clinical factors with cfDNA-derived CNA signatures, and validated it using internal and independent external cohorts. Conclusion Our findings suggest that post-transplant recurrence commonly originates from advanced subclones that emerge late during tumor evolution. The ZJU Criteria provides an accurate, non-invasive strategy that significantly improves pre-LT risk stratification and clinical decision-making for patients with HCC.

Background Pulmonary heart disease (PHD) is a critical cardiopulmonary disease in high-altitude areas, with pulmonary hypertension (PH) as its central pathological basis. Cold and hypoxia are vital environmental characteristics of high-altitude regions; however, their joint effect on the hospitalization risk for PHD and the underlying biological mechanisms remain unclear. Objective To evaluate the associations of cold, hypoxia, and their joint exposure with PHD hospitalization risk, and to investigate PH-related pathological changes induced by combined cold and hypoxic exposure and the intervention effects of γ-aminobutyric acid (GABA) in rats. Methods Based on hospitalization data for 92986 PHD cases from 333 hospitals in Gansu Province (2018–2022), a generalized additive model (GAM) with a quasi-Poisson distribution was used to assess the associations between daily mean temperature, atmospheric oxygen content (AOC) and the daily number of PHD hospitalizations. Four joint exposure categories were constructed using the fifth percentile of temperature (P₅, −7.90℃) and AOC grades (high or low) to assess the associations between different exposure combinations and daily PHD hospital admissions. To further investigate PH-related pathological changes and the intervention effects of GABA, 24 specific-pathogen-free male Sprague–Dawley rats were randomly assigned to four groups (n=6): normal temperature and normoxia control group (NC), combined cold and hypoxia exposure group (CH), combined exposure plus low-dose GABA intervention group (CHL), and combined exposure plus high-dose GABA intervention group (CHH). After 5 weeks, pulmonary hemodynamics and vascular remodeling were evaluated. Gut microbiota and inflammatory cytokines in lung tissue were assessed using 16S rRNA sequencing and enzyme-linked immunosorbent assay (ELISA), respectively. Results Each 1 g·m⁻³ decrease in AOC and each 1 ℃ decrease in daily mean temperature were associated with 2.6% (RR=1.026, 95%CI: 1.024, 1.027) and 1.8% (RR=1.018, 95%CI: 1.015, 1.020) increases in PHD hospitalization risk, respectively. Compared with the non-low-temperature and high-AOC group, the low-temperature and low-AOC group had the highest risk estimate (RR=1.673, 95%CI: 1.567, 1.785). Animal experiments demonstrated that, compared with the NC group, rats in the CH group showed shortened pulmonary artery acceleration time (PAT) and increased mean pulmonary artery pressure (MPAP), right ventricular hypertrophy index (RVHI), pulmonary arterial wall thickness, and α-smooth muscle actin (α-SMA) expression (P<0.05). The Observed Species and Chao1 indices of the gut microbiota were significantly lower in the CH group than in the NC group (P<0.05), while levels of tumor necrosis factor-alpha (TNF-α) and interleukin-1beta (IL-1β) in lung tissue were elevated (P<0.05). GABA intervention improved selected hemodynamic, vascular remodeling, and inflammatory indices. Conclusion Decreases in daily mean temperature and AOC are associated with an increased risk of PHD hospitalization, with the highest risk estimate observed in the low-temperature and low-AOC group. Animal experiments suggest that combined cold and hypoxic exposure induce PH-related pathological changes accompanied by alterations in the gut microbiota and pulmonary inflammation. GABA may partially ameliorate these changes.

The development of traditional Chinese medicine (TCM) diagnosis and treatment has undergone multiple paradigms, evolving from sporadic experiential practices to systematic approaches in syndrome differentiation and treatment and further integration of disease and syndrome frameworks. TCM is a vital component of the medical system, valued alongside Western medicine. Treatment based on syndrome differentiation embodies both personalized treatment and holistic approaches; however, the inconsistency and lack of stability in syndrome differentiation limit clinical efficacy. The existing integration of diseases and syndromes primarily relies on patchwork and embedded systems, where the full advantages of synergy between Chinese and Western medicine are not fully realized. Recently, driven by the development of diagnosis and treatment concepts and advances in analytical technology, Western medicine has been rapidly transforming from a traditional biological model to a precision medicine model. TCM faces a similar need to progress beyond traditional syndrome differentiation and disease-syndrome integration toward a more precise diagnosis and treatment paradigm. Unlike the micro-level precision trend of Western medicine, precision diagnosis and treatment in TCM is primarily reflected in data-driven applications that incorporate information at various levels, including precise syndrome differentiation, medication, disease management, and efficacy evaluation. The current priority is to accelerate the development of TCM precision diagnosis and treatment technology platforms and advance discipline construction in this area.

Objective:To explore the effects of Qingre Qudu Decoction for fumigation combined with three-gap drainage on wound healing and serum inflammatory factors in patients with acute perianal abscess.Methods:Randomized controlled trial was conducted. A total of 117 patients with acute perianal abscess in the hospital were enrolled as the observation objects between August 2022 and May 2024. According to random number table method, they were divided into observation group (59 cases) and control group (58 cases). Both groups received three-gap drainage therapy. On basis of three-gap drainage, control group was given potassium permanganate, while observation group was given Qingre Qudu Decoction for fumigation. All patients were treated for 14 d. The growth of granulation tissue and wound secretions before and after treatment was evaluated. VAS scale was used to evaluate the degree of incision pain, and Wexner score was used to assess incontinence; ELISA was used to detect serum activator A (ACTA), immunoturbidimetry was used to detect serum CRP, and radioimmunoassay was used to detect serum IL-6 levels. The occurrence of complications and abscess recurrence during treatment was recorded, and clinical efficacy was evaluated.Results:The total effective rate of the observation group was 96.61% (57/59), while that of the control group was 82.76% (48/58), with statistical significance ( χ2=6.10, P=0.014). After treatment, scores of granulation tissue growth and wound secretions in observation group, and scores of VAS and Wexner incontinence in observation group were lower than those in the control group ( t=9.66, 5.00, 7.98, 3.65, P<0.001), and wound healing time was shorter than that in control group ( t=8.41, P<0.001). After treatment, levels of serum ACTA, CRP and IL-6 in observation group were lower than those in control group ( t=15.30, 2.08, 19.34, P<0.01 or P<0.05). The incidence of postoperative complications in the observation group was 6.78% (4/59), while in the control group it was 27.59% (16/58), with statistical significance ( χ2=8.93, P=0.003). Conclusion:Qingre Qudu Decoction for fumigation combined with three-gap drainage can relieve postoperative incision pain, inhibit inflammatory response, accelerate the recovery of wound and promote the recovery of anal function and improve clinical efficacy.

Objective To analyze the differences in bone serum protein between patients with osteoporosis accompanied by obstructive sleep apnea syndrome(OSAS)and those with osteoporosis only using proteomics.Methods A total of 80 osteoporosis patients who attended our hospital between June 2022 and June 2024 were enrolled.Based on their polysomnography results,the participants were divided into an OSAS and osteoporosis comorbidity(OSAS-osteoporosis)group(n=42)and an osteoporosis only group(n=38).Propensity score matching was applied to incorporate covariates in logistic regression so that the individual characteristics of the two groups of patients were generally balanced.Following the matching procedure,a final cohort of 20 matched pairs was obtained and subsequently utilized for further analysis.The mass spectrum was obtained using laser desorption ionization mass spectrometry.Principal component analysis(PCA)was performed to assess differences in metabolic patterns between groups.Partial least squares discriminant analysis(PLS-DA)and orthogonal PLS-DA(OPLS-DA)were employed for further data analysis.Variable importance in projection(VIP)scores of each substance were calculated with OPLS-DA to screen the metabolites showing inter-group differences.Heatmaps were generated to visualize metabolic profile differences between the OSAS-osteoporosis group and the osteoporosis group.Enrichment pathway analysis was conducted on the differential identified metabolites.Results After propensity score matching,individual characteristics between the groups were well balanced.Mass spectrometry revealed significant differences between the OSAS-osteoporosis and osteoporosis groups.In the PCA score plot,the separation trend of the two groups was not significant.The PLS-DA score plot showed a discernible separation trend,with R2 and Q2 lower than those of the corresponding results of the real model,confirming the reliability of the model.OPLS-DA showed that the total R2X of the model was 0.635,R2Y was 0.879,and O2Y was 0.728,showing obvious separation trends between the two groups.A total of 16 differential metabolites were identified,including stearyl-oleyl-glycerol phosphate choline,phosphate choline,L-histidine,erucamide,2'-deoxyuridine,1-palmitoyl glycerol,thymine,tyramine,L-pyroglutamic acid,L-glutamic acid,myristate,glycerol-3-phosphate,caprylic acid,pregnenolone,L-arginine,D-4-hydroxyphenylglycine,and isobutyric acid.Heatmaps showed significant differences in metabolic profiles between the OSAS-osteoporosis group and the osteoporosis group.Pathway enrichment analysis showed that 27 metabolic pathways were involved.27 metabolic pathways.Under the conditions of P<0.05 and pathway impact>0.2,the three most significant metabolic pathways identified included mainly alanine,aspartate,and glutamate metabolism,arginine biosynthesis,and histidine metabolism.Conclusion Significant differences were observed in the metabolic profiles between patients with both OSAS and osteoporosis and those with osteoporosis alone.

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

Body weight abnormalities, including overweight, obesity, and underweight, have become a dual public health challenge in Chinese adults: overweight and obesity lead to a variety of chronic complications, while underweight increases the risks of malnutrition, sarcopenia, and organ dysfunction. To systematically address these issues, multidisciplinary experts in endocrinology, sports science, nutrition, and psychiatry from various regions have held multiple weight management seminars. Based on the latest epidemiological data and clinical evidence, they expanded the guideline to include assessment and intervention strategies for underweight, in addition to the core content of obesity management. This guideline outlines the etiological mechanisms, evaluation methods, and multidimensional management strategies for overweight and obesity, covering key areas such as diagnosis and assessment, medical nutrition therapy, exercise prescription, pharmacological intervention, and psychological support. It is intended to provide a scientific and standardized approach to weight management across the adult population, aiming to curb the rising prevalence of obesity, mitigate complications associated with abnormal body weight, and improve nutritional status and overall quality of life.