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.

Gastric cancer remains a highly prevalent malignancy worldwide, with surgical resection currently constituting the cornerstone of treatment aimed at improving long-term patient survival. Owing to their notable advantages, including reduced surgical trauma and accelerated postoperative recovery, minimally invasive procedures are progressively supplanting conventional open surgery and have become the mainstream approach in gastric cancer management. Concurrently, the rapid advancement of artificial intelligence (AI) technologies has enabled real-time intraoperative monitoring of surgical scenes, thereby furnishing novel technical support for adjunctive decision-making, surgical navigation, and skill assessment during gastrectomy. This article provides a systematic review of the current status of AI applications in minimally invasive gastric cancer surgery, with a particular focus on research progress pertaining to instrument recognition, surgical phase identification, delineation of normal anatomical structures, detection of metastatic foci, and early warning of intraoperative adverse events. Furthermore, we discuss the potential value of AI in enhancing surgical efficiency, ensuring patient safety, and optimizing surgical education. On this basis, we further analyze the principal challenges and inherent risks confronting current AI systems, with the aim of informing future technological innovation and facilitating clinical translation.

The present study aimed to investigate the effects of eccentric treadmill exercise on adaptive hypertrophy of skeletal muscle in rats. Thirty-two 3-month-old Sprague Dawley (SD) rats were selected and randomly assigned to one of the four groups based on their body weights: 2-week quiet control group (2C), 2-week downhill running exercise group (2E), 4-week quiet control group (4C), and 4-week downhill running exercise group (4E). The downhill running protocol for rats in the exercise groups involved slope of -16°, running speed of 16 m/min, training duration of 90 min, and 5 training sessions per week. Twenty-four hours after the final session of training, all the four groups of rats underwent an exhaustion treadmill exercise. After resting for 48 h, all the rats were euthanized and their gastrocnemius muscles were harvested for analysis. HE staining was used to measure the cross-sectional area (CSA) and diameter of muscle fibers. Transmission electron microscope was used to observe the ultrastructural changes in muscle fibers. Purithromycin surface labeling translation method was used to measure protein synthesis rate. Immunofluorescence double labeling was used to detect the colocalization levels of lysosomal-associated membrane protein 2 (Lamp2)-leucyl-tRNA synthetase (LARS) and Lamp2-mammalian target of rapamycin (mTOR). Western blot was used to measure the protein expression levels of myosin heavy chain (MHC) IIb and LARS, as well as the phosphorylation levels of mTOR, p70 ribosomal protein S6 kinase (p70S6K), and eukaryotic translation initiation factor 4E binding protein 1 (4E-BP1). The results showed that, compared with the 2C group rats, the 2E group rats showed significant increases in wet weight of gastrocnemius muscle, wet weight/body weight ratio, running distance, running time, pre- and post-exercise blood lactate levels, myofibrillar protein content, colocalization levels of Lamp2-LARS and Lamp2-mTOR, and LARS protein expression. Besides these above changes, compared with the 4C group, the 4E group further exhibited significantly increased fiber CSA, fiber diameter, protein synthesis rate, and phosphorylation levels of mTOR, p70S6K, and 4E-BP1. Compared with the quiet control groups, the exercise groups exhibited ultrastructural damage of rat gastrocnemius muscle, which was more pronounced in the 4E group. These findings suggest that eccentric treadmill exercise may promote mTOR translocation to lysosomal membrane, activating mTOR signaling via up-regulating LARS expression. This, in turn, increases protein synthesis rate through the mTOR-p70S6K-4E-BP1 signaling pathway, promoting protein deposition and inducing adaptive skeletal muscle hypertrophy. Although the ultrastructural changes of skeletal muscle are more pronounced, the relatively long training cycles during short-term exercise periods have a more significant effect on promoting gastrocnemius muscle protein synthesis and adaptive hypertrophy.
Animals ; Rats, Sprague-Dawley ; Physical Conditioning, Animal/physiology* ; Rats ; Muscle, Skeletal/metabolism* ; TOR Serine-Threonine Kinases/metabolism* ; Male ; Hypertrophy ; Adaptation, Physiological/physiology* ; Adaptor Proteins, Signal Transducing/metabolism* ; Ribosomal Protein S6 Kinases, 70-kDa/metabolism* ; Intracellular Signaling Peptides and Proteins

This study aims to reveal the effect and mechanism of Gentianella turkestanorum total extract(GTI) in ameliorating non-alcoholic steatohepatitis(NASH). UPLC-Q-TOF-MS was employed to identify the chemical components in GTI. SwissTarget-Prediction, GeneCards, OMIM, and TTD were utilized to screen the targets of GTI components and NASH. The common targets shared by GTI components and NASH were filtered through the STRING database and Cytoscape 3.9.0 to identify core targets, followed by GO and KEGG enrichment analysis. AutoDock was used for molecular docking of key components with core targets. A mouse model of NASH was established with a methionine-choline-deficient high-fat diet. A 4-week drug intervention was conducted, during which mouse weight was monitored, and the liver-to-brain ratio was measured at the end. Hematoxylin-eosin staining, Sirius red staining, and oil red O staining were employed to observe the pathological changes in the liver tissue. The levels of various biomarkers, including aspartate aminotransferase(AST), alanine aminotransferase(ALT), hydroxyproline(HYP), total cholesterol(TC), triglycerides(TG), low-density lipoprotein cholesterol(LDL-C), high-density lipoprotein cholesterol(HDL-C), malondialdehyde(MDA), superoxide dismutase(SOD), and glutathione(GSH), in the serum and liver tissue were determined. RT-qPCR was conducted to measure the mRNA levels of interleukin 1β(IL-1β), interleukin 6(IL-6), tumor necrosis factor α(TNF-α), collagen type I α1 chain(COL1A1), and α-smooth muscle actin(α-SMA). Western blotting was conducted to determine the protein levels of IL-1β, IL-6, TNF-α, and potential drug targets identified through network pharmacology. UPLC-Q-TOF/MS identified 581 chemical components of GTI, and 534 targets of GTI and 1 157 targets of NASH were screened out. The topological analysis of the common targets shared by GTI and NASH identified core targets such as IL-1β, IL-6, protein kinase B(AKT), TNF, and peroxisome proliferator activated receptor gamma(PPARG). GO and KEGG analyses indicated that the ameliorating effect of GTI on NASH was related to inflammatory responses and the phosphoinositide 3-kinase(PI3K)/AKT pathway. The staining results demonstrated that GTI ameliorated hepatocyte vacuolation, swelling, ballooning, and lipid accumulation in NASH mice. Compared with the model group, high doses of GTI reduced the AST, ALT, HYP, TC, and TG levels(P<0.01) while increasing the HDL-C, SOD, and GSH levels(P<0.01). RT-qPCR results showed that GTI down-regulated the mRNA levels of IL-1β, IL-6, TNF-α, COL1A1, and α-SMA(P<0.01). Western blot results indicated that GTI down-regulated the protein levels of IL-1β, IL-6, TNF-α, phosphorylated PI3K(p-PI3K), phosphorylated AKT(p-AKT), phosphorylated inhibitor of nuclear factor kappa B alpha(p-IκBα), and nuclear factor kappa B(NF-κB)(P<0.01). In summary, GTI ameliorates inflammation, dyslipidemia, and oxidative stress associated with NASH by regulating the PI3K/AKT/NF-κB signaling pathway.
Animals ; Non-alcoholic Fatty Liver Disease/genetics* ; Mice ; Network Pharmacology ; Male ; Drugs, Chinese Herbal/administration & dosage* ; Chromatography, High Pressure Liquid ; Liver/metabolism* ; Mice, Inbred C57BL ; Humans ; Mass Spectrometry ; Tumor Necrosis Factor-alpha/metabolism* ; Disease Models, Animal ; Molecular Docking Simulation

This study aims to explore the mechanism of Jiaotai Pills in treating depression based on metabolomics and network pharmacology. The chemical constituents of Jiaotai Pills were identified by UHPLC-Orbitrap Exploris 480, and the targets of Jiaotai Pills and depression were retrieved from online databases. STRING and Cytoscape 3.7.2 were used to construct the protein-protein interaction network of core targets of Jiaotai Pills in treating depression and the "compound-target-pathway" network. DAVID was used for Gene Ontology(GO) function and Kyoto Encyclopedia of Genes and Genomes(KEGG) pathway enrichment analyses of the core targets. The mouse model of depression was established with chronic unpredictable mild stress(CUMS) and treated with different doses of Jiaotai Pills. The behavioral changes and pathological changes in the hippocampus were observed. UHPLC-Orbitrap Exploris 120 was used for metabolic profiling of the serum, from which the differential metabolites and related metabolic pathways were screened. A "metabolite-reaction-enzyme-gene" network was constructed for the integrated analysis of metabolomics and network pharmacology. A total of 34 chemical components of Jiaotai Pills were identified, and 143 core targets of Jiaotai Pills in treating depression were predicted, which were mainly involved in the arginine and proline, sphingolipid, and neurotrophin metabolism signaling pathways. The results of animal experiments showed that Jiaotai Pills alleviated the depression behaviors and pathological changes in the hippocampus of the mouse model of CUMS-induced depression. In addition, Jiaotai Pills reversed the levels of 32 metabolites involved in various pathways such as arginine and proline metabolism, sphingolipid metabolism, and porphyrin metabolism in the serum of model mice. The integrated analysis showed that arginine and proline metabolism, cysteine and methionine metabolism, and porphyrin metabolism might be the key pathways in the treatment of depression with Jiaotai Pills. In conclusion, metabolomics combined with network pharmacology clarifies the antidepressant mechanism of Jiaotai Pills, which may provide a basis for the clinical application of Jiaotai Pills in treating depression.
Animals ; Drugs, Chinese Herbal/chemistry* ; Depression/genetics* ; Mice ; Network Pharmacology ; Metabolomics ; Male ; Disease Models, Animal ; Humans ; Protein Interaction Maps/drug effects* ; Antidepressive Agents

Based on the α7 nicotinic acetylcholine receptor(α7nAChR), this study examined how tetrahydropalmatine(THP) affected BV2 microglia exposed to lipopolysaccharide(LPS), aiming to clarify the possible mechanism underlying the anti-depression effect of THP from the perspectives of preventing inflammation and regulating polarization. First, after molecular docking and determination of the content of Corydalis saxicola Bunting total alkaloids, THP was initially identified as a possible anti-depression component. The BV2 microglia model of inflammation was established with LPS. BV2 microglia were allocated into a normal group, a model group, low-and high-dose(20 and 40 μmol·L~(-1), respectively) THP groups, and a THP(20 μmol·L~(-1))+α7nAChR-specific antagonist MLA(1 μmol·L~(-1)) group. The CCK-8 assay was used to screen the safe concentration of THP. A light microscope was used to examine the morphology of the cells. Western blot and immunofluorescence were used to determine the expression of α7nAChR. qRT-PCR was performed to determine the mRNA levels of inducible nitric oxide synthase(iNOS), cluster of differentiation 86(CD86), suppressor of cytokine signaling 3(SOCS3), arginase-1(Arg-1), cluster of differentiation 206(CD206), tumor necrosis factor(TNF)-α, interleukin(IL)-6, and IL-1β. Enzyme-linked immunosorbent assay(ELISA) was employed to measure the levels of TNF-α, IL-6, and IL-1β in the cell supernatant. The experimental results showed that THP at concentrations of 40 μmol·L~(-1) and below had no effect on BV2 microglia. THP improved the morphology of BV2 microglia, significantly up-regulated the protein level of α7nAChR, significantly down-regulated the mRNA levels of iNOS, CD86, SOCS3, TNF-α, IL-6, and IL-1β, significantly up-regulated the mRNA levels of Arg-1 and CD206, and dramatically lowered the levels of TNF-α, IL-6, and IL-1β in the cell supernatant. However, the antagonist MLA abolished the above-mentioned ameliorative effects of THP on LPS-treated BV2 microglia. As demonstrated by the aforementioned findings, THP protected LPS-treated BV2 microglia by regulating the M1/M2 polarization and preventing inflammation, which might be connected to the regulation of α7nAChR on BV2 microglia.
Berberine Alkaloids/chemistry* ; alpha7 Nicotinic Acetylcholine Receptor/chemistry* ; Microglia/metabolism* ; Mice ; Animals ; Cell Line ; Corydalis/chemistry* ; Humans ; Molecular Docking Simulation ; Inflammation/drug therapy* ; Nitric Oxide Synthase Type II/immunology* ; Tumor Necrosis Factor-alpha/immunology*

Various techniques have been described for reconstructing the skin of the penile shaft; however, no universally accepted standard exists for correcting buried penis in adults. We aimed to describe a new technique for correcting an adult-acquired buried penis through a diamond-shaped incision at the penopubic junction. We retrospectively analyzed data from patients treated with our technique between March 2019 and June 2023 in the Department of Andrology, Nanjing Drum Tower Hospital (Nanjing, China). Forty-two adult males with buried penises, with a mean (±standard deviation [s.d.]) age of 26.6 (±6.6) years, underwent surgery. All patients were obese, with an average (±s.d.) body mass index of 35.56 (±3.23) kg m -2 . In addition to phalloplasty, 32 patients concurrently underwent circumcision, and 28 underwent suprapubic liposuction. The mean (±s.d.) duration of the operation was 98.02 (±13.28) min. The mean (±s.d.) duration of follow-up was 6.71 (±3.43) months. The length in the flaccid unstretched state postoperatively was significantly greater than that preoperatively (mean ± s.d: 5.55±1.19 cm vs 1.94±0.59 cm, P < 0.01). Only minor complications, such as wound disruption (7.1%) and infection (4.8%), were observed. The mean (±s.d.) score of patient satisfaction was 4.02 (±0.84) on a scale of 5. This technique provides excellent cosmetic and functional outcomes with a minimal risk of complications. However, additional clinical studies are needed to evaluate the long-term effects of this procedure.
Humans ; Male ; Patient Satisfaction ; Adult ; Lipectomy/methods* ; Penis/surgery* ; Retrospective Studies ; Treatment Outcome ; Plastic Surgery Procedures/methods* ; Young Adult ; Penile Diseases/surgery* ; Urologic Surgical Procedures, Male/methods*

OBJECTIVE: To analyze the Epstein-Barr virus (EBV) load in different lymphocyte subsets, as well as clinical characteristics and outcomes in patients with hematologic malignancies experiencing EBV reactivation. METHODS: Peripheral blood samples from patients were collected. B, T, and NK cells were isolated sorting with magnetic beads by flow cytometry. The EBV load in each subset was quantitated by real-time quantitative polymerase chain reaction (RT-qPCR). Clinical data were colleted from electronic medical records. Survival status was followed up through outpatient visits and telephone calls. Statistical analyses were performed using SPSS 25.0. RESULTS: A total of 39 patients with hematologic malignancies were included, among whom 35 patients had undergone allogeneic hematopoietic stem cell transplantation (allo-HSCT). The median time to EBV reactivation was 4.8 months (range: 1.7-57.1 months) after allo-HSCT. EBV was detected in B, T, and NK cells in 20 patients, in B and T cells in 11 patients, and only in B cells in 4 patients. In the 35 patients, the median EBV load in B cells was 2.19×10⁴ copies/ml, significantly higher than that in T cells (4.00×10³ copies/ml, P <0.01) and NK cells (2.85×10² copies/ml, P <0.01). Rituximab (RTX) was administered for 32 patients, resulting in EBV negativity in 32 patients with a median time of 8 days (range: 2-39 days). Post-treatment analysis of 13 patients showed EBV were all negative in B, T, and NK cells. In the four non-transplant patients, the median time to EBV reactivation was 35 days (range: 1-328 days) after diagnosis of the primary disease. EBV was detected in one or two subsets of B, T, or NK cells, but not simultaneously in all three subsets. These patients received a combination chemotherapy targeting at the primary disease, with 3 patients achieving EBV negativity, and the median time to be negative was 40 days (range: 13-75 days). CONCLUSION In hematologic malignancy patients after allo-HSCT, EBV reactivation commonly involves B, T, and NK cells, with a significantly higher viral load in B cells compared to T and NK cells. Rituximab is effective for EBV clearance. In non-transplant patients, EBV reactivation is restricted to one or two lymphocyte subsets, and clearance is slower, highlighting the need for prompt anti-tumor therapy.
Humans ; Hematologic Neoplasms/virology* ; Herpesvirus 4, Human/physiology* ; Epstein-Barr Virus Infections ; Hematopoietic Stem Cell Transplantation ; Virus Activation ; Lymphocyte Subsets/virology* ; Flow Cytometry ; Killer Cells, Natural/virology* ; Male ; Female ; B-Lymphocytes/virology* ; Viral Load ; Adult ; T-Lymphocytes/virology* ; Middle Aged