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.

OBJECTIVE: Microcirculatory disturbance is pathologically critical to acute pancreatitis (AP), which can be effectively alleviated by traditional Chinese medicine (TCM) formulas that activate blood flow. However, there has been no evidence-based research to date. Therefore, a well-designed systematic review and meta-analysis is necessary to elucidate the therapeutic transformative benefit of improving microcirculation during AP. This study aims to confirm the therapeutic efficacy of TCM formulas and explore the potential mechanisms underlying their effects on AP treatment. METHODS: Studies from eight databases including Pubmed, Embase, Web of Science, Cochrane Library, CNKI, CBM, Wanfang, and Chinese VIP, were screened for the eligible randomized controlled trials (RCTs). The APACHE II score and effectiveness rate were set as primary outcomes, while mortality rate, complications, total hospital stays, serum amylase recovery time, the time until the disappearance of abdominal pain, microcirculation indicators, and inflammation indicators were chosen as secondary outcomes. A systematic review and meta-analysis were subsequently conducted. Network pharmacology analysis was performed to analyze potential bioactive components with relevant targets of the core herbs included in the TCM formulas for activating blood flow. RESULTS: A total of 51 RCTs (n = 3 721) were included. Compared with conventional western medical treatments alone, TCM groups were associated with lower APACHE II score (SMD =  - 1.36, 95% CI: -2.01 to - 0.71, P = 0.000) and higher effectiveness rate (RR: 1.22, 95% CI: 1.18 to 1.26, P = 0.000). Furthermore, the formulas for activating blood flow demonstrated significant efficacy in improving both microcirculation and inflammation indicators. Additionally, six core Chinese herbal medicines including Rhei Radix et Rhizoma with the highest frequency, Aurantii Fructus Immaturus, Paeoniae Radix Rubra, Bupleuri Radix, Salviae Miltiorrhizae Radix et Rhizoma, and Corydalis Rhizoma were filtered out from the adopted TCM formulas. Finally, 166 shared targets between the six herbs and AP were identified. KEGG analysis indicated that lipid and atherosclerosis pathway is highly related to microcirculation. CONCLUSION TCM formulas for activating blood flow significantly improve microcirculation and alleviate AP. Further high-quality, well-designed RCTs and deep mechanism exploration are required.

ObjectiveThe theory of homotherapy for heteropathy is one of the classical rules in traditional Chinese medicine. Taking this theory as a breakthrough point， this study employed gas chromatography-mass spectrometry （GC-MS） to elucidate the mechanism underlying the therapeutic effects of Zhuyuwan on both intrahepatic cholestasis （IC） and ulcerative colitis （UC） from the viewpoint of serum metabolic homeostasis. MethodsThe rat models of α-naphthylisothiocyanate （ANIT）-induced cholestasis and 2，4，6-trinitro-benzenesulfonic acid （TNBS）-induced UC were treated with low （0.6 g·kg^-1） and high （1.2 g·kg^-1） doses of Zhuyuwan by gavage. In the experiment regarding IC， 24 Sprague-Dawley （SD） rats were randomly assigned into four groups： normal， ANIT model， low-dose Zhuyuwan， and high-dose Zhuyuwan. In the experiment regarding UC， 24 SD rats were randomly allocated into four groups： normal， TNBS model， low-dose Zhuyuwan， and high-dose Zhuyuwan. Firstly， the two disease models and the intervention effects of Zhuyuwan on the two diseases were evaluated based on serum levels of biochemical indicators ［alanine aminotransferase （ALT）， aspartate transaminase （AST）， γ-glutamyltranspeptidase （γ-GT）， and total bile acid （TBA）］， colon damage score， colon weight index， disease activity index， and histopathological changes in rats. Secondly， the rat serum samples were analyzed by gas chromatography-mass spectrometry （GC-MS） to screen the common core pathways of the two disease models， and the expression of core genes in the pathways was determined by Real-time PCR， on the basis of which the biological mechanism of the treatment of the two disease models by Zhuyuwan was ultimately elucidated. ResultsThe results of the experiment regarding IC showed that the ANIT model group had higher ALT， AST， γ-GT， and TBA levels than the normal group （P<0.01）. Compared with the ANIT model group， the low-dose Zhuyuwan group showed declined ALT and TBA levels （P<0.01） and the high-dose Zhuyuwan group showed lowered ALT， TBA， AST， and γ-GT levels （P<0.01）. The results of the experiment regarding UC showed that compared with the normal group， the TNBS model group presented increases in the colonic damage score， colon weight index， and disease activity index （P<0.01）. Compared with the TNBS model group， the low-dose Zhuyuwan group showcased declines in colon weight index （P<0.01） and disease activity index （P<0.05）， and the high-dose Zhuyuwan group showed reductions in the colon damage score， colon weight index， and disease activity index （P<0.01）. GC-MS metabolomics analysis combined with qRT-PCR demonstrated that Zhuyuwan had a similar inverse regulatory effect on arginine metabolism disruption in the above two disease models. ConclusionZhuyuwan exhibited definite therapeutic effects on both IC and UC， and the regulation of arginine biosynthesis pathway is the core mechanism for the treatment of both diseases by Zhuyuwan.

ObjectiveThe theory of homotherapy for heteropathy is one of the classical rules in traditional Chinese medicine. Taking this theory as a breakthrough point， this study employed gas chromatography-mass spectrometry （GC-MS） to elucidate the mechanism underlying the therapeutic effects of Zhuyuwan on both intrahepatic cholestasis （IC） and ulcerative colitis （UC） from the viewpoint of serum metabolic homeostasis. MethodsThe rat models of α-naphthylisothiocyanate （ANIT）-induced cholestasis and 2，4，6-trinitro-benzenesulfonic acid （TNBS）-induced UC were treated with low （0.6 g·kg^-1） and high （1.2 g·kg^-1） doses of Zhuyuwan by gavage. In the experiment regarding IC， 24 Sprague-Dawley （SD） rats were randomly assigned into four groups： normal， ANIT model， low-dose Zhuyuwan， and high-dose Zhuyuwan. In the experiment regarding UC， 24 SD rats were randomly allocated into four groups： normal， TNBS model， low-dose Zhuyuwan， and high-dose Zhuyuwan. Firstly， the two disease models and the intervention effects of Zhuyuwan on the two diseases were evaluated based on serum levels of biochemical indicators ［alanine aminotransferase （ALT）， aspartate transaminase （AST）， γ-glutamyltranspeptidase （γ-GT）， and total bile acid （TBA）］， colon damage score， colon weight index， disease activity index， and histopathological changes in rats. Secondly， the rat serum samples were analyzed by gas chromatography-mass spectrometry （GC-MS） to screen the common core pathways of the two disease models， and the expression of core genes in the pathways was determined by Real-time PCR， on the basis of which the biological mechanism of the treatment of the two disease models by Zhuyuwan was ultimately elucidated. ResultsThe results of the experiment regarding IC showed that the ANIT model group had higher ALT， AST， γ-GT， and TBA levels than the normal group （P<0.01）. Compared with the ANIT model group， the low-dose Zhuyuwan group showed declined ALT and TBA levels （P<0.01） and the high-dose Zhuyuwan group showed lowered ALT， TBA， AST， and γ-GT levels （P<0.01）. The results of the experiment regarding UC showed that compared with the normal group， the TNBS model group presented increases in the colonic damage score， colon weight index， and disease activity index （P<0.01）. Compared with the TNBS model group， the low-dose Zhuyuwan group showcased declines in colon weight index （P<0.01） and disease activity index （P<0.05）， and the high-dose Zhuyuwan group showed reductions in the colon damage score， colon weight index， and disease activity index （P<0.01）. GC-MS metabolomics analysis combined with qRT-PCR demonstrated that Zhuyuwan had a similar inverse regulatory effect on arginine metabolism disruption in the above two disease models. ConclusionZhuyuwan exhibited definite therapeutic effects on both IC and UC， and the regulation of arginine biosynthesis pathway is the core mechanism for the treatment of both diseases by Zhuyuwan.

OBJECTIVE: To investigate the accuracy and safety of pedicle screw placement using 3D-printed auxiliary guides in scoliosis correction surgery for adolescents. METHODS: A retrospective analysis was conducted on the clinical data of 51 patients who underwent posterior scoliosis correction surgery from January 2020 to March 2023. Among them, there were 35 cases of adolescent idiopathic scoliosis and 16 cases of congenital scoliosis. The patients were divided into two groups based on the auxiliary tool used:the 3D-printed auxiliary guide screw placement group (3D printing group) and the free-hand screw placement group (free-hand group, without auxiliary tools). The 3D printing group included 32 patients (12 males and 20 females) with an average age of (12.59±2.60) years;the free-hand group included 19 patients (7 males and 12 females) with an average age of (14.58±3.53) years. The two groups were compared in terms of screw placement accuracy and safety, spinal correction rate, intraoperative blood loss, number of intraoperative fluoroscopies, operation time, hospital stay, and preoperative and last follow-up scores of the Scoliosis Research Society-22 (SRS-22) questionnaire. RESULTS: A total of 707 pedicle screws were placed in the two groups, with 441 screws in the 3D printing group and 266 screws in the free-hand group. All patients in both groups successfully completed the surgery. There was a statistically significant difference in operation time between the two groups (P<0.05). The screw placement accuracy rate of the 3D printing group was 95.46% (421/441), among which the Grade A placement rate was 89.34% (394/441);the screw placement accuracy rate of the free-hand group was 86.47% (230/266), with a Grade A placement rate of 73.31% (195/266). There were statistically significant differences in the accuracy of Grade A, B, and C screw placements between the two groups (P<0.05), while no statistically significant differences were observed in intraoperative blood loss, number of fluoroscopies, correction rate, or hospital stay (P>0.05). In the SRS-22 questionnaire scores, the scores of functional status and activity ability, self-image, mental status, and pain of patients in each group at the last follow-up were significantly improved compared with those before surgery (P<0.05), but there were no statistically significant differences in all scores between the two groups (P>0.05). CONCLUSION In scoliosis correction surgery, compared with traditional free-hand screw placement, the use of 3D-printed auxiliary guides for screw placement significantly improves the accuracy and safety of screw placement and shortens the operation time.
Humans ; Male ; Scoliosis/surgery* ; Female ; Adolescent ; Printing, Three-Dimensional ; Retrospective Studies ; Pedicle Screws ; Child

Objective To analyze the effect of sodium cantharidinate and vitamin B6 injection(SCV)on four human hepatocellular carcinoma(HCC)cell lines(SMMC-7721,Bel-7402,Huh7,and HepG2)and explore its mechanism.Methods Normal hepatic cell line L02 was treated with SCV at concentrations of 0 μmol/L(control),0.5,1,2,4,8,16,and 32 μmol/L,and the cytotoxicity of SCV on L02 cells was detected using CCK-8 assay.Human HCC cell lines(SMMC-7721,Bel-7402,Huh7,and HepG2)were cultured.SCV-untreated control group(0 μmol/L)and 2,4,and 8 μmol/L SCV-treated groups were set up.CCK-8 assay,plate cloning formation assay,Transwell assay,wound healing assay,and flow cytometry were used to detect the effects of SCV on the growth and proliferation capacity,colony formation ability,invasion and migration capabilities,cell cycle,and apoptosis of the four hepatocellular carcinoma cell lines,respectively.Western blotting was performed to detect the expression levels of apoptosis-related proteins,including nuclear factor kappa-B subunit p65(p65),B-cell lymphoma 2(Bcl-2),and Caspase-3,and to preliminarily explore the underlying mechanism.Results The CCK-8 assay showed that SCV at 0.5,1,2,4,and 8 μmol/L had no significant cytotoxic effect on L02 cells compared with untreated control group,so 2,4,and 8 μmol/L SCV were selected for subsequent experiments.Compared with the untreated control group(0 μmol/L),SCV at different concentrations(2,4,and 8 μmol/L)significantly inhibited the proliferation of the four HCC cell lines(P<0.001).The plate cloning formation assay showed that SCV at different concentrations(2,4,and 8 μmol/L)significantly reduced the colony formation ability of the four HCC cell lines(P<0.05 or P<0.01 or P<0.001).In addition,Transwell and wound healing assays revealed that SCV at different concentrations(2,4,and 8 μmol/L)significantly inhibited the invasion and migration of HCC cells(P<0.05 or P<0.01 or P<0.001).In the above results,the inhibitory effect of SCV was concentration-dependent.Flow cytometry analysis indicated that SCV arrested cells in the G2/M phase(P<0.05 or P<0.01 or P<0.001)and significantly promoted cell apoptosis(P<0.05 or P<0.01 or P<0.001).Western blotting showed that SCV significantly down-regulated the expression of p65(P<0.05 or P<0.01)and Bcl-2(P<0.05),and up-regulated the expression of Caspase-3(P<0.05 or P<0.01).Conclusions SCV can significantly inhibit the proliferation,colony formation,invasion,and migration of multiple human HCC cell lines and arrest the cell cycle.SCV may inhibit the expression of p65 and Bcl-2,thereby lifting their inhibitory effect on the apoptotic pathway and activating Caspase-3 to promote apoptosis.

Background/Aims: Despite the high efficacy of direct-acting antivirals (DAAs), approximately 1–3% of hepatitis C virus (HCV) patients fail to achieve a sustained virological response. We conducted a nationwide study to investigate risk factors associated with DAA treatment failure. Machine-learning algorithms have been applied to discriminate subjects who may fail to respond to DAA therapy. Methods: We analyzed the Taiwan HCV Registry Program database to explore predictors of DAA failure in HCV patients. Fifty-five host and virological features were assessed using multivariate logistic regression, decision tree, random forest, eXtreme Gradient Boosting (XGBoost), and artificial neural network. The primary outcome was undetectable HCV RNA at 12 weeks after the end of treatment. Results: The training (n=23,955) and validation (n=10,346) datasets had similar baseline demographics, with an overall DAA failure rate of 1.6% (n=538). Multivariate logistic regression analysis revealed that liver cirrhosis, hepatocellular carcinoma, poor DAA adherence, and higher hemoglobin A1c were significantly associated with virological failure. XGBoost outperformed the other algorithms and logistic regression models, with an area under the receiver operating characteristic curve of 1.000 in the training dataset and 0.803 in the validation dataset. The top five predictors of treatment failure were HCV RNA, body mass index, α-fetoprotein, platelets, and FIB-4 index. The accuracy, sensitivity, specificity, positive predictive value, and negative predictive value of the XGBoost model (cutoff value=0.5) were 99.5%, 69.7%, 99.9%, 97.4%, and 99.5%, respectively, for the entire dataset. Conclusions Machine learning algorithms effectively provide risk stratification for DAA failure and additional information on the factors associated with DAA failure.

Background/Aims: Chronic hepatitis C (CHC) patients who failed antiviral therapy are at increased risk for hepatocellular carcinoma (HCC). This study assessed the potential role of metformin and statins, medications for diabetes mellitus (DM) and hyperlipidemia (HLP), in reducing HCC risk among these patients. Methods: We included CHC patients from the T-COACH study who failed antiviral therapy. We tracked the onset of HCC 1.5 years post-therapy by linking to Taiwan’s cancer registry data from 2003 to 2019. We accounted for death and liver transplantation as competing risks and employed Gray’s cumulative incidence and Cox subdistribution hazards models to analyze HCC development. Results: Out of 2,779 patients, 480 (17.3%) developed HCC post-therapy. DM patients not using metformin had a 51% increased risk of HCC compared to non-DM patients, while HLP patients on statins had a 50% reduced risk compared to those without HLP. The 5-year HCC incidence was significantly higher for metformin non-users (16.5%) versus non-DM patients (11.3%; adjusted sub-distribution hazard ratio [aSHR]=1.51; P=0.007) and metformin users (3.1%; aSHR=1.59; P=0.022). Statin use in HLP patients correlated with a lower HCC risk (3.8%) compared to non-HLP patients (12.5%; aSHR=0.50; P<0.001). Notably, the increased HCC risk associated with non-use of metformin was primarily seen in non-cirrhotic patients, whereas statins decreased HCC risk in both cirrhotic and non-cirrhotic patients. Conclusions Metformin and statins may have a chemopreventive effect against HCC in CHC patients who failed antiviral therapy. These results support the need for personalized preventive strategies in managing HCC risk.