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.

Patients with Takayasu arteritis combined with aortic valve disease often have a poor prognosis following surgical valve replacement, frequently encountering complications such as perivalvular leakage, valve detachment, and anastomotic aneurysm. This article presents a high-risk case wherein severe aortic valve insufficiency associated with Takayasu arteritis was successfully managed through transcatheter aortic valve implantation via the transapical approach. The patient had satisfactory valve function with no complications observed during the six-month postoperative follow-up. This case provides a minimally invasive and feasible alternative for the clinical management of such high-risk patients.

Aim To explore the mechanism of luteolin in alleviating hepatic fibrosis.Methods C57BL/6 mice were randomly divided into the control group,CCl4 group,silybin group(100 mg·kg-1)and luteo-lin group(100 mg·kg-1).After 10-week modeling and 2-week treatment,the serum levels of aminotrans-ferase and liver histopathology were examined.Hepatic fibrosis and autophagy-related gene expression were as-sessed using immunohistochemistry and immunofluores-cence.Human hepatic stellate cell line(LX2)was cultured and divided into control,TGF-β1(10 mg·L-1),TGF-β1+silybin(40 μmol·L-1),TGF-β1+luteolin(40 μmol·L-1).Fibrotic and autophagy-re-lated markers were analyzed using quantitative real-time PCR,Western blot,immunofluorescence and MDC staining.Results Compared with the CCl4 group,the treatment groups showed significantly improved liver function and reduced hepatic fibrosis,with markedly downregulated COL1A1 and α-SMA expression,and luteolin demonstrated superior efficacy.Compared with TGF-β1 group,luteolin treatment significantly de-creased mRNA levels of COL1A1,ACTA2 and MAP1LC3B,while increasing the mRNA level of SQSTM1,the protein levels of COL1A1 and α-SMA de-creased,p62 was enhanced,the LC3Ⅱ/Ⅰ ratio was downregulated,and autophagy was reduced.These effects of luteolin were reversed by autophagy inducer rapamycin.Conclusion Luteolin alleviates liver fi-brosis by decreasing the autophagy of hepatic stellate cells.

Objective:To explore the prognostic factors of seroma after endoscopic thyroidectomy by breast ap-proach,and construct a nomogram to predict the possibility of cervical seroma.Methods:Data of patients undergoing endoscopic thyroid surgery in Dongguan Tungwah Hospital from January 2022 to May 2024 and Dongguan Songshan Lake Tungwah Hospital from May 2023 to August 2024 were retrospectively analyzed,and 1493 patients meeting the in-clusion criteria were selected.Among them,there were 1048 patients in Dongguan Tungwah Hospital as the training co-hort,1015 patients without seroma group and 33 patients with seroma group.There were 445 patients in Dongguan Songshan Lake Tungwah Hospital as the verification cohort,including 424 patients without seroma and 21 patients with seroma.Multivariate logistic regression analysis was used to obtain relevant independent prognostic factors,and R soft-ware established a nomogram model.Calibration curves,Hosmer-Lemeshow goodness of fit,ROC curves were used to evaluate the calibrability of the nomogram model,and clinical utility was assessed by clinical decision curves.Results:Multivariate logistic regression analysis showed that central lymph node dissection,diabetes,hyperthyroidism,and nod-ule size were independent prognostic factors related to seroma.Based on the prognostic factors,the nomogram of se-roma after ETBA was constructed.The calibration curves of the training and the verification group were in good agree-ment with the observed results,and the Hosmer-Lemeshow goodness of fit test was good,with the training cohort P=0.244 and the verification cohort P=0.803.The ROC curve of the training cohort showed that the area under the curve was 0.810(95%CI:0.740～0.879),and the ROC curve of the verification cohort showed that the area under the curve was 0.815(95%CI:0.722～0.909).Conclusion:The nomogram model based on the relevant prognostic factors ob-tained by multivariate logistic regression analysis has a good prediction effect on the seroma after ETBA,and can provide reasonable and individualized treatment plan for patients.

Objective:To establish a model of reactive Th17 cells proliferation induced by collagen type Ⅱ(C Ⅱ)in vitro and investigate its influencing factors.Methods:The splenic lymphocytes of normal and CIA mice were isolated and divided into groups.They were given inactivated or non-inactivated C Ⅱ or different concentrations of IL-23(2,10,50 ng/mL),or IL-23p19 antibody.Culturing for 60 hours,the ratio of CD4+RORγt+Th17 cells was detected by flow cytometry.Then,the results obtained are ana lyzed,and the corresponding conclusions are drawn.Results:After 60 hours of culture in vitro,the ratio of Th 17 cells stimulated by inactivated or non-inactivated C Ⅱ in normal mouse spleen lymphocytes was significantly lower than that before culture,and the ratio of Th17 cells not stimulated by C Ⅱ in CIA mouse spleen lymphocytes was also significantly lower than that before culture,while the ratio of Th17 cells stimulated by inactivated C Ⅱ or non-inactivated C Ⅱ in CIA mouse spleen lymphocytes was significantly higher than that before culture,and there was a significant difference compared with the CIA control group(P＜0.05).However,there was no statistical difference in the ratio of Th17 cells between the two groups without inactivated C Ⅱ and inactivated C Ⅱ(P=0.44).After the analysis of the data obtained from the study,it was further concluded that different concentrations of IL-23 did not affect the Th17 cell ratio of spleen lymphocytes of CIA mice in vitro,but after adding IL-23p19 antibody neutralization reagent,the Th17 cell ratio of spleen lymphocytes of CIA mice in vitro decreased significantly,with a statistical difference compared with the blank control group(P＜0.01).Conclusions:This study established an in vitro Th17 cell proliferation model induced by type Ⅱ collagen,exploring the effects of IL-23 and collagen activity on Th17 cell proliferation.The results showed that CⅡ stimulation significantly promoted Th17 cell proliferation in CIA mice,with both active and inactivated CⅡ inducing proliferation.IL-23 was found to be essential for the maintenance of Th17 cells,although its direct proliferative effect was limited.These findings provide new experimental evidence and theoretical support for the mechanism research of rheumatic diseases and IL-23/IL-17 pathway-targeted therapies,with important implications for immune regulation and drug development.

Objective:To explore the prognostic factors of seroma after endoscopic thyroidectomy by breast ap-proach,and construct a nomogram to predict the possibility of cervical seroma.Methods:Data of patients undergoing endoscopic thyroid surgery in Dongguan Tungwah Hospital from January 2022 to May 2024 and Dongguan Songshan Lake Tungwah Hospital from May 2023 to August 2024 were retrospectively analyzed,and 1493 patients meeting the in-clusion criteria were selected.Among them,there were 1048 patients in Dongguan Tungwah Hospital as the training co-hort,1015 patients without seroma group and 33 patients with seroma group.There were 445 patients in Dongguan Songshan Lake Tungwah Hospital as the verification cohort,including 424 patients without seroma and 21 patients with seroma.Multivariate logistic regression analysis was used to obtain relevant independent prognostic factors,and R soft-ware established a nomogram model.Calibration curves,Hosmer-Lemeshow goodness of fit,ROC curves were used to evaluate the calibrability of the nomogram model,and clinical utility was assessed by clinical decision curves.Results:Multivariate logistic regression analysis showed that central lymph node dissection,diabetes,hyperthyroidism,and nod-ule size were independent prognostic factors related to seroma.Based on the prognostic factors,the nomogram of se-roma after ETBA was constructed.The calibration curves of the training and the verification group were in good agree-ment with the observed results,and the Hosmer-Lemeshow goodness of fit test was good,with the training cohort P=0.244 and the verification cohort P=0.803.The ROC curve of the training cohort showed that the area under the curve was 0.810(95%CI:0.740～0.879),and the ROC curve of the verification cohort showed that the area under the curve was 0.815(95%CI:0.722～0.909).Conclusion:The nomogram model based on the relevant prognostic factors ob-tained by multivariate logistic regression analysis has a good prediction effect on the seroma after ETBA,and can provide reasonable and individualized treatment plan for patients.

Objective:To establish a model of reactive Th17 cells proliferation induced by collagen type Ⅱ(C Ⅱ)in vitro and investigate its influencing factors.Methods:The splenic lymphocytes of normal and CIA mice were isolated and divided into groups.They were given inactivated or non-inactivated C Ⅱ or different concentrations of IL-23(2,10,50 ng/mL),or IL-23p19 antibody.Culturing for 60 hours,the ratio of CD4+RORγt+Th17 cells was detected by flow cytometry.Then,the results obtained are ana lyzed,and the corresponding conclusions are drawn.Results:After 60 hours of culture in vitro,the ratio of Th 17 cells stimulated by inactivated or non-inactivated C Ⅱ in normal mouse spleen lymphocytes was significantly lower than that before culture,and the ratio of Th17 cells not stimulated by C Ⅱ in CIA mouse spleen lymphocytes was also significantly lower than that before culture,while the ratio of Th17 cells stimulated by inactivated C Ⅱ or non-inactivated C Ⅱ in CIA mouse spleen lymphocytes was significantly higher than that before culture,and there was a significant difference compared with the CIA control group(P＜0.05).However,there was no statistical difference in the ratio of Th17 cells between the two groups without inactivated C Ⅱ and inactivated C Ⅱ(P=0.44).After the analysis of the data obtained from the study,it was further concluded that different concentrations of IL-23 did not affect the Th17 cell ratio of spleen lymphocytes of CIA mice in vitro,but after adding IL-23p19 antibody neutralization reagent,the Th17 cell ratio of spleen lymphocytes of CIA mice in vitro decreased significantly,with a statistical difference compared with the blank control group(P＜0.01).Conclusions:This study established an in vitro Th17 cell proliferation model induced by type Ⅱ collagen,exploring the effects of IL-23 and collagen activity on Th17 cell proliferation.The results showed that CⅡ stimulation significantly promoted Th17 cell proliferation in CIA mice,with both active and inactivated CⅡ inducing proliferation.IL-23 was found to be essential for the maintenance of Th17 cells,although its direct proliferative effect was limited.These findings provide new experimental evidence and theoretical support for the mechanism research of rheumatic diseases and IL-23/IL-17 pathway-targeted therapies,with important implications for immune regulation and drug development.

Relevant China's literature on the application of brain-computer interface technology in the field of rehabilita-tion of stroke patients was retrieved in the China Knowledge Network database from its establishment to December 31,2024,and CiteSpace visual analysis software was used to analyze the selected literature in terms of trend of annual publica-tion number,author collaboration network,keyword co-occurrences and emergences and to generate a corresponding knowledge map.It's pointed out brain-computer interface technology showed significant application potential for motor function recovery and neurorehabilitation,which had the research hotspots of the cross technologies covering motor imagina-tion,rehabilitation training and virtual reality and the research frontiers of the fusion application of intelligent algorithms of deep learning and pattern recognition.The challenges and future development directions of the field were investigated,and references were provided for promoting the application of brain-computer interface technology to rehabilitation of sroke patients in China.[Chinese Medical Equipment Journal,2025,46(9):65-69]

Relevant China's literature on the application of brain-computer interface technology in the field of rehabilita-tion of stroke patients was retrieved in the China Knowledge Network database from its establishment to December 31,2024,and CiteSpace visual analysis software was used to analyze the selected literature in terms of trend of annual publica-tion number,author collaboration network,keyword co-occurrences and emergences and to generate a corresponding knowledge map.It's pointed out brain-computer interface technology showed significant application potential for motor function recovery and neurorehabilitation,which had the research hotspots of the cross technologies covering motor imagina-tion,rehabilitation training and virtual reality and the research frontiers of the fusion application of intelligent algorithms of deep learning and pattern recognition.The challenges and future development directions of the field were investigated,and references were provided for promoting the application of brain-computer interface technology to rehabilitation of sroke patients in China.[Chinese Medical Equipment Journal,2025,46(9):65-69]