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.

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.

Aim To explore the underlying molecular mechanism of Alisol A 24-acetate(24A)in improving oxygen-glucose deprivation/reoxygenation(OGD/R)injury in brain microvascular endothelial cells(BMECs)and its correlation with miR-98-5p/transi-ent receptor potential melastatin-2(TRPM2).Meth-ods The ischemia-reperfusion injury in brain micro-vascular endothelial cells(BMECs)was established u-sing bEnd.3 cells subjected to 8 h of oxygen-glucose deprivation followed by 16 h of re-oxygenation.The cells were intervened by miR-98-5p mimics and/or 18.77 μmol·L-1 24A for 24 h and divided into the control group,OGD/R group,OGD/R+24A group,OGD/R+24A+miR-98-5p mimics group and OGD/R+miR-98-5p mimics group.The mRNA levels of miR-98-5p and TRPM2 were detected by qPCR.IL-1 β and TNF-α levels were detected by ELISA.The expression levels of TRPM2,p-AKT,p-GSK3 β,AKT,GSK3 β,Bcl-2,Bax,ZO-1,Occludin,Claudin-5 were detected by Western blot.Apoptosis and reactive oxygen species(ROS)levels were detected by flow cytometry.The targeting relationship between miR-98-5p and TRPM2 was verified using dual luciferase assay.Results Compared with the control group,the apoptosis of OGD/R group was obvious,Bcl-2/Bax decreased,ZO-1,Occludin,Claudin-5 decreased,IL-1 β,TNF-α and ROS increased,miR-98-5p,p-AKT/AKT,p-GSK3β/GSK3β decreased but TRPM2 increased.But com-pared with the OGD/R group,except the control group,the other three groups showed the opposite trend in the above aspects;compared with the OGD/R+24A group,OGD/R+24A+miR-98-5p mimics group showed decreased apoptosis,decreased degradation of ZO-1,Occludin and Claudin-5,and decreased inflam-mation and ROS.miR-98-5p,p-AKT/AKT,p-GSK3β/GSK3β increased and TRPM2 decreased.However,compared with the OGD/R+24A+miR-98-5p mimics group,the OGD/R+miR-98-5p mimics group reversed this trend.Dual luciferase confirmed that miR-98-5p targeted regulation of TRPM2.Conclusion 24A in-hibits the expression of TRPM2 in BMECs through miR-98-5p,regulates AKT/GSK3β signal pathway,re-duces OGD/R inflammation and oxidative stress-medi-ated apoptosis,prevents the degradation of ZO-1,Oc-cludin and Claudin-5,and improves BBB permeability.

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.

Aim To investigate the intervention effect of Rehmannia radix water extract on bleomycin(BLM)-induced pulmonary fibrosis in mice combined with metabolomics and to reveal the potential mechanism,in order to provide new ideas for clinical treatment of pul-monary fibrosis.Methods Male C57BL/6N mice were randomly divided into the control group,model group,pirfenidone group(positive control,PFD,270 mg·kg-1),and low dose(DH-L,4.55 g·kg-1)group,medium dose(DH-M,9.1 g·kg-1)group and high dose(DH-H,18.2 g·kg-1)group of Rehman-nia.Except for the control group,BLM(5 mg·kg-1)was instilled into the trachea to establish the model of pulmonary fibrosis in the other groups.The survival rate,lung index and blood oxygen saturation of mice in each group were evaluated.HE and Masson staining were used to observe the pathological changes of lung tissue.WBP was used to detect lung function.Flow cytometry was used to detect the apoptosis of primary lung cells,ROS and immune cells.ELISA was used to detect the levels of fibrosis markers and inflammatory factors(α-SMA,collagen Ⅰ,collagen Ⅲ,TGF-β1,TNF-α,IL-1 β,and IL-6).Biochemical method was employed to detect the contents of GSH-Px,T-SOD and MDA.Liquid chromatograph mass spectrometer(LC-MS)metabolomics was used to analyze the changes of serum metabolic profile.Results Water extract of Re-hmannia significantly increased the survival rate,oxy-gen saturation and lung function of mice with pulmona-ry fibrosis,reduced the lung coefficient,ameliorated pathological damage and collagen deposition in lung tissue,reduced the levels of apoptosis and oxidative stress,and down-regulated the levels of inflammatory factors in lung tissue.It regulated the levels of metabo-lites such as bile acid metabolism,sphingolipid metabo-lism,and unsaturated fatty acid metabolism.Conclu-sions Water extract of Rehmannia inhibits lung injury and collagen deposition in mice with pulmonary fibrosis by inhibiting inflammatory response,which may be a-chieved by regulating the levels of inflammatory factors through the metabolic pathways of bile acid and sphin-golipid.

Aim To investigate the role of mesence-phalic astrocyte-derived neurotrophic factor(MANF)in the inhibitory effect of rhynchophylline(Rhy)on the malignant biological behaviors of gastric cancer cells and its underlying regulatory mechanisms.Meth-ods SGC-7901 gastric cancer cells were transfected using adenovirus and liposome transfection techniques.The experimental groups included:Control group,Rhy group,Rhy+NC group(Rhy+adenovirus-transfected MANF-irrelevant fragment),Rhy+si-MANF group(Rhy+adenovirus-transfected MANF siRNA),Vec-tor group(empty vector),OVE-MANF group(recom-binant plasmid overexpressing MANF).After 24 hours of intervention,cell proliferation,apoptosis,migra-tion,and invasion were assessed using the MTT assay,Hoechst staining,and Transwell assays,respectively.The expressions of MANF,Cyclin D1,and cleaved caspase-3 proteins were measured using Western blot.NF-κB transcriptional activity was evaluated via a lucif-erase reporter assay.Results Compared to the control group,Rhy treatment significantly inhibited gastric cancer cell growth in a dose-dependent manner(P＜0.05),induced typical apoptotic morphological chan-ges,and increased the expression of MANF and cleaved caspase-3 proteins(P＜0.05),while reduc-ing Cyclin D1 protein expression and NF-κB transcrip-tional activity(P＜0.05).Additionally,Rhy treat-ment markedly decreased cell migration and invasion capabilities(P＜0.05).In comparison to the Rhy group,adenovirus-mediated transfection of MANF siR-NA suppressed apoptosis,promoted gastric cancer cell proliferation,migration,and invasion,inhibited MANF and cleaved caspase-3 expression(P＜0.05),and enhanced Cyclin D1 protein levels and NF-κB transcriptional activity(P＜0.05).Compared to the Vector group,OVE-MANF(overexpression of MANF)induced apoptosis,suppressed proliferation,invasion,and metastasis of gastric cancer cells,upregulated MANF and cleaved caspase-3 expression(P＜0.05),and inhibited Cyclin D1 protein levels and NF-κB tran-scriptional activity(P＜0.05).Conclusion Rhy in-hibits the proliferation,migration,and invasion of gas-tric cancer cells and induces apoptosis,with its mecha-nism linked to the promotion of MANF expression and suppression of NF-κB transcriptional activity.

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]

This study was aimed at preliminarily assessing the prevalence of latent tuberculosis infection(LTBI)among cattle farming and slaughterhouse workers across Wuxue,Xianning,and Yichang Cities in Hubei Province,and exploring associated risk factors.Data on cattle farming and slaughterhouse workers were gathered via a questionnaire.LTBI detection was performed with a tu-berculin skin test and interferon-gamma release assay,and influencing factors were subsequently analyzed.The LTBI prevalence among cattle farming and slaughterhouse personnel in the three cities was 30.50%,and a higher rate was observed in slaughterhouse workers(39.01%)than cattle farmers(21.63%)(P<0.01).Multifactor analysis indicated that working in slaughterhouses(95%CI:1.582-3.878),having a history of tuberculosis(95%CI:1.377-25.057)or BCG vaccination(95%CI:1.229-3.285),and having a college education or above(95%CI:0.303-0.859)were significant factors influencing LTBI positivity in these personnel.Having more than 30 years of work experience(95%CI:1.303-18.782)was a risk factor for personnel at cattle breeding farms.Among slaugh-terhouse personnel,having a college education or above(95%CI:0.164-0.894),11-20 years of work experience(95%CI:0.122-0.994),or a history of tuberculosis(95%CI:1.661-64.397);performing logistics work(95%CI:3.234-126.424);and working in slaughter-related positions(95%CI:1.209-19.639)were associated with LTBI positivity.Therefore,the slaughterhouse workers in the three cities had higher LTBI rates than the cattle farming workers,thus underscoring the need for increased attention to personnel in logistics and slaughter-related positions.

China has become the country with the highest global burden of heart failure(HF).Despite the widespread use of prognostic-improving medications today,the mortality rate of HF remains high,reaching 13.7％at one year-particularly among patients with heart failure with reduced ejection fraction(HFrEF).HF interventional device therapy(structural intervention)targets the structural factors underlying HF,including atrial pressure,ventricular remodeling,and valvular intervention.It leverages the heart's intrinsic physiological properties and pathological progression mechanisms to deliver treatments through interventions without external active forces,achieving anatomical or functional repair.This field has emerged as a rapidly growing area and plays an increasingly critical role in HF management.This article provides a comprehensive review and summary of the latest advancements in HF and cardiomyopathy interventional therapy over the past year.It covers various novel technologies and products currently in the research phase,aiming to provide an in-depth analysis of the current status and future directions of HF interventional therapy,and further advance the development of this discipline.