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.

Objective This study reports on the obligatory parasitism of water mites Arrenurus sp. on Anopheles messeae at the Manzhouli Port, Inner Mongolia, China. Methods Duing July 2024, a survey on the mosquito diversity was conducted at the Manzhouli Port. Captured mosquitoes and their ectoparasites were identified to species level. Results A total of 1840 adult mosquitoes were collected, representing species from three genera: Culex(Cx. modestus, Cx. pipiens pallens), Aedes(Ae. dorsalis, Ae. flavidorsalis, Ae. flavescens), and Anopheles (An. messeae). Among all the mosqutioes specimens,3 out of 150 captured An. messeae were found to carry ectoparasitic mites, with number of 2,4,27 mites separately. Morphological and molecular identification reached the same result as water mites(Hydrachnidiae, Hydracrina). COI gene sequence showed 94% similarity with the closest species Arrenurus truncatellus. Conlusions Literature review suggests water mites are host-specific parasitism of mosquito species and herein with the first record of Arrenurus sp. parasiting on An. Messeae in the most high-latitude region globally.

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.

The integration of science and education is not only an important strategy for promoting social progress and technological development,but also a modern form of higher education aiming at cultivating innovative talents.Conducting scientific research training for undergraduate medical students is one of the important ways to cultivate their innovative abilities and comprehensive qualities.Our team proposed a"teaching,science,and ideology trinity"teaching model to comprehensively cultivate students' scientific research comprehensive abilities under the value orientation of ideological and political education by or-ganically integrating molecular biology experimental teaching with the scientific research training of under-graduate medical students.In this teaching activity,taking the experiment of gene polymorphism as an example,our team selected students with research potential from the whole grade and divided them into 4 project groups that were instructed by 4 teachers.The students were trained in the whole process of scien-tific research,including topic selection,project writing,experimental designing,application for research ethics,and project summary.Our team has always adhered to student-contentedness of educational con-cepts to stimulate students' intrinsic motivation throughout the teaching process.Students are the design-ers and implementers of the project,and teachers are only guides and promoters of learning.After this training,students not only became familiar with the writing and implementation of scientific research pro-jects,but also improved their literature reading,experimental designing,experimental skills,and prob-lem-solving abilities.More importantly,this teaching activity also cultivated students' awareness of re-search ethics and academic moral standards.

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 evaluate the clinical application value of the half-Fourier acquisition single-shot turbo spin echo(HASTE)sequence based on deep learning(DL)in pancreatic T2WI.Methods Data were collected from 41 patients who un-derwent both BLADE and DL-HASTE sequence scans during pancreatic T2WI at some hospital from February to July 2023.Qualitative assessments were made regarding overall image quality,pancreatic edge sharpness,pancreatic duct edge sharp-ness,pancreatic duct visua-lization(proximal,middle and distal segments)and lesion visibility of BLADE-sequence and DL-HASTE-sequence images.Quantitative assessments were carried out in terms of scan time,signal-to-noise ratio(SNR)and contrast-to-noise ratio(CNR).Statistical analyses were performed using SPSS 24.0.Results DL-HASTE sequence behaved significantly better than BLADE in pancreatic duct edge sharpness,pancreatic duct visualization,lesion visibility,scan time and CNR,while worse in pane-reatic edge sharpness(all P＜0.05).There were significant differences between DL-HASTE and BLADE sequences in overall image quality and SNR(all P＞0.05).Conclusion The DL-HASTE sequence maintains image quality while significantly shor-tening scan time,making it suitable for patients with irregular respiratory rates.[Chinese Medical Equipment Journal,2025,46(3):59-63]

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.