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 To analyze the risk factors,traditional Chinese medicine(TCM)syndromes,and pathogen distribution in patients with bronchiectasis(BE)complicated with type 2 diabetes mellitus(T2DM).Methods From June 2022 to June 2024,a total of 299 patients with acute exacerbation of BE admitted to Guangdong Integrated Chinese and Western Medicine Hospital Affiliated to Guangzhou University of Chinese Medicine were selected.Based on the presence of T2DM,the patients were divided into the BE-T2DM group(74 cases)and the BE-only group(225 cases).Clinical data of the patients were collected,and univariate analysis and multivariate logistic regression models were used to identify risk factors for BE complicated with T2DM.TCM syndromes and pathogen distribution were statistically analyzed.Results(1)Univariate analysis showed that there were statistically significant differences between the two groups in gender,age,body mass index(BMI),hypertension,coronary atherosclerotic heart disease(shortened to coronary heart disease),atherosclerosis,the ratio of forced expiratory volume in one second to forced vital capacity(FEV1/FVC),white blood cell count(WBC),neutrophil count analysis for the risk factors showed that gender,BMI,hypertension,and FEV1/FVC were the independent risk factors for BE complicated with T2DM.(2)In terms of the distribution of TCM syndromes,both groups were mainly characterized by phlegm-heat accumulating in the lung syndrome and phlegm-damp obstructing the lung syndrome,and BE-T2DM group had a higher proportion of phlegm-heat accumulating in the lung syndrome.(3)For the infection of pathogens,BE-T2DM group had a higher infection rate of Haemophilus influenzae,Acinetobacter baumannii,and Klebsiella pneumoniae,while the BE-only group was predominantly infected with Pseudomonas aeruginosa;BE-T2DM group had a significantly higher rate of viral infections,mainly infected with influenza A virus,rhinovirus,and SARS-CoV-2;BE-T2DM group also suffered from fungal infections,usually infected with Candida albicans.Conclusion For BE patients complicated with T2DM,the independent risk factors are gender,BMI,hypertension,and FEV1/FVC;the common TCM syndromes are phlegm-heat accumulating in the lung and phlegm-damp obstructing the lung;pathogen infections are mainly caused by Gram-negative bacteria,viruses,and fungi.

Objective To extract and summarize the clinical registration information of periodontitis registered in the US ClinicalTrials.gov and Chinese Clinical Trial Registry(ChiCTR),and further analyze the registration characteristics of periodontitis clinical trials.Methods The ClinicalTrials.gov and ChiCTR data-bases were searched and compiled for periodontitis clinical registration information from 2000 to December 26,2024,including registration number,country/region of registration,annual number of registered projects,sample size,study type and design,study phase,and trial progress status.Results As of December 26,2024,a total of 520 242 registered clinical trials were retrieved from the ClinicalTrials.gov registry platform,of which 1 542(0.30%)were related to periodontitis.There were 189(12.26%)studies on periodontitis-related pro-jects in Turkey,while a total of 37(2.4%)projects were initiated by researchers in China,which ranked ninth.The Chinese Clinical Trial Register(ChiCTR)had 92 954 registered projects,of which 165 were on pe-riodontitis,and most of them were conducted by well-known affiliated hospitals and stomatology hospitals.The number of registrations in the ClinicalTrials.gov database increased year by year and reached a peak in 2022(146 registrations).Trial designs were focused on interventional and observational studies.ClinicalTri-als.gov study phases were focused on phases 2 and 4,while ChiCTR was clustered at phase 0(pre-registra-tion).Conclusion The number of clinical registrations for periodontitis in China's database is relatively low,and despite a steady upward trend,there is still a gap compared with other countries internationally.Future re-search should focus on how to encourage more oral health related research institutions to register on the plat-form and how to increase the sample size.

(±)-Talapyrones A-F (1-6), six pairs of dimeric polyketide enantiomers featuring unusual 6/6/6 and 6/6/6/5 ring systems, were isolated from the fungus Talaromyces adpressus. Their structures were determined by spectroscopic analysis and HR-ESI-MS data, and their absolute configurations were elucidated using a modified Mosher's method and electronic circular dichroism (ECD) calculations. (±)-Talapyrones A-F (1-6) possess a 6/6/6 tricyclic skeleton, presumably formed through a Michael addition reaction between one molecule of α-pyrone derivative and one molecule of C₈ poly-β-keto chain. In addition, compounds 2/3 and 4/5 are two pairs of C-18 epimers, respectively. Putative biosynthetic pathways of 1-6 were discussed.
Polyketides/isolation & purification* ; Talaromyces/chemistry* ; Stereoisomerism ; Molecular Structure ; Circular Dichroism ; Pyrones/chemistry*

To investigate the impact of preoperative serum follicle-stimulating hormone (FSH) levels on the probability of testicular sperm retrieval, we conducted a study of nonobstructive azoospermic (NOA) men with different testicular volumes (TVs) who underwent microdissection testicular sperm extraction (micro-TESE). A total of 177 NOA patients undergoing micro-TESE for the first time from April 2019 to November 2022 in Shenzhen Zhongshan Obstetrics and Gynecology Hospital (formerly Shenzhen Zhongshan Urology Hospital, Shenzhen, China) were retrospectively reviewed. The subjects were divided into four groups based on average TV quartiles. Serum hormone levels in each TV group were compared between positive and negative sperm retrieval subgroups. Overall sperm retrieval rate was 57.6%. FSH levels (median [interquartile range]) were higher in the positive sperm retrieval subgroup compared with the negative outcome subgroup when average TV was <5 ml (first quartile [Q1: TV <3 ml]: 43.32 [17.92] IU l -1 vs 32.95 [18.56] IU l -1 , P = 0.048; second quartile [Q2: 3 ml ≤ TV <5 ml]: 31.31 [15.37] IU l -1 vs 25.59 [18.40] IU l -1 , P = 0.042). Elevated serum FSH levels were associated with successful micro-TESE sperm retrieval in NOA men whose average TVs were <5 ml (adjusted odds ratio [OR]: 1.06 per unit increase; 95% confidence interval [CI]: 1.01-1.11; P = 0.011). In men with TVs ≥5 ml, larger TVs were associated with lower odds of sperm retrieval (adjusted OR: 0.84 per 1 ml increase; 95% CI: 0.71-0.98; P = 0.029). In conclusion, elevated serum FSH levels were associated with positive sperm retrieval in micro-TESE in NOA men with TVs <5 ml. In men with TV ≥5 ml, increases in average TVs were associated with lower odds of sperm retrieval.
Humans ; Male ; Azoospermia/surgery* ; Sperm Retrieval/statistics & numerical data* ; Adult ; Follicle Stimulating Hormone/blood* ; Retrospective Studies ; Testis/pathology* ; Microdissection ; Organ Size

Addressing the enduring challenge of evaluating traditional Chinese medicines (TCMs), the integrated evidence chain-based effectiveness evaluation of TCMs (Eff-iEC) has emerged. This paper explored its capacity through a demonstration study that evaluated the effectiveness evidence of six commonly used anti-hepatic fibrosis Chinese patent medicines (CPMs), including Biejiajian Pill (BP), Dahuang Zhechong Pill (DZP), Biejia Ruangan Compound (BRC), Fuzheng Huayu Capsule (FHC), Anluo Huaxian Pill (AHP), and Heluo Shugan Capsule (HSC), using both Eff-iEC and the Grading of Recommendations, Assessment, Development, and Evaluation (GRADE) system. The recognition of these CPMs within the TCM academic community was also assessed through their inclusion in relevant medical documents. Results showed that the evidence of BRC and FHC received higher assessments in both Eff-iEC and GRADE system, while the assessments for others varied. Analysis of community recognition revealed that Eff-iEC more accurately reflects the clinical value of these CPMs, exhibiting superior evaluative capabilities. By breaking through the conventional pattern of TCMs effectiveness evaluation, Eff-iEC offers a novel epistemology that better aligns with the clinical realities and reasoning of TCMs, providing a coherent methodology for clinical decision-making, new drug evaluations, and health policy formulation.

Objective:To evaluate the effect of cathepsin B(CTSB)/NOD-like receptor pyrin domain containing 3(NLRP3)pathway on the polarization of macrophages induced by LPS.Methods:The well-growing RAW264.7 mouse mononuclear macrophage lines were cultured in vitro and divided into 3 groups(n=6)according to the random number table method:control group(C group),LPS group(L group)and LPS+CA074-me(CTSB inhibitors)group(B group).C group was cultured normally for 24 h,L group was cultured with LPS concentration of 1 μg/ml medium for 24 h.B group was pretreated with CTSB inhibitor CA074-me 30 μmol/L for 1 h before LPS induction,and co-cultured with LPS concentration of 1 μg/ml medium for 24 h.After 24 hours,the morphological changes of the cells were observed by microscope,the concentrations of IL-1β and IL-18 in the supernatant were determined by ELISA.The ex-pressions of cathepsin B precursor(pro-CTSB),mature cathepsin B(mature-CTSB),NLRP3,apoptosis-related speck protein(ASC)and apoptosis-related speck protein-1(caspase-1)were detected by Western blot.The mRNA expression levels of CD32,inducible ni-tric oxide synthase(iNOS),arginase 1(Arg-1)and CD206 were detected by qRT-PCR.The positive expression rates of M1 macro-phage surface marker CD86 and M2 macrophage surface marker CD206 were detected by flow cytometry.Results:Compared with group C,the morphology of cells in groups L and B became larger and pseudopodia appeared.The concentrations of IL-1β and IL-18 in cell supernatant were increased,the expressions of pro-CTSB,mature-CTSB,NLRP3,ASC and caspase-1 were increased,and the expressions of CD32,iNOS mRNA were up-regulated and the positive rates of CD86 and CD206 were increased(P<0.01).Arg-1 and CD206 mRNA in group B were up-regulated(P<0.01).Compared with group L,the pseudopodia of group B were reduced,and the morphology was closer to group C.The concentration of IL-1β and IL-18 in the supernatant,the expression of mature-CTSB,NLRP3,ASC and caspase-1,CD32 and iNOS mRNA and the positive rate of CD86 were down-regulated in group B.The expression of pro-CTSB,Arg-1 and CD206 mRNA and the positive rate of CD206 were increased(P<0.01).Conclusion:Inhibition of CTSB/NLRP3 pathway can reduce the inflammatory response,reduce the LPS-induced polarization of RAW264.7 cells to M1 macrophages,and pro-mote their polarization to M2 macrophages.

Sepsis is an immune disorder caused by infection,which can lead to multiple organ dysfunction,immune response and immune cells play key role in the onset and progression of sepsis.As an immunoglobulin,triggering receptor expressed on mye-loid cells-1(TREM-1)has immunomodulatory effects and participates in the pathophysiological process of many diseases.TREM-1 can regulate inflammatory mediators and immune cells in sepsis,playing an important role in its diagnosis,treatment and prognosis.This article summarizes the role and possible mechanism of TREM-1 in occurrence and development of sepsis,and provides a theoreti-cal basis for the research direction and treatment strategy of the disease.

Objective:To investigate the effects of psychological state on the setup errors of radiotherapy for breast cancer patients.Methods:A prospective cohort study was conducted. A total of 193 breast cancer patients in Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College from October 2022 to May 2023 were selected. Radiotherapy was performed after fixation with an integrated multi-functional device for the head, chest and abdomen. Psychological status of patients was assessed by using 9-item health questionnaire (PHQ-9) and generalized anxiety disorder 7 self-rating scale (GAD-7) before first radiotherapy, the 10th radiotherapy and the last radiotherapy. Based on the results of the questionnaires, patients were divided into psychological problem (anxiety or depression) group and non-psychological problem group. The general data and setup errors of radiotherapy in both groups were compared.Results:All the 193 patients were female, with a median age of 47 years. There were 53 patients in psychological problem group and they underwent a total of 507 image-guided procedures, with setup errors [ M ( Q1, Q3)] of 0.18 (0.07, 0.33), 0.20 (0.10, 0.33) and 0.19 (0.09, 0.30) in the left-right (X), superior-inferior (Y), and anterior-posterior (Z) directions, respectively; the remaining 140 patients in non-psychological problem group underwent 1 240 image-guided procedures, with setup errors [ M ( Q1, Q3)]of 0.17 (0.08, 0.30), 0.20 (0.10, 0.30) and 0.18 (0.09, 0.28) in the X, Y, and Z directions, respectively, and the differences were statistically significant ( Z values were -3.78, -2.00; P < 0.001, P = 0.046). Conclusions:Anxiety and depression have an influence on the setup errors of radiotherapy in patients with breast cancer. In the processs of radiotherapy for breast cancer, it is important to pay attention to the psychological status of patients.