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 prospective study evaluated the feasibility of chemical exchange saturation transfer (CEST) MRI for assessing disease activity in thyroid-associated ophthalmopathy (TAO). Materials and Methods: A total of 88 patients with active TAO, 76 with inactive TAO, and 30 healthy controls were enrolled. CEST MRI-derived magnetization transfer ratio (MTR) and MTR asymmetry (MTRasym) at 1 ppm, 2 ppm, and 3.5 ppm were calculated. Clinical data, MTR, and MTRasym values for the extraocular muscles (one representative muscle per eye, yielding two measurements per participant) were compared among the groups. Spearman’s correlation was used to examine associations between imaging parameters and the clinical activity score (CAS) in patients with TAO. Logistic regression analysis was used to identify independent associations between imaging parameters and disease activity in patients with TAO (active vs. inactive). Receiver operating characteristic (ROC) analysis was conducted to evaluate the diagnostic performance for discriminating active from inactive TAO. Results: Patients with active TAO showed lower MTR values (P < 0.001) and higher MTRasym (1 ppm), MTRasym (2 ppm), and MTRasym (3.5 ppm) (all P < 0.001) compared with those with inactive TAO. MTR was negatively correlated with CAS (r = -0.402; P < 0.001), while MTRasym (1 ppm), MTRasym (2 ppm), and MTRasym (3.5 ppm) were positively correlated with CAS (r = 0.369, 0.350, and 0.349, respectively;all P < 0.001). MTR and MTRasym (1 ppm) were independently associated with TAO activity. The areas under the ROC curve (AUCs) for MTR and MTRasym (1 ppm) in discriminating active from inactive TAO were 0.772 and 0.730, respectively. Combining MTR with MTRasym (1 ppm) significantly improved diagnostic performance compared with either parameter alone, achieving an AUC of 0.805 (P = 0.029 and 0.001). Conclusion MTR and MTRasym (1 ppm) were independently associated with TAO activity. Their combination further enhanced diagnostic performance in distinguishing active from inactive TAO, suggesting their potential as quantitative imaging biomarkers to guide treatment in patients with TAO.

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

Through reviewing literature on thyroid cancer management strategies, this article discusses the problems of active monitoring strategies, the real implementation status, and the hidden dangers of thermal ablation as a downgrade treatment. It points out that further research is needed to accurately identify low-risk PTMCs and implement active monitoring methods. The improvement in the 5-year survival rate of thyroid cancer is due to standardized diagnosis and treatment. At present, it is still too early to downgrade the diagnosis and treatment of thyroid cancer in China. The World Health Organization recommends the three early principles of early detection, early diagnosis, and early treatment for the treatment of malignant tumors as the fundamental principles of thyroid cancer diagnosis and treatment.

AIM To study the chemical constituents from the sticks and leaves of Croton cascarilloides Raeusch.and their biological activities.METHODS The 95％ethanol extract from the sticks and leaves of C.cascarilloides was isolated and purified by MCI,silica gel,Sephadex LH-20 and semi-preparative HPLC,then the structures of obtained compounds were identified by physicochemical properties and spectral data.LPS-induced NO RAW264.7 cell model induced by LPS was used to evaluate its anti-inflammatory activity in vitro.GES-1 injury model induced by taurocholic acid was used to screen the gastric mucosal protection activity.RESULTS Fourteen compounds were isolated and identified as bullatantriol(1),(-)-boscialin(2),(+)-dehydrovomifoliol(3),3-(hydroxylacetyl)-indole(4),pinoresinol(5),3,7-dimethyl-octa-1,7-diene-3,6-ol(6),(+)-syringaresinol(7),curcasinlignan B(8),cleomiscosin C(9),cleomiscosinD(10),2,6-dimethyl-octa-1,7-dien-3,6-diol(11),vanillin(12),vanillic acid(13),methyl vanillate(14).Compound 4 had certain anti-inflammatory activity,with IC50 values of 73.62 μmol/L.The protective rates of 25 μmol/L compounds 1-4,6,9-12 and 14 on gastric mucosal epithelial cells were 30.07％,34.18％,23.91％,30.92％,17.51％,19.69％,31.76％,22.46％,30.56％and 14.49％,respectively.CONCLUSION Compounds 1-14 are isolated from this plant for the first time.Compound 4 shows anti-inflammatory activity,1-4,6,9-12 and 14 show different degrees of gastric mucosal epithelial cell protective activity.

OBJECTIVE To investigate the relationship between azimuth direction,angular intervals,and the accuracy of azimuthal sound source localization.METHODS Fifteen young subjects with normal hearing were tested using nine azimuth settings.The test results were presented as root mean square error and percentage confusion.RESULTS The confusion rate under high-frequency narrowband noise was significantly higher than that under broadband noise and three-syllable words.In the frontal direction,statistically significant differences were observed between the 20° and 10° intervals,as well as between the 20° and 15° intervals(P<0.05),but no significant difference was found between the 10° and 15° intervals(P>0.05).In the lateral and rear directions,statistically significant differences were found between the 30° and 15° intervals,as well as between the 30° and 20° intervals(P<0.05),but no significant difference was found between the 15° and 20° intervals(P>0.05).Statistically significant differences were observed between the frontal direction and both the lateral and rear directions(P<0.05),but no significant difference was found between the lateral and rear directions(P>0.05).CONCLUSION Using stimuli that contain broader bandwidth cues can more accurately reflect the subject's horizontal localization ability.For source azimuth identification tests using broadband noise and three-syllable words,it is recommended to use a 15° interval in the frontal direction,and a 20° interval in the lateral and rear directions.The frontal and lateral directions can be preferred for testing.

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

This study explores whether the current external quality assessment (EQA) level and acceptable bias for basic semen analysis in China are clinically useful. We collected data of semen EQA from Andrology laboratories in the Hunan Province (China) in 2022 and searched for data in the published literature from January 2000 to December 2023 in China. On the basis of these data, we analyzed the coefficients of variation and acceptable biases of different quality control materials for basic semen analysis through robust statistics. We compared these findings with quality specifications based on biological variation from optimal, desirable, and minimum levels of bias to seek a unified and more suitable semen EQA bias evaluation standard for China's national conditions. Different sources of semen quality control material exhibited considerable variation in acceptable biases among laboratories, ranging from 8.2% to 56.9%. A total of 50.0% of the laboratories met the minimum quality specifications for progressive motility (PR), whereas 100.0% and 75.0% of laboratories met only the minimum quality specifications for sperm concentration and total motility (nonprogressive [NP] + PR), respectively. The Z value for sperm concentration and PR+NP was equivalent to the desirable performance specification, whereas the Z value for PR was equivalent only to the minimum performance specification. This study highlights the feasibility of operating external quality assessment schemes for basic semen analysis using quality specifications based on biological variation. These specifications should be unified among external quality control (EQC) centers based on biological variation.
Semen Analysis/standards* ; Humans ; China ; Male ; Quality Control ; Sperm Motility ; Sperm Count/standards*