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.

Objective:To investigate the relationship between serum soluble interleukin-2 receptor(sCD25)and insulin-like growth factor-Ⅰ(IGF-Ⅰ)and the immunophenotype and therapeutic efficacy of newly diagnosed multiple myeloma(MM)patients.Methods:125 newly diagnosed MM patients(MM group)who received treatment at Tianmen First People's Hospital from January 2023 to June 2024 were selected,and another 70 healthy individuals who underwent physical examinations at our hospital during the same period were selected(control group).The serum sCD25 and IGF-Ⅰ levels in newly diagnosed MM patients of different stages were compared,and newly diagnosed MM patients were divide into remission group(76 cases)and non remission group(49 cases)based on treatment efficacy,the serum sCD25 and IGF-Ⅰ levels between the remission group and non remission group were compared.The patients were divided into high sCD25 group and low sCD25 group,high IGF-Ⅰ group and low IGF-Ⅰgroup according to the median levels of serum sCD25 and IGF-Ⅰ,the immunophenotypic differences between high sCD25 group and low sCD25 group,as well as high IGF-Ⅰ group and low IGF-Ⅰ group were analyzed.Serum sCD25 and IGF-Ⅰ for evaluating the efficacy of newly diagnosed MM patients were analyzed by receiver operating characteristic(ROC)curve.Factors affecting the therapeutic effect of newly diagnosed MM patients were analyzed by multivariate logistic regression analysis.Results:Serum sCD25 and IGF-Ⅰ levels in the control group were significantly lower than those in the MM group(P＜0.05).There was a statistically significant difference in serum sCD25 and IGF-Ⅰ levels among newly diagnosed MM patients at different stages(P＜0.05).Serum sCD25 and IGF-Ⅰ levels in stage Ⅲ newly diagnosed MM patients were significantly higher than those in stage Ⅰ and Ⅱ(P＜0.05),And stage Ⅱ was higher than that in stage Ⅰ(P＜0.05).The positive expression rate of CD56 in the high sCD25 group was higher than that in the low sCD25 group,there was no significant difference in the positive expression rates of CD117 and CD200 between the two groups(P＞0.05).The positive expression rates of CD56 and CD117 in the high IGF-Ⅰ group were higher than those in the low IGF-Ⅰ group(P＜0.05),and there was no significant difference in the positive expression rate of CD200 between the two groups(P＞0.05).Serum sCD25 and IGF-Ⅰ levels in the remission group were significantly lower than those in the non remission group(P＜0.05).ROC curve analysis showed that,the area under the curve(AUC)for evaluating the efficacy of newly diagnosed MM patients using serum sCD25 and IGF-Ⅰ detection alone and in combination were 0.748,0.775 and 0.832,respectively,and the AUC for combined detection was greater than that for each indicator detected separately.The results of multivariate Logistic regression model showed that elevated serum sCD25 level,elevated serum IGF-Ⅰ level and MM stage Ⅲ were independent risk factors affecting the efficacy of newly diagnosed MM patients(P＜0.05).Conclusion:Serum sCD25 and IGF-Ⅰ levels are closely related to the disease stage and therapeutic efficacy of newly diagnosed MM patients.Combined detection has a high evaluation value for efficacy and can be used as an important evaluation index affecting efficacy.

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.

Objective To analyze the serum metabolomic changes of preterm infants with bronchopulmonary dysplasia(BPD)at postmenstrual age(PMA)36 weeks,screen potential biomarkers and associated metabolic pathways,and assess their relationship with short-term respiratory outcomes.Methods A retrospective case-control study was conducted.Infants with gestational age 28-32 weeks admitted to the Children's Hospital Affiliated to Zhengzhou University from January to December 2024 were included.Twenty infants with BPD and 20 gestational age-,birth weight-,and sex-matched non-BPD preterm infants were included.Serum collected at PMA 36 weeks was subjected to untargeted metabolomics analysis,and associations with short-term respiratory outcomes were analyzed.Results Thirteen potential biomarkers distinguishing BPD were identified(area under the curve>0.75,P<0.05).Eight biomarkers—including terephthalic acid,phosphatidylinositol,fumarate,and lysophosphatidic acid—were significantly upregulated(FC≥1.5),while five biomarkers,such as 7α-hydroxy-3-oxo-4-cholestenoate ester and phosphatidylcholine,were significantly downregulated(FC≤1/1.5).Pathway analysis indicated five pathways associated with BPD,including glycerophospholipid metabolism and phenylalanine metabolism.Dysregulation of glycerophospholipid and bile acid metabolism may affect adverse short-term respiratory outcomes in infants with BPD.Conclusions The 13 significantly different metabolites may serve as biomarkers for the diagnosis of BPD.Glycerophospholipid metabolism is associated with the occurrence of BPD and with adverse short-term respiratory outcomes.

Objective To analyze the serum metabolomic changes of preterm infants with bronchopulmonary dysplasia(BPD)at postmenstrual age(PMA)36 weeks,screen potential biomarkers and associated metabolic pathways,and assess their relationship with short-term respiratory outcomes.Methods A retrospective case-control study was conducted.Infants with gestational age 28-32 weeks admitted to the Children's Hospital Affiliated to Zhengzhou University from January to December 2024 were included.Twenty infants with BPD and 20 gestational age-,birth weight-,and sex-matched non-BPD preterm infants were included.Serum collected at PMA 36 weeks was subjected to untargeted metabolomics analysis,and associations with short-term respiratory outcomes were analyzed.Results Thirteen potential biomarkers distinguishing BPD were identified(area under the curve>0.75,P<0.05).Eight biomarkers—including terephthalic acid,phosphatidylinositol,fumarate,and lysophosphatidic acid—were significantly upregulated(FC≥1.5),while five biomarkers,such as 7α-hydroxy-3-oxo-4-cholestenoate ester and phosphatidylcholine,were significantly downregulated(FC≤1/1.5).Pathway analysis indicated five pathways associated with BPD,including glycerophospholipid metabolism and phenylalanine metabolism.Dysregulation of glycerophospholipid and bile acid metabolism may affect adverse short-term respiratory outcomes in infants with BPD.Conclusions The 13 significantly different metabolites may serve as biomarkers for the diagnosis of BPD.Glycerophospholipid metabolism is associated with the occurrence of BPD and with adverse short-term respiratory outcomes.

Objective:To explore medical students'cognition,current status,and demands regarding practical skills training in a medical college under the background of new medical science.Methods:A cross-sectional survey was conducted among medical undergraduates from Year 1 to Year 5 in a medical college.A self-designed scale was used to investigate the cognition,current status,and demands related to new medical education concepts.Pearson correlation analysis was used to analyze the relationship between satisfaction with practical teaching facilities and variables including understanding of new medical education,curriculum design,and proportion of practical teaching.Multiple linear regression was conducted to investigate the factors influencing satisfaction.Results:Among 1 253 participants,70.71%acquired new medical science information through online media,while 52.99%learned about it via school courses.Over 80%endorsed"whole-cycle health management"(preventive care before disease onset 91.94%,disease treatment 81.72%,and post-illness rehabilitation 82.60%).Experimental courses(85.16%),clinical skills training(66.88%),and social practice(66.24%)were primary practical forms,but the participation rates of research practice(40.78%)and innovation/entrepreneurship practice(34.72%)was comparatively lower.The satisfaction with practical teaching was positively correlated with adequacy of faculty guidance(r=0.707)and curriculum rationality(r=0.522)(P＜0.01).The multiple linear regression analysis showed that faculty guidance(β=0.436)and the proportion of practical teaching(β=0.319)were the key predictors of satisfaction.Conclusion:Medical students'cognition of practical skills training,curriculum optimization,and adequacy of faculty guidance significantly influence satisfaction with practical teaching,with faculty guidance and rationality of practical process playing key roles.

Objective To explore the correlation between amide proton transfer weighted(APTw)imaging of the brain and the clinical psychological scale assessment in patients with Alzheimer's disease(AD).Methods A total of 30 AD patients(AD group)and 33 gender-and age-matched healthy volunteers(control group)were selected and all patients underwent brain MRI,magnetic resonance angiography(MR A)and APTw imaging examinations.According to the APTw images,the magnetization transfer ratio asym-metry(MTRasym)at 3.5 ppm values of each brain region were measured.The differences in MTRasym(3.5 ppm)values of each brain region between the AD group and the control group were compared,and then the diagnostic efficacy of MTRasym(3.5 ppm)values with significant differences were further analyzed.The correlations between the MTRasym(3.5 ppm)values of each brain region and scores of the mini-mental state examination(MMSE)and Montreal cognitive assessment(MoCA)were analyzed.Results Compared with the control group,the MTRasym(3.5 ppm)values of the left hippocampal head in the AD group were significantly increased(P＜0.05).The area under the curve(AUC)of the receiver operating characteristic(ROC)curve of MTRasym(3.5 ppm)values of the left hippocampal head was 0.656(P＜0.05).MTRasym(3.5 ppm)values of the left hippocampal head,right temporal white matter,and bilateral occipital white matter were significantly negatively correlated with MoCA score(P＜0.05).Conclusion APTw imaging can effectively reflect the changes of protein concentration of the brain regions in AD patients,and is associated with cognitive func-tion,providing a new approach and method for early non-invasive diagnosis and disease monitoring of AD.

Objective:To explore medical students'cognition,current status,and demands regarding practical skills training in a medical college under the background of new medical science.Methods:A cross-sectional survey was conducted among medical undergraduates from Year 1 to Year 5 in a medical college.A self-designed scale was used to investigate the cognition,current status,and demands related to new medical education concepts.Pearson correlation analysis was used to analyze the relationship between satisfaction with practical teaching facilities and variables including understanding of new medical education,curriculum design,and proportion of practical teaching.Multiple linear regression was conducted to investigate the factors influencing satisfaction.Results:Among 1 253 participants,70.71%acquired new medical science information through online media,while 52.99%learned about it via school courses.Over 80%endorsed"whole-cycle health management"(preventive care before disease onset 91.94%,disease treatment 81.72%,and post-illness rehabilitation 82.60%).Experimental courses(85.16%),clinical skills training(66.88%),and social practice(66.24%)were primary practical forms,but the participation rates of research practice(40.78%)and innovation/entrepreneurship practice(34.72%)was comparatively lower.The satisfaction with practical teaching was positively correlated with adequacy of faculty guidance(r=0.707)and curriculum rationality(r=0.522)(P＜0.01).The multiple linear regression analysis showed that faculty guidance(β=0.436)and the proportion of practical teaching(β=0.319)were the key predictors of satisfaction.Conclusion:Medical students'cognition of practical skills training,curriculum optimization,and adequacy of faculty guidance significantly influence satisfaction with practical teaching,with faculty guidance and rationality of practical process playing key roles.