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.

Background: Adrenocortical hormones, particularly 11-oxygenated androgens, are pivotal in female reproductive health and fertility. Standardized detection kits and population-specific reference intervals are lacking in China, hindering related clinical applications. Methods: A HPLC-tandem mass spectrometry (HPLC-MS/MS) pipeline was developed, rigorously validated, and applied to simultaneously quantify corticosterone, cortisone, cortisol, 18-OH cortisol, androstenedione (A4), 11β-hydroxyandrostenedione (11-OH A4), dehydroepiandrosterone, and dehydroepiandrosterone sulfate in serum samples from 455 reproductive-aged women (18–45 yrs) in Guangxi, China. Age-dependent concentration trends were analyzed, and reference intervals stratified by age (2.5th to 97.5th percentiles) were established. Correlations with body-composition metrics, ethnicity, and the menstrual cycle were investigated. Results: The HPLC-MS/MS method demonstrated high precision (intra- and inter-assay CVs < 15%), accuracy, and sensitivity. All eight hormones exhibited significant age-related declines (P < 0.001 for seven hormones; P = 0.001 for 11-OH A4). Notably, 11-OH A4 levels were significantly lower in the 35–45-yr (3.05 nmol/L) and 25–34-yr (3.09 nmol/L) age groups than in the 18–24-yr (3.57 nmol/L) age group, whereas no significant difference was observed between the 35–45-yr and 25–34-yr age groups. Weak negative correlations were observed between the body mass index and corticosterone and cortisone levels, whereas ethnicity and the menstrual cycle showed no significant associations with hormone levels. Conclusions We developed an HPLC-MS/MS-based method for simultaneously quantifying eight adrenocortical hormones, including 11-OH A4, and defined age-specific reference intervals for reproductive-aged Chinese women. These findings advance the clinical utility of adrenocortical hormones in diagnosing and managing reproductive disorders.

Objective To investigate the epidemiological characteristics of malaria and provide a basis for developing improved prevention and control measures. Methods Data were obtained from the Chinese Disease Prevention and Control Information System. Malaria surveillance data for Nanning City from January 1,2023, to December 31,2024, were exported from the Infectious Disease Reporting Information Management Subsystem. The characteristics of the two groups of malaria cases were compared. Results A total of 103 imported malaria cases were reported in Nanning City in 2024, representing a 38.32% decrease compared with the same period of the previous year. No statistically significant difference were observed between cases reported in 2023 and 2024 in terms of average age, gender ratio, proportion of parasite species, and monthly reporting distribution;however, statistically significant differences were found in the proportion of reporting areas and current residence areas(χ2= 13.572 and 10.355, respectively; P = 0.001 and 0.035, respectively). The proportion of cases reported in Shanglin County and the proportion of cases residing in Shanglin County were both lower than those during the same period of the previous year. Conclusions The high aggregation of imported malaria cases in Nanning City has decreased. Medical institutions in areas other than Shanglin County should strengthen their vigilance against malaria.

OBJECTIVE To explore the mechanism of the active ingredient compatibility（quercetin， quercitrin and kaempferol at a mass ratio of 2∶9∶3）of Dimocarpus longan Lour. leaves（abbreviated as CDL） on ameliorating glucose and lipid metabolism disorders in type 2 diabetes mellitus （T2DM） rats. METHODS SD rats were randomly divided into blank control group， model group， metformin hydrochloride group （100 mg/kg）， and CDL high-， medium- and low-dose groups （280， 140， 75 mg/kg）， with 10 rats in each group. Rats in the blank control group were fed with standard chow， while rats in the other groups were given high-sugar and high-fat diet combined with intraperitoneal injection of streptozotocin to establish the T2DM rat model. After successful modeling， rats in each administration group were given corresponding drug solution， and rats in the blank control group and model group were intragastrically administered with equal volume of pure water， once a day， for consecutive 4 weeks. Fasting blood glucose （FBG） was detected at fixed time every week. The curves of oral glucose tolerance test （OGTT） and intraperitoneal insulin tolerance test （IPITT） were plotted， and the area under curve （AUC） was calculated. The pancreatic islet function indexes [fasting insulin （FINS）， homeostasis model assessment of insulin resistance （HOMA-IR）， insulin sensitivity index （ISI）]，blood lipid indexes [total cholesterol （TC）， triglyceride （TG）， low-density lipoprotein cholesterol （LDL-C）， high-density lipoprotein cholesterol （HDL-C）] and hepatic glycogen content were determined. The pathological morphological changes of liver and pancreatic tissues were observed. The protein and mRNA expression levels of molecules related to phosphatidylinositol 3-kinase （PI3K）/protein kinase B （Akt） signaling pathway in liver tissues were detected. RESULTS Compared with the blank control group， the FBG， AUC of IPITT curve， AUC of OGTT curve， HOMA-IR， the levels of FINS， TC， TG and LDL-C， as well as the protein and mRNA expression of phosphatase and tensin homolog， forkhead box protein O1 and glycogen synthase kinase-3β in liver tissues were significantly increased in the model group （ P ＜0.05）. ISI， the levels of HDL-C and hepatic glycogen content， along with the protein and mRNA expression of PI3K， insulin receptor substrate-1， Akt and protein expression of phosphorylated Akt in liver tissues were markedly decreased （ P ＜0.05）. In model group rats， the arrangement of hepatocytes was irregular， the overall structure of pancreatic lobules was disordered， and a large number of inflammatory cell infiltration was observed. Compared with the model group， most of the above quantitative indexes were significantly reversed in the CDL high-dose group （ P ＜0.05）， and the pathological lesions of liver and pancreas were obviously alleviated. CONCLUSIONS CDL can regulate glucose and lipid metabolism disorders， elevate insulin sensitivity and relieve insulin resistance in T2DM rats. Its mechanism may be related to the activation of the PI3K/Akt signaling pathway.

Objective To explore the correlation and lag effect between meteorological factors and the incidence of scarlet fever in Xicheng District, Beijing, and to provide a theoretical basis for the surveillance, early warning，and scientific prevention and control of scarlet fever. Methods Based on the daily scarlet fever incidence data and concurrent meteorological data in Xicheng District, Beijing from 2010 to 2019, the distributed lag non-linear model (DLNM) was used to analyze the impact of meteorological factors on the incidence of scarlet fever. Results The risk of scarlet fever was the highest when the daily average temperature was 34.2℃ with a lag of 0 days (RR=1.175, 95% CI:1.006-1.372). The risk was the second highest when the daily average temperature was 2.2℃ with a lag of 12 days (RR=1.123, 95% CI:1.044 -1.209). The cumulative relative risk of scarlet fever was statistically significant when the daily average temperature ranged from 27.2℃ to 34.2℃, with the highest cumulative relative risk at 34.2℃ (RR=1.906, 95%CI:1.215-2.989). When the daily average relative humidity was 19.5% with a lag of 6 days, the risk of scarlet fever incidence was the highest (RR=1.022, 95% CI: 1.005-1.040). The cumulative relative risk was statistically significant when the daily average relative humidity ranged from 24.7% to 40.2%, with the highest cumulative relative risk at 27.3% (RR=1.170, 95% CI: 1.020-1.343). The risk of scarlet fever was the highest when the daily average vapor pressure was 1.2 hPa with a lag of 5 days (RR=1.029, 95%CI:1.008-1.050). The cumulative relative risk of scarlet fever was statistically significant when the daily average vapor pressure was 1.2-7.4 hPa, and the highest cumulative risk was when the daily average vapor pressure was 1.2 hPa (RR=1.362, 95% CI:1.022-1.815). Conclusion There is a nonlinear relationship between meteorological factors and the incidence of scarlet fever in Xicheng District, Beijing, with a certain lag effect. Daily average temperature (27.2-34.2℃), daily average relative humidity (28.6~36.3%) and daily average vapor pressure (1.2-7.4 hPa) increase the risk of scarlet fever. These factors can be used as indicators for the prevention, control, surveillance, and early warning of scarlet fever.

OBJECTIVE To explore the mechanism of the active ingredient compatibility（quercetin， quercitrin and kaempferol at a mass ratio of 2∶9∶3）of Dimocarpus longan Lour. leaves（abbreviated as CDL） on ameliorating glucose and lipid metabolism disorders in type 2 diabetes mellitus （T2DM） rats. METHODS SD rats were randomly divided into blank control group， model group， metformin hydrochloride group （100 mg/kg）， and CDL high-， medium- and low-dose groups （280， 140， 75 mg/kg）， with 10 rats in each group. Rats in the blank control group were fed with standard chow， while rats in the other groups were given high-sugar and high-fat diet combined with intraperitoneal injection of streptozotocin to establish the T2DM rat model. After successful modeling， rats in each administration group were given corresponding drug solution， and rats in the blank control group and model group were intragastrically administered with equal volume of pure water， once a day， for consecutive 4 weeks. Fasting blood glucose （FBG） was detected at fixed time every week. The curves of oral glucose tolerance test （OGTT） and intraperitoneal insulin tolerance test （IPITT） were plotted， and the area under curve （AUC） was calculated. The pancreatic islet function indexes [fasting insulin （FINS）， homeostasis model assessment of insulin resistance （HOMA-IR）， insulin sensitivity index （ISI）]，blood lipid indexes [total cholesterol （TC）， triglyceride （TG）， low-density lipoprotein cholesterol （LDL-C）， high-density lipoprotein cholesterol （HDL-C）] and hepatic glycogen content were determined. The pathological morphological changes of liver and pancreatic tissues were observed. The protein and mRNA expression levels of molecules related to phosphatidylinositol 3-kinase （PI3K）/protein kinase B （Akt） signaling pathway in liver tissues were detected. RESULTS Compared with the blank control group， the FBG， AUC of IPITT curve， AUC of OGTT curve， HOMA-IR， the levels of FINS， TC， TG and LDL-C， as well as the protein and mRNA expression of phosphatase and tensin homolog， forkhead box protein O1 and glycogen synthase kinase-3β in liver tissues were significantly increased in the model group （ P ＜0.05）. ISI， the levels of HDL-C and hepatic glycogen content， along with the protein and mRNA expression of PI3K， insulin receptor substrate-1， Akt and protein expression of phosphorylated Akt in liver tissues were markedly decreased （ P ＜0.05）. In model group rats， the arrangement of hepatocytes was irregular， the overall structure of pancreatic lobules was disordered， and a large number of inflammatory cell infiltration was observed. Compared with the model group， most of the above quantitative indexes were significantly reversed in the CDL high-dose group （ P ＜0.05）， and the pathological lesions of liver and pancreas were obviously alleviated. CONCLUSIONS CDL can regulate glucose and lipid metabolism disorders， elevate insulin sensitivity and relieve insulin resistance in T2DM rats. Its mechanism may be related to the activation of the PI3K/Akt signaling pathway.

Against the backdrop of the "New Medical Education" initiative by the Ministry of Education, higher medical education faces an urgent need to reconstruct its talent cultivation system. Taking the Laboratory Animal Science course at Shandong University of Traditional Chinese Medicine as an example, this paper systematically explores how to construct a teaching system featuring deep integration of scientific research and education, aiming to address the disconnection between theory and practice and the lack of innovative thinking among students in traditional teaching. The construction of this system is based on three pillars: first, the establishment of a high-level, interdisciplinary faculty team that strongly supports teaching innovation; second, reliance on the Laboratory Animal Center of Shandong University of Traditional Chinese Medicine and the Shandong Provincial Engineering Research Center for Prevention and Treatment of Major Brain Diseases with Traditional Chinese Medicine to provide the necessary facilities; third, a systematic restructuring of teaching content and modes through the "multidisciplinary, multi-format, and multilevel" integration approach. The reform has yielded remarkable results, particularly evident in the increase in students' scientific research and innovation capabilities: the proportion of student projects approved at the provincial level or above in undergraduate innovation training programs significantly increased from 11.1% before the reform to 41.6%. Furthermore, student teams have won gold and silver awards in national competitions such as the "National Undergraduate Innovation Forum for Basic Medical Sciences and Experimental Design" and the "National College Students' Medical Innovation Competition". Additionally, the teaching team's faculty members serving as chief editors and editorial board members of the national planning textbook Laboratory Animal Science published by People's Medical Publishing House mark the nationwide recognition of the course construction achievements. Practice has shown that this integrated system effectively transforms scientific research resources into educational advantages, providing an effective pathway for cultivating top-notch talents in traditional Chinese medicine with solid practical skills and advanced innovative capabilities. It also offers a replicable model for teaching reforms in peer institutions.

Objective To analyze the predictive value of the combined application of triglyceride-glucose index (TyG) and triglyceride-glucose-body mass index (TyG-BMI) for the severity of coronary artery lesions in postmenopausal patients with coronary heart disease (CHD). Methods Menopausal women who were diagnosed by coronary angiography and received treatment in the cardiovascular department were selected as the research subjects. According to the Gensini score, the patients were divided into a moderate group (n=151) and a severe group (n=203). The clinical indexes of the two groups were compared and ROC analysis was used to predict the efficacy. Results The BMI, age, hypertension rate, WBC, PLT, NEUT, TC, TG, LCI, TyG, and TyG‑BMI in the severe group were significantly higher than those in the moderate group, while ALB was significantly lower than that in the moderate group (P<0.05). Multivariate logistic regression showed that age, hypertension, WBC, TyG, and TyG‑BMI were independent risk factors for severe coronary artery disease in postmenopausal women (P<0.05). ROC curve analysis showed that the AUC of TyG was 0.745, the AUC of TyG‑BMI was 0.797, and the AUC of TyG combined with TyG‑BMI was 0.803, all showing good predictive performance. Conclusion TyG and TyG‑BMI are effective indicators for predicting the severity of coronary artery disease in postmenopausal CHD patients. The combination of the two can further improve predictive efficacy, providing a simple and economical reference for early clinical risk stratification.

ObjectiveBased on ultra-performance liquid chromatography-quadrupole-electrostatic field orbitrap high resolution mass spectrometry（UPLC-Q-Orbitrap-MS）， the chemical constituents of Qili Qiangxin capsules was identified， and their distribution in vivo was analyzed. MethodsUPLC-Q-Orbitrap-MS was used to detect the sample solution of Qili Qiangxin capsules， as well as the serum， brain， heart， lung， spleen， liver and kidney tissues of mice after oral administration. Using the Thermo Xcalibur 2.2 software， the compound information database was constructed， and the molecular formulas of compounds corresponding to the quasi-molecular ions were fitted. Based on the information of retention time， accurate relative molecular mass and fragments， the compounds and their distribution in vivo were analyzed by comparing with the data of reference substances and literature. ResultsA total of 233 compounds， including 70 terpenoids， 60 flavonoids， 23 organic acids， 17 alkaloids， 20 steroids， 7 coumarins and 36 others， were identified or predicted from Qili Qiangxin capsules， 73 of which were identified matching with standard substances. Tissue distribution results showed that 71， 17， 38， 33， 32， 58 and 43 migrating components were detected in blood， brain， heart， lung， spleen， liver and kidney， respectively. Thirty-seven components were absorbed into the blood and heart， including quinic acid， benzoylaconitine benzoylmesaconine and so on. Fourteen components were absorbed into the blood and six tissues， including calycosin， methylnissolin， formononetin， alisol B， alisol A and so on. ConclusionThis study comprehensively analyzes the chemical components of Qili Qiangxin capsules and their distribution in vivo. Among them， astragaloside Ⅳ， salvianolic acid B， ginsenoside Rb₁， ginsenoside Rb₃， ginsenoside Rd， ginsenoside Rg₃， calycosin-7-glucoside， and sinapine may be the important components for the treatment of heart failure， which can provide useful reference for its quality control and research on pharmacodynamic material basis.