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.

BackgroundIndividuals with chronic multimorbidity exhibit a high prevalence of sleep disturbances， which impair their physical and psychological well-being and impede chronic disease management. Although sleep quality is closely influencing with psychological functioning， empirical evidence regarding the longitudinal trajectories of sleep quality and their influencing factors in this population remains insufficient. ObjectiveTo investigate the longitudinal trajectories of sleep quality and associated factors among patients with chronic multimorbidity from hospitalization through 6 months post-discharge， aiming to facilitate the early identification of high-risk individuals and the development of personalized sleep interventions. MethodsA total of 228 inpatients with chronic multimorbidity， primarily with cardiovascular and metabolic chronic conditions， were enrolled via random sampling at The Third People's Hospital of Fuyang from January 2023 to September 2024. Baseline assessments were conducted during hospitalization using the general socio-demographic questionnaire， the Sleep Dysfunction Rating Scale （SDRS）， the Ruminative Response Scale （RRS）， and the Hospital Anxiety and Depression Scale （HADS）. Sleep quality was further evaluated using the SDRS at 1-， 3-， and 6-month post-discharge. Latent class growth model was employed to delineate trajectories of sleep quality. Multivariate logistic regression analysis was subsequently performed to identify influencing factors of sleep quality. ResultsA total of 201 patients with chronic diseases were included.Based on four predefined assessment time points （baseline， 1 month， 3 months， 6 months post-discharge）， the latent class growth model identified three distinct trajectories of sleep quality： persistent high sleep disturbance （n=72， 35.82%）， moderate sleep disturbance-progression （n=73， 36.32%）， and low sleep disturbance （n=56， 27.86%）. Multivariate logistic regression analysis revealed that， compared with the low sleep disturbance group， multiple factors increased the odds of membership in the persistent high sleep disturbance group： per capita monthly household income of <3 000 yuan （β=13.131， P<0.01） or 3 000–5 000 yuan （β=5.913， P<0.05）， aged 45–65 years （β=9.536， P<0.05）， presence of 3 （β=7.792， P<0.05） or >3 comorbid chronic conditions （β=6.626， P<0.05）， as well as higher RRS （β=0.334， P<0.01） and HADS score （β=1.628， P<0.01）. Additionally， patients aged 45–65 years （β=2.777， P<0.05）， with 3 （β=3.802， P<0.01） or >3 comorbid chronic conditions （β=2.463， P<0.01）， and with higher RRS （β=0.111， P<0.01） and HDAS scores （β=0.350， P<0.05） also demonstrated significantly increased odds of belonging to the moderate sleep disturbance–progression group. ConclusionThere is significant population heterogeneity in sleep quality trajectories among patients with chronic multimorbidity， with age， per capita household monthly income， number of comorbidities， rumination， and anxiety/depression symptoms being identify as the primary influencing factors.

AIM To investigate the effects of volatile oil from Acorus tatarinowii Schott(A.tatarinowii)on neuroinflammation in a rat model of tic disorders.METHODS The SD rats were randomly divided into the blank group(8 rats)and the model group(40 rats).The rat models of tic disorders established successfully by intraperitoneal injection of iminodiapropionitrile(IDPN)were further divided into the model group,the tiapride group and the high-dose,moderate-dose and low-dose A.tatarinowii volatile oil groups,with 8 rats in each group.The 4-week intragastric treatment of respective drug was initiated the next day after the completion of modeling,and normal saline was dosed upon the blank group and the model group,during which the rats' behavioral changes were assessed by stereotyped behavior and motor behavior score every week.After the administration,the rats had their morphological changes of striatal neurons observed by Nissl staining;their levels of TGF-β,IL-10,TNF-αand IL-1β in serum and striatum detected by ELISA;their striatal protein expressions of CX3CL1 and CX3CR1 detected by Western blot and immunohistochemistry;and their striatal expressions of M1,M2 microglia marker proteins CD86,CD206,SYN and PSD-95 detected by immunofluorescence co-staining.RESULTS Compared with the model group,the A.tatarinowii volatile oil groups demonstrated improved twitch-like behavior;decreased scores of motor behavior and rigid behavior(P＜0.01);alleviated damage of Nissl bodies in neurons;increased serum and striatum levels of TGF-β and IL-10(P＜0.05,P＜0.01);decreased levels of TNF-α and IL-1β(P＜0.01);decreased striatal protein expressions of CX3CL1 and CX3CR1(P＜0.01);increased protein expressions of PSD95 and SYN(P＜0.05,P＜0.01);and decreased CD86/Iba1(P＜0.01)and increased CD206/Iba1(P＜0.01)in terms of the fluorescence intensity.CONCLUSION A.tatarinowii volatile oil contributes an anti-tic effect and improves the neuroinflammation in the brain of the rat model of tic disorders by promoting the transformation of microglia into M2 type via CX3CL1/CX3CR1 signal axis.

In recent decades,the incidence of human papillomavirus(HPV)-associated oropharyngeal squamous cell carcinoma(OPSCC)has shown a marked increase.Significant changes have also occurred in the OPSCC diagnosis and treatment paradigm.Deter-mining HPV status prior to treatment is now essential,and radiotherapy/chemotherapy,immunotherapy,and minimally invasive surgical techniques have progressively emerged as key modalities for managing OPSCC.However,alongside these paradigm shifts,a comprehen-sive technical consensus guiding the entire diagnostic and therapeutic process for OPSCC patients is currently lacking.Given China's large population base and the rising incidence of OPSCC,an expert panel convened to develop a clinical technical consensus on OPSCC diagno-sis and management tailored to China's specific context.This consensus aims to further enhance and standardize understanding of OPSCC management techniques among relevant healthcare professionals.

BACKGROUND:Bone morphogenetic protein 2 is vital in embryonic development,bone formation,and regeneration,but its high-dose application is linked to cancer.Bone morphogenetic protein 2 osteogenic peptide L20 reduces adverse effects like cancer and boosts bone tissue regeneration.OBJECTIVE:To graft bone morphogenetic protein 2 active peptide segments onto mesopores and surfaces through a peptide mimicry strategy inspired by oysters,and explore its impact on osteogenic properties of tissue-engineered bone.METHODS:(1)Mesoporous bioactive glass was synthesized using a template method.Bone morphogenetic protein 2 osteogenic peptide L20 was loaded onto mesoporous bioactive glass using a one-step synthesis method to characterize the morphology and in vitro sustained release properties of mesoporous active glass nanoparticles loaded with bone morphogenetic protein 2 osteogenic active peptide L20.(2)Bone marrow mesenchymal stem cells were isolated and extracted from SD rats.After two generations,they were co-cultured with PBS(blank group),mesoporous bioactive glass nanoparticles(control group),and mesoporous bioactive glass nanoparticles loaded with bone morphogenetic protein 2 osteogenic active peptide L20(experimental group).Cell live/dead fluorescence staining and CCK-8 assay were used to detect cytotoxicity and cell proliferation.Scanning electron microscopy was used to observe cell adhesion.After osteogenic induction and differentiation,alkaline phosphatase staining,Alizarin red S staining,and osteogenesis-related gene expression were detected.(3)Fifteen SD rats were selected to establish bilateral femoral condyle defect models and divided into three groups using a random number table method:the blank group(n=5)was not implanted with any material;the control group(n=5)was implanted with mesoporous bioactive glass nanoparticles,and the experimental group(n=5)was implanted with mesoporous bioactive glass nanoparticles loaded with bone morphogenetic protein 2 osteogenic active peptide L20.Eight weeks after surgery,femoral Micro-CT scanning and tissue morphology observation were performed.RESULTS AND CONCLUSION:(1)Scanning electron microscopy showed that the mesoporous bioactive glass nanoparticles loaded with bone morphogenetic protein 2 osteogenic active peptide L20 were spherical and monodisperse particles.Transmission electron microscopy showed their porous structure with an average particle size of(268.10±0.58)nm,which could release L20 in vitro.(2)Mesoporous bioglass nanoparticles loaded with bone morphogenetic protein 2 osteogenic active peptide L20 were non-cytotoxic and could promote the proliferation and adhesion of bone marrow mesenchymal stem cells.Compared with the blank group and the control group,the alkaline phosphatase activity and extracellular matrix mineralization capacity of the experimental group were increased(P<0.05),and the mRNA expression levels of alkaline phosphatase,Runx2,and osteocalcin were increased(P<0.05).(3)The results of femoral Micro-CT scanning showed that compared with the blank group and the control group,the new bone mass and bone density of the experimental group were increased(P<0.05).The results of hematoxylin-eosin and Masson staining showed that compared with the blank group and the control group,the new bone formation and collagen fibers of the experimental group were increased.(4)These findings indicate that mesoporous bioactive glass loaded with bone morphogenetic protein 2 active peptide L20 exhibits excellent biocompatibility and in vitro and in vivo osteogenic properties,promoting regeneration and repair of SD rat femoral condyle defects.

AIM:To evaluate the pharmacokinet-ics(PK)and pharmacodynamics(PD)of two recom-binant human insulin injection by euglycemic clamp technology in healthy male subjects after a single subcutaneous injection.METHODS:We con-ducted a randomized,open-label,single dose,two period,crossover study.A total of 24 healthy male subjects were enrolled and randomized to receive single subcutaneous doses(0.2 U/kg)of the investi-gational products every period.The PK and PD characteristics were assessed by euglycemic clamp up to 14 hours after dosing.RESULTS:Euglycemic clamp technique was successfully established.C-peptide levels detected at each time point before and after administration indicated that endoge-nous insulin secretion was inhibited in the two groups after administration.The geometric mean ratio of Cmax and AUC0-tand 90％confidence interval(CI)of test preparation and reference preparation under fasting condition were in the range of 80.00％-125.00％.CONCLUSION:The human insulin produced by KP Biotech demonstrated similarity to the reference preparation Humulin? in PK and PD characteristics in healthy Chinese subjects.

Malignant tumors,as chronic diseases that seriously affect human life and health,are one of the most serious public health problems worldwide in the 21st century.Hubei Province at-taches great importance to the prevention and control of chronic diseases such as cancer,and launched the"323"campaign in 2021.This paper reviews the progress of cancer prevention and control in the"323"campaign from 2021 to 2024 from the aspects of science popularization,tu-mor registration,cancer screening,standardized diagnosis and treatment,and grassroots capacity improvement,and explores the key points of cancer prevention and control work in Hubei Province in the next step.