The initiation of adaptive immune responses relies on the precise recognition and interpretation of antigenic information. In this process, the specific binding of T cell receptors (TCRs) to peptide-major histocompatibility complex (pMHC) molecules represents one of the key molecular events in the initiation of adaptive immune responses. Accordingly, the structural features of TCR-pMHC complexes provide a fundamental basis for dissecting antigen recognition mechanisms and support rational vaccine design, therapeutic target discovery in TCR-based immunotherapy, and TCR identification and optimization. However, experimental determination of TCR-pMHC structures remains costly, time-consuming, and limited in coverage, making computational approaches essential for rapidly obtaining reliable structural information. Computational methods for predicting the structures of TCR-pMHC complexes have advanced rapidly in recent years, driven by progress in deep learning-based modeling frameworks and the increasing availability of structural and sequence resources. Despite these developments, most existing tools do not adequately distinguish the key structural and biophysical differences between MHC class I (MHC-I) and MHC class II (MHC-II) complexes during model construction. As a consequence, their predictive performance differs substantially between class I and class II complexes. In general, structural predictions for class I complexes outperform those for class II complexes. This discrepancy may be related to several fundamental differences between the two systems, including the architecture of the peptide-binding groove, the distribution of peptide lengths, and the properties of peptide flanking residues (PFRs). Compared with MHC-I molecules, MHC-II molecules usually bind longer antigenic peptides, which typically range from 13 to 25 amino acids in length. PFRs at both termini of these peptides participate in regulating the overall conformation of TCR-pMHC class II complexes and exert a pronounced effect on the geometric and physicochemical characteristics of the TCR-pMHC binding interface. Furthermore, within the TCR recognition interface, the complementarity-determining regions (CDRs) consist of segments that differ markedly in conformational behavior. They commonly include regions that are relatively rigid and structurally stable, together with highly flexible segments exhibiting substantial conformational plasticity. These rigidity-flexibility features constitute an essential structural basis enabling TCRs to recognize diverse peptide-MHC ligands and to accommodate conformational heterogeneity at the interface. However, many current modeling tools, in an effort to enforce global conformational stability or reduce structural noise, tend to over-constrain intrinsically flexible regions. Such oversimplification may lead to inappropriate rigidification of flexible CDR loops, resulting in local structural distortions, compromised interface geometry, or even complete modeling failure for specific complexes. Against this background, the review approaches the field from the perspective of computational differences between MHC-I and MHC-II complexes. We first systematically organize and summarize available resources related to TCRs and pMHCs, including structural datasets, sequence databases, prediction tools, and benchmarking studies. We then focus on five representative tools capable of predicting both class I and class II complexes—AlphaFold2, AlphaFold3, TCRmodel2, tFold-TCR, and TCR-pHLA_ModellerS. After excluding structures present in the training sets of these tools, we constructed a benchmark dataset comprising 25 class I and 10 class II TCR-pMHC complexes in the bound state and conducted a systematic evaluation using this dataset. We first employ widely used general evaluation metrics, including All-Atom Root Mean Square Deviation (All-Atom RMSD), Backbone RMSD, Template Modeling score (TM-score), and DockQ, to assess the global conformational accuracy and interface modeling quality of class I and class II complexes. For class II complexes, we propose for the first time a peptide flanking residue deviation index, including the PFRs-Deviation Index (PFRs-DI), N-PFR-Deviation Index (N-PFR-DI), and C-PFR-Deviation Index (C-PFR-DI), to quantitatively characterize conformational deviations in PFRs. In addition, we propose the CDR conformational consistency index (CCC) designed to qualitatively evaluate the ability of prediction tools to capture TCR CDR conformational flexibility. These metrics collectively assess a tool’s ability to model both overall conformation and critical functional regions, thereby addressing the limitations of existing evaluation criteria that overemphasize global structure while inadequately capturing modeling quality in key functional areas. This establishes a unified analytical framework for MHC-I and MHC-II complexes to guide data resource selection, modeling strategy formulation, and evaluation system development. The framework further advances computational modeling and provides crucial support for multi-scale analysis of TCR-pMHC recognition mechanisms and their biological functions.

The initiation of adaptive immune responses relies on the precise recognition and interpretation of antigenic information. In this process, the specific binding of T cell receptors (TCRs) to peptide-major histocompatibility complex (pMHC) molecules represents one of the key molecular events in the initiation of adaptive immune responses. Accordingly, the structural features of TCR-pMHC complexes provide a fundamental basis for dissecting antigen recognition mechanisms and support rational vaccine design, therapeutic target discovery in TCR-based immunotherapy, and TCR identification and optimization. However, experimental determination of TCR-pMHC structures remains costly, time-consuming, and limited in coverage, making computational approaches essential for rapidly obtaining reliable structural information. Computational methods for predicting the structures of TCR-pMHC complexes have advanced rapidly in recent years, driven by progress in deep learning-based modeling frameworks and the increasing availability of structural and sequence resources. Despite these developments, most existing tools do not adequately distinguish the key structural and biophysical differences between MHC class I (MHC-I) and MHC class II (MHC-II) complexes during model construction. As a consequence, their predictive performance differs substantially between class I and class II complexes. In general, structural predictions for class I complexes outperform those for class II complexes. This discrepancy may be related to several fundamental differences between the two systems, including the architecture of the peptide-binding groove, the distribution of peptide lengths, and the properties of peptide flanking residues (PFRs). Compared with MHC-I molecules, MHC-II molecules usually bind longer antigenic peptides, which typically range from 13 to 25 amino acids in length. PFRs at both termini of these peptides participate in regulating the overall conformation of TCR-pMHC class II complexes and exert a pronounced effect on the geometric and physicochemical characteristics of the TCR-pMHC binding interface. Furthermore, within the TCR recognition interface, the complementarity-determining regions (CDRs) consist of segments that differ markedly in conformational behavior. They commonly include regions that are relatively rigid and structurally stable, together with highly flexible segments exhibiting substantial conformational plasticity. These rigidity-flexibility features constitute an essential structural basis enabling TCRs to recognize diverse peptide-MHC ligands and to accommodate conformational heterogeneity at the interface. However, many current modeling tools, in an effort to enforce global conformational stability or reduce structural noise, tend to over-constrain intrinsically flexible regions. Such oversimplification may lead to inappropriate rigidification of flexible CDR loops, resulting in local structural distortions, compromised interface geometry, or even complete modeling failure for specific complexes. Against this background, the review approaches the field from the perspective of computational differences between MHC-I and MHC-II complexes. We first systematically organize and summarize available resources related to TCRs and pMHCs, including structural datasets, sequence databases, prediction tools, and benchmarking studies. We then focus on five representative tools capable of predicting both class I and class II complexes—AlphaFold2, AlphaFold3, TCRmodel2, tFold-TCR, and TCR-pHLA_ModellerS. After excluding structures present in the training sets of these tools, we constructed a benchmark dataset comprising 25 class I and 10 class II TCR-pMHC complexes in the bound state and conducted a systematic evaluation using this dataset. We first employ widely used general evaluation metrics, including All-Atom Root Mean Square Deviation (All-Atom RMSD), Backbone RMSD, Template Modeling score (TM-score), and DockQ, to assess the global conformational accuracy and interface modeling quality of class I and class II complexes. For class II complexes, we propose for the first time a peptide flanking residue deviation index, including the PFRs-Deviation Index (PFRs-DI), N-PFR-Deviation Index (N-PFR-DI), and C-PFR-Deviation Index (C-PFR-DI), to quantitatively characterize conformational deviations in PFRs. In addition, we propose the CDR conformational consistency index (CCC) designed to qualitatively evaluate the ability of prediction tools to capture TCR CDR conformational flexibility. These metrics collectively assess a tool’s ability to model both overall conformation and critical functional regions, thereby addressing the limitations of existing evaluation criteria that overemphasize global structure while inadequately capturing modeling quality in key functional areas. This establishes a unified analytical framework for MHC-I and MHC-II complexes to guide data resource selection, modeling strategy formulation, and evaluation system development. The framework further advances computational modeling and provides crucial support for multi-scale analysis of TCR-pMHC recognition mechanisms and their biological functions.

OBJECTIVE To investigate the effects and mechanisms of Lycium ruthenicum Murr. in improving sleep. METHODS Network pharmacology was employed to identify the active components of L. ruthenicum and their associated disease targets， followed by enrichment analysis. A caffeine‑induced zebrafish model of sleep deprivation was established ， and the zebrafish were treated with L. ruthenicum Murr. extract （LRME） at concentrations of 0.1， 0.2 and 0.4 mg/mL， respectively； 24 h later， behavioral changes of zebrafish and pathological alterations in brain neurons were subsequently observed. The levels of inflammatory factors [interleukin-6 （IL-6）， IL-1β， IL-10， tumor necrosis factor-α （TNF-α）]， oxidative stress markers [superoxide dismutase （SOD）， malondialdehyde （MDA）， glutathione peroxidase （GSH-Px）， catalase （CAT）]， and neurotransmitters [5- hydroxytryptamine （5-HT）， γ-aminobutyric acid （GABA）， glutamic acid （Glu）， dopamine （DA）， and norepinephrine （NE）] were measured. The protein expression levels of protein kinase B1 （AKT1）， phosphorylated AKT1 （p-AKT1）， epidermal growth factor receptor （EGFR）， B-cell lymphoma 2 （Bcl-2）， sarcoma proto-oncogene，non-receptor tyrosine kinase （SRC）， and heat shock protein 90α family class A member 1 （HSP90AA1） in the zebrafish were also determined. RESULTS A total of 12 active components and 176 intersecting disease targets were identified through network pharmacology analysis. Among these， apigenin， naringenin and others were recognized as core active compounds， while AKT1， EGFR and others served as key targets； EGFR tyrosine kinase inhibitor resistance signaling pathway was identified as the critical pathway. The sleep improvement rates in zebrafish of LRME low-， medium-， and high-dose groups were 54.60%， 69.03% and 77.97%， 开发。E-mail：hjp_yft@163.com respectively， while the inhibition ratios of locomotor distance were 0.57， 0.83 and 0.95， respectively. Compared with the model group， the number of resting counts， resting time and resting distance were significantly increased/extended in LRME medium- and high-dose groups （P＜0.05）. Neuronal damage in the brain was alleviated. Additionally， the levels of IL-6， IL-1β， TNF-α， MDA， Glu， DA and NE， as well as the protein expression levels of AKT1， p-AKT1， EGFR， SRC and HSP90AA1， were markedly reduced （P＜0.05）， while the levels of IL-10， SOD， GSH-Px， CAT， 5-HT and GABA， as well as Bcl-2 protein expression， were significantly elevated （P＜0.05）. CONCLUSIONS L. ruthenicum Murr. demonstrates sleep-improving effects， and its specific mechanism may be related to the regulation of inflammatory responses， oxidative stress， neurotransmitter balance， and the EGFR tyrosine kinase inhibitor resistance signaling pathway.

OBJECTIVE To investigate the mechanism by which Qiaobai cold compress solution （QBCS） improves acne vulgaris （AV） based on transcriptomics and animal experiments. METHODS Rats were randomly divided into a blank control group （ n ＝6） and a modeling group （ n ＝30）. AV models were established in the modeling group by topical application of oleic acid to the inner surface of both ears， combined with subcutaneous injection of Cutibacterium acnes suspension into the auricle. Successfully modeled rats were further divided into the model group， positive control group （Tretinoin cream， 0.045 g/kg）， and QBCS low-， medium-， high-dose groups [3.55， 7.11， 14.22 g/kg （calculated by the amount of crude drug） ] ， with 6 rats in each group. Rats in each d rug group were treated with the corresponding drugs once daily for 14 consecutive days. After the final administration， changes in the appearance of the ears and histopathological changes in the ear tissues were observed， and serum levels of inflammatory factors， including tumor necrosis factor-α （TNF-α）， interleukin-6 （IL-6） and IL-1β， were measured. Auricular tissues from the blank control group， model group and QBCS medium-dose group were collected for transcriptome sequencing. Differential expressed genes （DEGs） were screened and subjected to Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis， followed by validation using real-time quantitative polymerase chain reaction and Western blot assay. RESULTS Compared with the model group， rats in all QBCS groups showed alleviated auricular acne symptoms， with reduced epidermal thickening， sebaceous gland hyperplasia， and inflammatory cell infiltration. Serum levels of TNF-α （except for the QBCS low-dose group）， IL-6 （except for the QBCS low-dose group） and IL-1β were significantly decreased （ P ＜0.05）. A total of 590 DEGs were identified （blank control group vs. model group）， and 596 DEGs were identified （model group vs. QBCS medium-dose group）. Above DEGs （blank control group vs. model group） were mainly enriched in Toll-like receptor （TLR） and nuclear factor-kappa B （NF-κB） signaling pathways， etc. Validation experiments showed that， compared with model group， low-， medium- and high-dose of QBCS reduced， to varying degrees， the mRNA expression of TNF-α， TLR2， interferon-γ and CXC chemokine ligand 8 in the auricular tissues of AV rats， increased the mRNA expression of peroxisome-proliferator-activated receptor gamma and tumor protein 53， and inhibited the phosphorylation of NF-κB p65 protein as well as the expressions of TLR2 and myeloid differentiation primary response protein 88（MyD88） （ P ＜0.05）. CONCLUSIONS QBCS can alleviate auricular inflammation and skin lesions in AV rats. This effect may be related to inhibition of the TLR/MyD88/NF-κB signaling pathway， thereby suppressing the expression of downstream inflammatory factors such as TNF-α.

Objective To provide a reference for the establishment, development and application of the adverse drug reaction （ADR） automated monitoring system, through verifying and quantifying the research hotspots and advantages of the system by CiteSpace software and systematic review. Methods Literature on ADR automated monitoring up to December 2023 were retrieved and screened from CNKI and web of science databases. CiteSpace 6.4.R1 software was used to conduct co-occurrence, clustering and emergence analysis, and to visualize and comparatively analyze the research hotspots, rules and distribution in the field of automated monitoring of ADR at home and abroad. In compliance with the preferred reporting items for systematic reviews and Meta-analyses （PRISMA）, literature covering publications in English and Chinese including detection rates of ADR collected using Incident Reporting Systems （IRSs） and/or automated monitoring systems were retrieved and screened. The advantages and disadvantages of automated monitoring systems were analyzed by comparing the differences between these two systems in terms of the number of ADR reports and the types of positive signals. Results A total of 56 articles in English and 80 articles in Chinese were indexed by CiteSpace. The research hotspots in recent years included data mining, deep learning, text classification techniques, machine learning and so on. A total of seven studies compiled with the inclusion criteria for the systematic evaluation, all of which were completed between 1991 and 2021 in hospitals in four countries. 150 526 medical records were reviewed from 15 institutions. A total of 194 ADR reports were collected by IRSs. A total of 2 090 ADR reports were collected by the automated monitoring system over the same period, indicating a 977% increase in the number of ADR reports （P=0.0156） compared with the IRSs. Conclusion The ADR automatic monitoring system had significantly improved the level of drug risk identification and reduced costs, but it was necessary to optimize the algorithm, expand the data source and carry out standardization construction to overcome the current limitations.

AIM:To investigate whether melatonin can ameliorate acute myocardial infarction(AMI)by in-hibiting ferroptosis.METHODS:H9C2 cells were cultured in AnaeroPack system with low sugar and serum-free medium for 10 h to construct a cell model of AMI.Then cells were treated with melatonin and ferroptosis inducer erastin.The cell activity,reactive oxygen species(ROS),lipid peroxidation,mitochondrial membrane potential(MMP),and ferroptosis related protein expression were detected.A rat model of AMI induced by isoprenaline(ISO)injection was established to evaluate the effects of melatonin,in which the myocardial infarction size,cardiac injury,pathological changes,oxidative stress,iron ion and ferroptosis related protein expression were examined.RESULTS:Melatonin decreased the oxidative stress,lipid peroxidation and expression of ferroptosis protein in cardiomyocytes induced by hypoxia,but these effects could be impeded by the ferroptosis inducer erastin.Furthermore,in vivo experiments,we also found that melatonin im-proved the myocardial infarction size,cardiac injury,pathological changes,oxidative stress,and alleviated iron ion accu-mulation and ferroptosis.CONCLUSION:The cardioprotective effects of melatonin in AMI are associated with the inhi-bition of ferroptosis.

Objective To investigate the expression of T cell subsets in the spleen of BTBR T+Itpr3tf autistic mouse at 4,8,and 12 weeks of age,and to determine the optimal age for studying the relationship between immune abnormalities and autism in BTBR autistic mice.Methods It randomly selected 5～6 male BTBR mouse at 4 weeks,8 weeks,and 12 weeks of age and C57BL/6J mouse of the same gender at corresponding ages for the three-box social interaction test,the self-grooming test,and the marble-burying test;Single cell suspensions were prepared from the spleens of mouse at 8 and 12 weeks of age,and flow cytometry was used to detect 8 subsets of T cells(TH 1,TH2,TH17,TC1,TC2,TC17,TFH,and Treg).Results Compared with C57BL/6J mouse of the same age,BTBR mouse at 4 weeks,8 weeks,and 12 weeks of age showed a decrease in social time(P＜0.001),an increase in grooming time(P＜0.01,P＜0.001),and an increase in the number of marbles buried(P＜0.01,P＜0.001)in BTBR mouse at 8 weeks and 12 weeks of age.As well,the expression of TH 1(P＜0.001),TH2(P＜0.01),TC 1(P＜0.05),TC2(P＜0.001),and TFH(P＜0.01)cells in 8-week-old BTBR mouse were significantly increased,while the expression of Treg(P＜0.001)cells were significantly decreased;The expression of TH 1(P＜0.01),TH2(P＜0.01),TH 17(P＜0.05),TC1(P＜0.01),TC2(P＜0.001),TC 17(P＜0.01),and TFH(P＜0.001)increased in 12-week-old BTBR mouse,while the expression of Treg(P＜0.05)cells decreased.At different age stages(P＜0.050)the ratio of TH 1/Treg and TC 1/Treg in 8-week-old BTBR mouse were significantly higher than those in 12 week old mouse,while the TC 17/Treg ratio decreased.Conclusions BTBR mouse at different developmental stages exhibit varying degrees of abnormal increase in Teff/Treg ratio.Based on result of behavioral test,it is recommended to use 8-week-old BTBR mice for research on autism and immune abnormalities.

OBJECTIVE To analyze the status of targeted monitoring of the health care-associated infections in in-tensive care unit(ICU)of a three-A hospital of northwest China in recent 7 years so as to provide bases for formu-lating effective prevention and control measures for the health care-associated infections.METHODS The related data were successively collected from the ICU hospitalized patients of the Second Affiliated Hospital of Air Force Medical University who were under the targeted monitoring by nosocomial infection real-time monitoring system from Jan.2017 to Dec.2023.The data included the incidence of infections,infection sites,use of catheters,inci-dence of catheter-related infections,and distribution and prevalence trend of pathogens.RESULTS A total of 49,137 hospitalized patients from five ICU wards of respiratory medicine department,neurosurgery department,neurology department,thoracic surgery department and critical care medicine department were under the monito-ring,1446(1681 case-times)of whom had health care-associated infections,with the infection rate 2.94％,the case-time infection rate 3.42％.The respiratory system was the major infection site of the patients with the health care-associated infections.Among the patients with instrument-associated infections,20.70％had ventilator-asso-ciated pneumonia(VAP),5.71％had urinary catheter-associated urinary tract infection(CAUTI),and 1.96％had catheter-related bloodstream infection(CRBSI).Totally 405 strains of non-repetitive pathogens were isolated from 477 patients with instruments-associated infections,78.77％of which were gram-negative bacteria.The iso-lation rate of multidrug-resistant organisms(MDROs)was 44.12％,and Acinetobacter baumannii was the pre-dominant species of pathogen isolated from the patients with VAP.CONCLUSIONS The targeted monitoring of health care-associated infections may facilitate the awareness of dynamic changes of the infections in a accurate and timely manner so as to provide bases for developing effective prevention and control measures for the health care-associated infection.It is necessary to strengthen the prevention and control of the infections in critical care medi-cine department,neurosurgery department as well as the lower respiratory tract infections and prevent the MDROs infection in the meantime.

AIM To study the effects of total flavonoids of Dracocephalum Moldavica L.(TFDM)on reducing the inflammatory response of RAW264.7 macrophages induced by ox-LDL via the nuclear factor κB(NF-κB)/NOD-like receptor 3(NLRP3)signaling pathway.METHODS The RAW264.7 macrophages cultured in vitro were divided into the normal group,the model group(50 μg/mL ox-LDL),the TFDM group(100 μg/mL TFDM+50 μg/mL ox-LDL),the NF-κB inhibitor group(10 μmol/L Bay11-7821+50 μg/mL ox-LDL)and the TFDM+NF-κB inhibitor group(100 μg/mL TFDM+10 μmol/L Bay11-7821+50 μg/mL ox-LDL).The cells had their viability assessed by CCK-8 method;their ROS expression detected by the ROS kit;their mRNA expressions of NF-κB p65,NLRP3,Caspase-1,IL-18 and IL-1β detected by RT-qPCR;their protein expressions of NF-κB p65,IκBα,NLRP3,pro-Caspase-1,Caspase-1,IL-18 and IL-1β by Western blot;their protein expressions of NF-κB p65 and NLRP3 detected using immunofluorescence method.RESULTS Compared with the normal group,the model group showed increased ROS expression(P＜0.01);increased mRNA expressions of NF-κB p65,NLRP3,Caspase-1,IL-18 and IL-1β(P＜0.05,P＜0.01);decreased protein expressions of IκBα and cytoplasmic NF-κB p65(P＜0.01);increased protein expressions of nuclear NF-κB p65,NLRP3,Caspase-1,IL-1 β and IL-18(P＜0.01);and increased fluorescence intensity of NF-κB p65 and NLRP3(P＜0.01).Compared with the model group,the groups intervened with either TFDM or TFDM+inhibitor displayed decreased ROS expression(P＜0.01);the groups administrated with TFDM or NF-κB inhibitor,or TFDM+inhibitor showed decreased mRNA expressions of NF-κB p65,NLRP3,Caspase-1,IL-18 and IL-1β(P＜0.05,P＜0.01),increased protein expressions of IκBα and cytoplasmic NF-κB p65(P＜0.05,P＜0.01),decreased protein expressions of nuclear NF-κB p65,NLRP3,Caspase-1,IL-1β and IL-18(P＜0.05,P＜0.01),and decreased fluorescence intensity of NF-κB p65 and NLRP3(P＜0.01).There existed no significant group difference between the TFDM group and the NF-κB inhibitor group(P＞0.05).The TFDM+inhibitor group demonstrated decreased mRNA expressions of IL-1βand IL-18(P＜0.05),increased IκBα protein expression(P＜0.05),decreased protein expressions of nuclear NF-κB p65,NLRP3,Caspase-1,IL-1 β and IL-18(P＜0.05),and decreased fluorescence intensity of NLRP3 protein(P＜0.05).CONCLUSION TFDM can inhibit the ox-LDL-induced inflammatory response of RAW264.7 macrophages,and the mechansism may be associated with the reduced ROS expression and inflammatory factors due to the inhibited activation of the NF-κB/NLRP3 signaling pathway.

Objective To analyze the global literature related to functional cure of hepatitis B from 2019 to 2023 by using bibliometric analysis methods,so as to help researchers understand the research hotspots and trends in this field.Methods The literature related to the topic of functional cure of hepatitis B included in the Science Citation Index Expanded(SCI-Expanded)of the Web of Science Core Collection from 2019 to 2023 was searched.By using VOSviewer and CiteSpace visual analysis tools,analyses were conducted from the perspectives of publication trends,international research cooperation networks,and keyword emergence,and were elaborated with the specific contents of the related literature to elucidate research hotspots and trends.Results A total of 600 eligible papers in this field were included.Keyword co-occurrence and thematic clustering suggested that the main research directions of functional cure were:serum biomarkers for prediction and monitoring of functional cure,functional cure and immunity,nucleoside analog discontinuation,interferon therapy,and long-term prognosis of functional cure.The research contents of the ESI highly cited original research papers were similar to the clustering of the above,but showed more attention on the novel agents for functional cure.The content of the keyword emergence map showed that hotspots of interest changed from virologic mechanisms and serum markers,to nucleoside analog discontinuation and interferon therapy,and finally to immunologic mechanisms and new drug.Conclusion The research hotspots and trends of functional cure of hepatitis B were focused on virological mechanism,serum markers,immunological mechanism,nucleoside analog discontinuation,interferon therapy,and long-term prognosis after cure.