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.

ObjectiveTo explore the effects and mechanism of the modified Danggui Yinzi on "itch-anxiety" model rats of chronic urticaria （CU）. MethodsThe 36 SPF-grade 6-8-week-old female SD rats were randomly divided into a blank control group，a model group，a positive control group，a low-dose modified Danggui Yinzi group，a medium-dose modified Danggui Yinzi group，and a high-dose modified Danggui Yinzi group. A "itch-anxiety" model was established by intraperitoneal injection of a suspension of sodium chloride and aluminum hydroxide and ovalbumin，combined with chronic unpredictable emotional stress stimulation. After successful modeling，rats in each group were administered drugs by gavage. The positive control group was given intragastric administration of the drug solutions of cetirizine and fluoxetine （2.08 mg·kg^-1·d^-1 fluoxetine， 2 mg·kg^-1·d^-1 cetirizine）， the low-，medium-，and high-dose modified Danggui Yinzi groups were administered traditional Chinese medicine at 1.44，2.88， 5.76 g·kg^-1， respectively，while the blank control group and model group were given an equal volume of normal saline. All interventions lasted for 15 days. Behavioral changes were evaluated by the elevated plus-maze test （detecting the percentage of entries into the open arms （OE%），the percentage of time spent in the open arms （OT%），and the total number of entries into the open and closed arms （TNE）），the open-field test （detecting total activity，average movement speed，and latency to enter the central area），and scratching behavior observation. Pathological changes of skin tissues were observed by hematoxylin-eosin （HE） staining and toluidine blue staining，while those of amygdala tissues were observed by HE staining，Nissl staining，and immunofluorescence detection of ionized calcium-binding adapter molecule-1 （Iba-1）. The content of immunoglobulin E （IgE），interleukin-33 （IL-33），histamine in serum and glutamate in the amygdala was detected by enzyme-linked immunosorbent assay （ELISA）. Western blot was used to detect the protein expression of striatal-enriched protein tyrosine phosphatase （STEP），N-methyl-D-aspartate receptor subunit 2B （NR2B）， calmodulin-dependent protein kinase Ⅱ （CaMKⅡ），phosphorylated CaMKⅡ （p-CaMKⅡ），mitogen-activated protein kinase （MAPK），phosphorylated MAPK （p-MAPK），nuclear factor kappa-light-chain-enhancer of activated B cells （NF-κB），phosphorylated NF-κB （p-NF-κB），and postsynaptic density protein-95 （PSD-95） in the amygdala. ResultsCompared with the blank control group，the model group rats showed obvious anxiety-like behaviors （decreased OE%，OT%，and TNE，reduced total activity，slower average movement speed，and prolonged latency to enter the central area），increased scratching times，obvious skin inflammation and mast cell degranulation，severe amygdala tissue damage，increased glutamate content in the amygdala，and elevated levels of IgE and IL-33 in serum. The expression of STEP，NF-κB，p-NF-κB，NR2B，MAPK，p-MAPK，CaMKⅡ，and p-CaMKⅡ proteins in the amygdala increased，while the expression of PSD-95 protein decreased （P<0.05）. Compared with the model group，the modified Danggui Yinzi group of each dose had increased OE%，OT%，TNE，total activity，and average movement speed，shortened latency to enter the central area， reduced scratching times，alleviated skin inflammation and mast cell degranulation，relieved amygdala tissue damage，decreased glutamate content in the amygdala，and reduced levels of IgE and IL-33 in serum. Moreover，compared with the model group，the low -，medium-，and high-dose modified Danggui Yinzi groups showed decreased expression levels of STEP，NF-κB，p-NF-κB，NR2B，MAPK，p-MAPK，CaMKⅡ，and p-CaMKⅡ proteins in the amygdala，and increased expression of PSD-95 protein. There was a significant dose-effect relationship，with the high-dose group showing the most significant regulatory effect （P<0.05）. ConclusionThe modified Danggui Yinzi has a therapeutic effect on "itch-anxiety" model rats of CU. Its mechanism may be related to regulating glutamate metabolism in the amygdala，modulating the STEP/NR2B/CaMKⅡ/MAPK/NF-κB pathway，and regulating the expression of PSD-95.

The International Council for Harmonisation of Technical Requirements for Pharmaceuticals for Human Use （ICH） issued the ICH E6（R3）： Guideline for Good Clinical Practice on January 14， 2025， which will enhance the speed and quality of global clinical trials， including those in China. As the ethical， scientific and quality standards for global drug clinical trials， the highlights of the revision in the ICH E6 （R3） include encouraging innovation to improve efficiency and quality， motivating research participants to participate in the design and implementation of clinical trials throughout the process， emphasizing quality originating from design and based on the quality management of risks， as well as underlining proportionality and standalone chapter to data governance. ICH E6 （R3） adopts an appendix and appendix structure， enabling future revisions to be more convenient and efficient. ICH E6 （R3） restructures the principles section， adding two new principles and reducing the total from 13 to 11， while incorporating extensive explanatory notes. Major revisions also encompass multiple aspects， including regular review by ethics committees to ensure the safety， rights and interests， and well-being of research participants； diversification of informed consent methods， refinement of its processes， and clarification of detailed rules for minors’ consent； qualifications， authorization， and oversight of investigators and their service providers； risk-proportionate design and implementation by sponsors； joint data governance by investigators and sponsors； and the addition and revision of terms， along with updates to three appendices. The design， implementation， and review of drug clinical trials in China are increasingly aligning with international standards. ICH E6 （R3） will accelerate the revision of China’s Guideline for Good Clinical Practice， promote the speed and quality of drug research and development， and further facilitate the internationalization of China’s new drug research and development.

Work serves as a critical means of obtaining resources, facilitating personal growth, realizing self-worth, and engaging in social interactions. However, work-related diseases pose significant threats to workers’ health and productivity, and impose considerable economic burdens. This article categorized work-related diseases into six major types, including musculoskeletal disorders, mental and behavioral disorders, cardiovascular and metabolic diseases, digestive system diseases, reproductive system diseases, and non-specific respiratory diseases, and summarized their risk factors, assessment methods, policy regulation, and prevention and control measures. Current research in this field predominantly relies on cross-sectional studies, which present limitations in causal inference and potential risks of bias. Future studies should expand sample sizes, optimize research designs, and establish multidimensional evaluation systems to comprehensively assess the health and economic impacts of work-related diseases. It is recommended to enhance the translation of research findings into practice, thereby providing a scientific basis for the occupational health protection system and promoting the well-being and sustainable development of the working population.

Since Li Dongyuan formally proposed the concept of "wind medicinals" （Feng Yao），their clinical application has been highly valued by physicians throughout history. However，influenced by the evolution of the term and connotation of "wind medicinals" in modern times，its conceptual understanding，leading to a decline in clinical utilization. Since the new century，Professor Wang Mingjie has integrated LIU Wanxu's sweat pore （Xuanfu） theory into the reinterpretation of wind medicinals，proposing the "Xuanfu-dredging wind medicinal theory"， which has gained widespread recognition in academic circles，revitalizing their clinical application. This study traces the origin of the Xuan-dredging wind medicinals theory and reviews their current functions and clinical applications，finding that the theoretical framework is preliminarily established. Characterized by their pungent and dispersing properties，wind medicines act by opening the Xuanfu throughout the body，exerting therapeutic effects such as dispelling pathogens，resolving stagnation，and enhancing treatments like blood-activation，spleen-fortification，and heat-clearing. They are widely used，showing advantages in treating systemic diseases including ophthalmic and cardiovascular/cerebrovascular disorders. Modern pharmacological research indicates preliminary consensus on hypotheses of cerebral，intestinal，hepatic，and renal Xuanfu. studies on formulas （e.g.，Qufeng Tongqiao Fang），single herbs （e.g.，Mahuang and Gegen），and active constituents （e.g.，tetramethylpyrazine） provide evidence that wind medicines improve key mechanisms like blood-brain barrier function and cerebral microcirculation （material basis of cerebral Xuanfu），supporting their use in brain disorders （e.g.，cerebral ischemia，depression）. Despite clinical and pharmacological support，the clinical application system for wind medicines remains incomplete. Future efforts should focus on high-quality clinical research and mechanistic studies to establish personalized application systems，enhance Xuanfu opening practices，and ensure the effectiveness and safety of wind medicines.

ObjectiveTo explore the potential mechanism of Xiaoqinglongtang（XQL） in the prevention and treatment of high altitude pulmonary edema（HAPE） by network pharmacology， molecular docking， and molecular dynamics simulation， and to verify it by in vivo animal model. MethodsIn this study， the active ingredients， drug targets， and HAPE-related targets of XQL were collected from BATMAN-TCM， GeneCards， and Online Mendelian Inheritance in Man（OMIM） databases. The protein-protein interaction（PPI） network was constructed by using intersection targets， and the core targets were screened and visualized by Cytoscape software. Functional annotation and pathway analysis of the intersection targets were performed by gene ontology （GO） and Kyoto encyclopedia of genes and genomes （KEGG） functional enrichment. AutoDock and GROMACS were used to evaluate the binding ability of active ingredients to key targets. In the experimental verification part， a mouse model of HAPE induced by hypobaric hypoxia（simulated 6 000 m altitude for 48 h） was established. The control effect was evaluated by hematoxylin-eosin（HE） staining， lung tissue water content， lung tissue wet/dry weight ratio， real-time quantitative polymerase chain reaction（Real-time PCR） detection of gene expression levels， and immunohistochemistry and Western blot detection of key protein expression. ResultsA total of 355 active ingredients of XQL， 2 142 targets， 716 HAPE-related targets， and 236 intersection targets were obtained by network pharmacology analysis. Key core targets such as interleukin （IL）-6， tumor necrosis factor （TNF）， protein kinase B1 （Akt1）， and hypoxia-inducible factor-1α （HIF-1α） were screened. The results of GO analysis of common targets involved 738 biological processes（BP）， 72 cellular components（CC）， and 135 molecular functions（MF）. KEGG analysis effectively enriched two important signaling pathways： Phosphoinositol 3-kinase （PI3K）/Akt and HIF-1α. The results of molecular docking and molecular dynamics simulation showed that the screened active ingredients had good binding ability with key targets. In the HAPE model induced by hypobaric hypoxia（6 000 m， 48 h）， the lung tissue water content， lung tissue wet/dry weight ratio， and pathological injury score of the model group were significantly increased（P<0.01）， accompanied by exudation of a large number of red blood cells in the alveoli and alveolar interstitium， a significant increase in inflammatory cells， a significant widening of the alveolar septum， and mutual fusion between the alveoli. The XQL administration group significantly improved the above pathological changes（P<0.01）. The results of inflammatory factor expression showed that compared with the control group， the model group showed significantly up-regulated expression of TNF-α， IL-6， and IL-1β in the lung tissue（P<0.01）. Compared with the model group， the XQL administration group had significantly decreased expression of inflammatory factors（P<0.05， P<0.01）. The mRNA expression of key pathway related genes PI3K， Akt1， mammalian target of rapamycin（mTOR）， and HIF-1α was significantly increased in the model group（P<0.01）， and decreased in a concentration-dependent manner after XQL administration（P<0.05， P<0.01）. The expression levels of key proteins PI3K， phosphorylation（p）-PI3K， Akt1， p-Akt1， mTOR， p-mTOR， and HIF-1α in lung tissue were analyzed by immunohistochemistry and Western blot. Compared with the blank group， the model group showed increased expression of key proteins（P<0.05， P<0.01）. Compared with the model group， the XQL administration group exhibited decreased expression of key proteins（P<0.05， P<0.01）. ConclusionXQL can reduce lung inflammation and improve HAPE. The mechanism may be related to the regulation of PI3K/Akt/mTOR and HIF-1α pathways. This study provides a new idea and a theoretical basis for the treatment of HAPE with XQL.

ObjectiveTo explore the potential mechanism of Xiaoqinglongtang（XQL） in the prevention and treatment of high altitude pulmonary edema（HAPE） by network pharmacology， molecular docking， and molecular dynamics simulation， and to verify it by in vivo animal model. MethodsIn this study， the active ingredients， drug targets， and HAPE-related targets of XQL were collected from BATMAN-TCM， GeneCards， and Online Mendelian Inheritance in Man（OMIM） databases. The protein-protein interaction（PPI） network was constructed by using intersection targets， and the core targets were screened and visualized by Cytoscape software. Functional annotation and pathway analysis of the intersection targets were performed by gene ontology （GO） and Kyoto encyclopedia of genes and genomes （KEGG） functional enrichment. AutoDock and GROMACS were used to evaluate the binding ability of active ingredients to key targets. In the experimental verification part， a mouse model of HAPE induced by hypobaric hypoxia（simulated 6 000 m altitude for 48 h） was established. The control effect was evaluated by hematoxylin-eosin（HE） staining， lung tissue water content， lung tissue wet/dry weight ratio， real-time quantitative polymerase chain reaction（Real-time PCR） detection of gene expression levels， and immunohistochemistry and Western blot detection of key protein expression. ResultsA total of 355 active ingredients of XQL， 2 142 targets， 716 HAPE-related targets， and 236 intersection targets were obtained by network pharmacology analysis. Key core targets such as interleukin （IL）-6， tumor necrosis factor （TNF）， protein kinase B1 （Akt1）， and hypoxia-inducible factor-1α （HIF-1α） were screened. The results of GO analysis of common targets involved 738 biological processes（BP）， 72 cellular components（CC）， and 135 molecular functions（MF）. KEGG analysis effectively enriched two important signaling pathways： Phosphoinositol 3-kinase （PI3K）/Akt and HIF-1α. The results of molecular docking and molecular dynamics simulation showed that the screened active ingredients had good binding ability with key targets. In the HAPE model induced by hypobaric hypoxia（6 000 m， 48 h）， the lung tissue water content， lung tissue wet/dry weight ratio， and pathological injury score of the model group were significantly increased（P<0.01）， accompanied by exudation of a large number of red blood cells in the alveoli and alveolar interstitium， a significant increase in inflammatory cells， a significant widening of the alveolar septum， and mutual fusion between the alveoli. The XQL administration group significantly improved the above pathological changes（P<0.01）. The results of inflammatory factor expression showed that compared with the control group， the model group showed significantly up-regulated expression of TNF-α， IL-6， and IL-1β in the lung tissue（P<0.01）. Compared with the model group， the XQL administration group had significantly decreased expression of inflammatory factors（P<0.05， P<0.01）. The mRNA expression of key pathway related genes PI3K， Akt1， mammalian target of rapamycin（mTOR）， and HIF-1α was significantly increased in the model group（P<0.01）， and decreased in a concentration-dependent manner after XQL administration（P<0.05， P<0.01）. The expression levels of key proteins PI3K， phosphorylation（p）-PI3K， Akt1， p-Akt1， mTOR， p-mTOR， and HIF-1α in lung tissue were analyzed by immunohistochemistry and Western blot. Compared with the blank group， the model group showed increased expression of key proteins（P<0.05， P<0.01）. Compared with the model group， the XQL administration group exhibited decreased expression of key proteins（P<0.05， P<0.01）. ConclusionXQL can reduce lung inflammation and improve HAPE. The mechanism may be related to the regulation of PI3K/Akt/mTOR and HIF-1α pathways. This study provides a new idea and a theoretical basis for the treatment of HAPE with XQL.

The American Heart Association (AHA) and the American College of Cardiology (ACC), in collaboration with multiple professional organizations, jointly released the "Guideline for the Prevention, Detection, Evaluation and Management of High Blood Pressure in Adults" in August 2025. Based on the latest evidence-based medical findings from February 2015 to January 2025, the guideline proposes an individualized treatment strategy grounded in total cardiovascular disease risk stratification, incorporates the novel PREVENT risk assessment model, lowers the medication initiation threshold and control targets for high-risk populations, and provides specific management recommendations for special populations. This article provides an interpretation of these updates and conducts a comparative analysis with the current status of hypertension prevention and treatment in China as well as Chinese guidelines, aiming to offer reference for hypertension control practices in China.

The American Heart Association (AHA) officially released the "2025 Heart Disease and Stroke Statistics: A Report of US and Global Data From the American Heart Association" on January 27, 2025. This report systematically compiles the latest statistics on major cardiovascular diseases worldwide, while simultaneously integrating relevant outcome indicators, including quality of care, procedures, and economic costs, and updating the global prevalence patterns and evolving trends of diverse risk factors impacting cardiovascular health, providing essential guidance for the prevention, diagnosis, and treatment of cardiovascular diseases. Synthesizing insights from this pivotal report and other relevant studies, this article highlights key findings concerning the global prevalence and mortality of heart diseases, associated risk factors, and emerging diagnostic and therapeutic technologies.