ObjectiveTo identify potential influencing factors of urate crystal deposition at ankle/foot in patients with hyperuricemia （HUA）， and to analyze the predictive value of inflammatory indicators for urate crystal deposition in patients with different traditional Chinese medicine （TCM） syndromes， so as to provide potential reference for clinical risk assessment and individualized TCM intervention. MethodsA retrospective study was carried out with the enrollment of 231 HUA patients from The Third Affiliated People's Hospital of Fujian University of Traditional Chinese Medicine between January 2021 and December 2024. The enrolled patients were further divided into a crystal deposition-positive group （143 cases） and a crystal deposition-negative group （88 cases） according to the results of dual-energy computed tomography （CT）. Sociodemographic data， living habits， serum uric acid levels， and inflammatory indicators of the enrolled patients were collcted， and TCM syndrome differentiation was performed. Furthermore， univariate analysis was used to compare inter-group differences in clinical characteristics. MMultivariate Logistic regression was applied to identify the influencing factors of urate crystal deposition. In addition， the receiver operating characteristic （ROC） curves were plotted to evaluate the predictive efficacy of inflammatory indicators for crystal deposition across different TCM syndromes. ResultsThere were statistically significant inter-group differences in the proportion of males， age， body mass index， proportion of mental labor， rate of low water intake， and rate of high-sugar beverage consumption （P<0.05），whereas no significant difference in low exercise intensity was found between the two groups. Furthermore， compared with the negative group， the positive group had higher serum uric acid level， neutrophil-to-lymphocyte ratio （NLR）， and platelet-to-lymphocyte ratio （PLR）， but lower systemic immune-inflammation index （SIRI） （P<0.05）. Regarding the distribution of TCM syndromes， the positive group was dominated by the dampness-heat accumulation syndrome （55/143，38.46%）， while the negative group was mainly characterized by the phlegm-turbidity obstruction syndrome （44/88，50.00%）. Multivariate Logistic regression analysis revealed that high-sugar beverage consumption， elevated NLR， and elevated PLR were risk factors for urate crystal deposition ［odd ratio （OR） = 8.002， 5.377， 1.034， respectively； 95% CI 1.572-40.732， 2.179-13.270， 1.013-1.054，all P<0.05］， while SIRI was a protective factor （OR = 0.869， 95% CI 0.778-0.971， P<0.05）. In the positive group， patients with the dampness-heat accumulation syndrome exhibited the highest NLR， while the lowest PLR and SIRI， showing statistically significant differences with those of other syndromes （all P<0.05）. In addition， ROC curve analysis indicated that for the dampness-heat accumulation syndrome， the combined "NLR + PLR" model had an area under the curve （AUC） of 0.901 （95% CI 0.850-0.951， P<0.01）， with a sensitivity of 89.1% and a specificity of 79.5%； for the blood stasis-heat obstruction syndrome， the combined "NLR + PLR" model had an AUC of 0.880 （95% CI 0.825-0.934， P<0.01）， with a sensitivity of 100.0% and a specificity of 67.3%； for the liver-kidney Yin-deficiency syndrome， the single PLR model had an AUC of 0.842 （95% CI 0.731-0.952， P<0.01）， with a sensitivity of 83.3% and a specificity of 84.0%. ConclusionUrate crystal deposition in HUA patients exhibits intimate associations with high-sugar beverage consumption as well as elevated NLR and PLR levels. Meanwhile， TCM syndrome differentiation has potential correlation with inflammatory characteristics. The inflammatory indicator-based prediction model constructed based on TCM syndromes exhibits good predictive value.

Objective: To explore the nonlinear dynamic effects of social capital, adverse childhood experiences (ACEs) and depressive symptoms on suicidal behavior among vocational high school students, so as to provide theoretical basis and practical references for formulating suicide prevention strategies. Methods: A convenience sampling method was employed to include 668 students from a vocational high school from Wuhan in March 2023. Social capital was used as the asymmetry variable, while ACEs and depressive symptoms were used as bifurcation variables, a cusp catastrophe model was constructed to analyze the nonlinear changes in suicidal behavior among vocational high school students, and its fit was compared with linear and Logistic regression models. Results: Among students in the health vocational high school in Wuhan, only suicidal ideation accounted for 8.5%, only suicide attempt for 18.6%, neither accounted for 31.9%, and both for 41.0%. Gender, left behind experience, family economic status, parental parenting styles, depressive symptoms, social capital, and ACEs were all related factors influencing suicidal behavior among vocational high school students ( χ 2/H=19.03, 13.33, 21.11, 46.70, 144.38, 24.61, 118.77, all P <0.05). Violin plots showed a bimodal distribution of suicidal behavior, indicating nonlinear variation characteristics. The cusp catastrophe model results showed that social capital was negatively correlated with suicidal behavior, but the relationship was bifurcated by ACEs ( α social capital = -0.006 , β ACEs =0.075) and depressive symptoms ( α social capital =-0.013, β depressive =0.028) (all P <0.05). When both ACEs and depressive symptoms coexisted, the impact of ACEs was stronger ( β ACEs =0.077, β depressive =0.014) (both P <0.05). The cusp catastrophe model fitted ( R 2=0.886, 0.881, 0.882) better than the linear ( R 2=0.258, 0.219, 0.258) and Logistic regression models ( R 2= 0.242, 0.211 , 0.176). Gender stratified analysis results showed that bifurcation effect of ACEs was stronger in males than in females( β boys =0.224, β girls =0.086); in females, both ACEs and depressive symptoms had a bifurcation effect, with the former showing a stronger effect ( β ACEs =0.062, β depressive =0.015) (all P <0.05). Conclusions Suicidal behavior among vocational high school students exhibits nonlinear characteristics. Improving social capital to reducing ACEs and depressive symptoms may contribute to decreasing adolescent suicidal behaviors.

This paper introduces a real-world cohort research model for community-acquired pneumonia （CAP） based on the Jiangsu Traditional Chinese Medicine （TCM） Dominant Diseases Diagnosis and Treatment Data Platform. Firstly， data cleaning is performed by standardizing diagnosis， symptoms， treatment and imaging， intelligently extracting unstructured information， and cleaning and constructing a standardized database. Secondly， for cohort establishment， CAP patients across the province are screened in accordance with CAP diagnostic criteria to build a high-quality disease-specific cohort. Lastly， in terms of protocol design， the characteristics of TCM research and the CAP disease profile are considered to determine appropriate inclusion and exclusion criteria， estimate sample size， define interventions， outcomes and economic evaluations， providing a reference for real-world TCM research on CAP.

This paper introduces a real-world cohort research model for community-acquired pneumonia （CAP） based on the Jiangsu Traditional Chinese Medicine （TCM） Dominant Diseases Diagnosis and Treatment Data Platform. Firstly， data cleaning is performed by standardizing diagnosis， symptoms， treatment and imaging， intelligently extracting unstructured information， and cleaning and constructing a standardized database. Secondly， for cohort establishment， CAP patients across the province are screened in accordance with CAP diagnostic criteria to build a high-quality disease-specific cohort. Lastly， in terms of protocol design， the characteristics of TCM research and the CAP disease profile are considered to determine appropriate inclusion and exclusion criteria， estimate sample size， define interventions， outcomes and economic evaluations， providing a reference for real-world TCM research on CAP.

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/Aims: Liver transplantation (LT) following total hepatectomy is a life-saving treatment for hepatocellular carcinoma (HCC). The HCC recurrence after LT hinders the effectiveness of the procedure. The objective of this study is to develop a pre-operative risk stratification model based on a liquid biopsy. Methods: We conducted a comprehensive multi-omics study of 260 HCC patients from three centers, including clinical data, low-coverage whole-genome sequencing of cell-free DNA (cfDNA) from plasma, as well as whole-exome, single-nucleus RNA, and spatial transcriptomics from matched tumor and non-tumor tissues. Results: We identified cfDNA-derived copy number alteration (CNA) signatures associated with post-transplant recurrence. By integrating cfDNA-derived CNA profiles with single-cell transcriptomic data, we traced recurrence-associated cfDNA to a distinct subpopulation of malignant cells within the primary tumor. These cells were embedded in a pro-metastatic microenvironment of specialized endothelial subtypes and cancer-associated fibroblasts. Notably, most recurrence-associated lesions were detectable in cfDNA prior to liver transplantation (LT). Building on these insights, we developed the ZJU Criteria based on CNA fragments and tumor markers, a pre-LT risk prediction tool that integrates conventional clinical factors with cfDNA-derived CNA signatures, and validated it using internal and independent external cohorts. Conclusion Our findings suggest that post-transplant recurrence commonly originates from advanced subclones that emerge late during tumor evolution. The ZJU Criteria provides an accurate, non-invasive strategy that significantly improves pre-LT risk stratification and clinical decision-making for patients with HCC.

Objective To analyze the core elements of standardized diagnosis of occupational carpal tunnel syndrome (OCTS). Methods Data of first reported OCTS case in Guangdong Province were retrospectively analyzed, including occupational exposure history, clinical manifestations, auxiliary examinations, and on-site occupational health investigation results. Furthermore, standardized diagnostic approaches for such occupational diseases were explored. Results The patient worked in a garment manufacturing enterprise, engaged in fabric cutting. The patient had a occupational history of at least three consecutive years of repetitive right wrist work, holding a 15.2 kg electric cutting tool for a cumulative 6.6 hours per day, with awkward wrist movements repeated 13 times per minute. Clinical symptoms included numbness and reduced muscle strength in the areas of median nerve distribution of the right hand, which were aggravated during work and relieved after rest. Physical examination showed inability of right hand to perform fine motor opposition movements of the thumb with the index, middle, and ring fingers; decreased tactile, pain, vibration, graphesthesia, and kinesthetic sensations below the right wrist; and positive Tinel′s sign over the median nerve at the wrist crease level, Phalen′s test, and reverse Phalen′s test of right wrist. Both wrist high-frequency color Doppler ultrasonography and nerve conduction studies and electromyography indicated damage to the right median nerve. However, only the findings from high-frequency color Doppler ultrasonography of the wrist meet the diagnostic criteria specified in GBZ 336-2025 Diagnostic Standard for Occupational Carpal Tunnel Syndrome. On-site occupational health investigation confirmed that the work involved typical repetitive wrist work, and three co-workers in similar positions exhibited comparable symptoms. The case was ultimately diagnosed as OCTS. Conclusion The definitive diagnosis of OCTS requires comprehensive evaluation of four core elements: a clear occupational exposure history, typical clinical symptoms and signs, characteristic objective examination findings, and quantitative on-site occupational health investigation data. Only when these four elements corroborate each other and non-occupational causative factors are excluded can a definitive diagnosis be made.

ObjectiveTo observe the effect and therapeutic effect of Kaixuan Jiedu core prescription （KXJD） on skin homing of Th17 cells in the mouse model of imiquimod （IMQ） combined with restraint stress-induced psoriasis-like skin damage， and to explore its potential mechanism from the perspective of neuro-immune axis. MethodsThirty male C57BL/6J mice were randomly allocated into five groups （n=6）： Control， model （IMQ）， restraint stress model （IMQ+RS）， KXJD， and methotrexate （MTX）. The mouse model of psoriasis-like skin damage was established by 5% IMQ combined with restraint stress. At the same time of modeling， each treatment group was treated with corresponding doses of drugs， and the control， IMQ， and IMQ+RS groups were treated with the same amount of normal saline by gavage once a day for 5 days. Hematoxylin-eosin （HE） staining was used to observe the pathological changes in the skin tissue and Baker scoring was performed. Serum levels of interleukin-1β （IL-1β） and angiopoietin-2 （Ang-2） were measured by enzyme-linked immunosorbent assay （ELISA）. The levels of matrix metalloproteinase-9 （MMP-9）， tissue inhibitor of metalloproteinase-1 （TIMP-1）， C-C motif chemokine ligand 20 （CCL20）， and C-C motif chemokine receptor 6 （CCR6） in the skin tissue were determined. Immunohistochemistry （IHC） was employed to determine the protein expression of cutaneous lymphocyte-associated antigen （CLA）， integrin α_E （CD103）， cytokeratin 10 （CK10）， and nuclear factor-kappa B （NF-κB） in the skin. Immunofluorescence double staining （DIF） was adopted to detect the expression and co-localization of vascular endothelial cadherin （VE-cadherin） and platelet-endothelial cell adhesion molecule （CD31）， CCR6， CD103， substance P （SP）， calcitonin gene-related peptide （CGRP）， and protein gene product 9.5 （PGP9.5） in the skin tissue. Real-time PCR was employed to quantify the mRNA levels of IL-10， IL-17A， and IL-23. ResultsCompared with the control group， the IMQ group and IMQ+RS group showed significant inflammatory cell infiltration， abnormal proliferation of epidermal cells， keratinization and other pathological changes in the skin tissue， and a significant increase in Baker score， elevated levels of IL-1β and Ang-2 in the serum and MMP-9， CCL20 and CCR6 in the skin lesions， upregulated expression of CLA， CD103， CK10， NF-κB， IL-17A mRNA， and IL-23 mRNA in the skin lesions， and downregulated expression of TIMP-1 and IL-10 mRNA. In addition， the fluorescence intensities of VE-cadherin and CD31 co-localization， CCR6 and CD103 co-localization， SP and PGP9.5 co-localization， and CGRP and PGP9.5 co-localization were increased （P<0.05）. Compared with the IMQ group， the above indicators in the IMQ+RS group were further aggravated. Compared with the IMQ group， the above indicators in the IMQ+RS group were further aggravated. Compared with the IMQ+RS group， KXJD and MTX significantly alleviated the pathological damage of skin lesions， significantly decreased the Baker score， lowered the levels of IL-1β and Ang-2 in the serum and MMP-9， CCL20 and CCR6 in skin lesions， downregulated the expression of CLA， CD103， CK10， NF-κB， IL-17A mRNA and IL-23 mRNA in skin lesions， and upregulated the expression of TIMP-1 and IL-10 mRNA. Furthermore， KXJD and MTX reduced the fluorescence intensities of VE-cadherin and CD31 co-localization， CCR6 and CD103 co-localization， CGRP and PGP9.5 co-localization， and SP and PGP9.5 co-localization （P<0.05）. ConclusionKXJD can significantly ameliorate the psoriasis-like skin damage induced by IMQ combined with restraint stress in mice by regulating the neural-immune axis and inhibiting the skin homing of Th17 cells.

ObjectiveTo establish the mouse models of psoriasis-like lesions induced by continuous cold exposure or intermittent cold exposure combined with imiquimod （IMQ）， and to evaluate the interventional effects of Kaixuan Jiedu core prescription （KXJD） on the two models. MethodsMale C57BL/6J mice were selected and classified into two experimental batches. The first batch of 36 mice was randomized into a room temperature group， a continuous cold exposure （10 ℃/24 h） group， and an intermittent cold exposure （10 ℃/6 h） group. Each group was further divided into a normal subgroup and a model subgroup （topical application of IMQ to induce skin lesions）， with 6 mice in each subgroup， for modeling and evaluation. The second batch of 54 mice， with 6 in each group， were subjected to the same temperature grouping with an additional KXJD （30.42 g·kg^-1， continuous gavage for 5 days） group. Comprehensive evaluation of model characteristics and KXJD efficacy was conducted through Psoriasis Area and Severity Index （PASI） scoring， skin temperature measurement by infrared thermography， histopathological observation by hematoxylin-eosin （HE） staining， detection of vascular endothelial growth factor （VEGF） and platelet endothelial cell adhesion molecule 1 （CD31） by immunohistochemistry， detection of Claudin-1 and Occludin by immunofluorescence assay， determination of serum levels of tumor necrosis factor-α （TNF-α） and interleukin （IL）-10 by enzyme-linked immunosorbent assay （ELISA）， and quantification of mRNA levels of IL-17A， IL-23， IL-6， and chemokine ligand 20 （CCL20） in skin lesions by quantitative Real-time polymerase chain reaction （Real-time PCR）. ResultsModel mice in all temperature groups exhibited typical psoriasis-like skin lesions. Compared with the normal groups， the model groups showed increased PASI scores， decreased skin temperatures （P<0.05）， obvious epidermal thickening， parakeratosis， and dermal inflammatory cell infiltration， as well as elevated mRNA levels of IL-17A， IL-23， IL-6， and CCL20 （P<0.05）. Cold exposure further aggravated psoriasis. The total PASI score of the intermittent cold exposure model group was higher than that of the room temperature model group （P<0.05）. The serum IL-10 did not show a compensatory elevation， and the blood vessels presented a characteristic of elevated CD31 expression （P<0.05） without a synchronous increase in VEGF. The continuous cold exposure model group exhibited more significant dermal capillary tortuosity and dilation， with the highest mRNA levels of IL-17A， IL-23， IL-6， and CCL20 among all groups. Compared with the respective model groups， KXJD intervention alleviated skin lesions， reduced epidermal thickness and inflammatory cell infiltration， and increased skin temperature， with the temperature increase being particularly significant in the intermittent cold exposure+KXJD group （P<0.05）. Furthermore， KXJD down-regulated the expression of VEGF and CD31， restored the expression of Claudin-1 and Occludin， decreased the mRNA levels of IL-17A and IL-23 （P<0.05）， and reduced the serum TNF-α level. ConclusionThis study successfully established compound psoriasis-like mouse models induced by cold exposure combined with IMQ. It confirms that cold aggravates the severity of psoriasis by exacerbating the closure of Xuanfu （sweat pores）， microcirculation disorders， and immune imbalance. Moreover， different cold exposure patterns have distinct mechanism differences. Continuous cold exposure focuses on enhancing the inflammatory response via the IL-23/IL-17 axis and angiogenesis， simulating chronic aggravation under a long-term cold environment. Intermittent cold exposure tends to impair immune regulation and induce microvascular endothelial stress， corresponding to acute exacerbations caused by sudden temperature drops. KXJD can effectively alleviate psoriasis-like skin lesions under cold conditions by unblocking Xuanfu， regulating vasomotor function， and correcting abnormal immune-inflammatory responses.