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.

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.

Objective: To establish an accurate and rapid typing method for Rh typing of samples from patients who have received recent blood transfusions by utilizing the difference in osmotic fragility between fresh and old red blood cells. Methods: A lysing solution suitable for destroying old RBCs was prepared. Sixty-one samples collected in our hospital in 2024 with Rh typing of double groups were treated with the lysing solution to remove the old allogeneic red blood cells while preserving the patient's own fresh red blood cells, followed by repeat Rh typing tests. Results: For 61 samples with Rh typing in double groups, 41 were accurately detected identified through the red blood cell lysis method, yielding an identification rate of 67.21%. No significant difference was observed compared to the detection rate of the commonly used capillary centrifugation modified method (χ =0.103, P>0.05). Conclusion: The red blood cell lysis method provides a novel and rapid experimental approach for clinical use in processing Rh-typed samples that are of double groups, thereby offering a basis for Rh compatibility blood transfusion.

Objective To construct an index system for assessment of schistosomiasis transmission risk following natural disasters such as rainstorms, floods, earthquakes, mudslides, and landslides, so as to provide insights into rapid identification of schistosomiasis transmission risk post-disasters and formulation of targeted schistosomiasis control strategies. Methods An initial framework for the index system for assessment of schistosomiasis transmission risk following natural disasters was drafted through literature review, brainstorming, and focus group discussions. Two rounds of expert correspondence consultations were conducted using the Delphi method to refine and finalize the system, and the degrees of expert activeness, authority and endorse ment, and consensus were evaluated. In addition, the weights of each index were calculated using the analytic hierarchy process. Results A total of 18 experts participated in the consultation. The expert positive coefficients were 100.00% and 94.44% for two rounds of consultations, with authority coefficients of 0.92 and 0.94, respectively. The coefficients of coordination on the index importance, rationality and operability were 0.209, 0.185, 0.222 and 0.407, 0.214, 0.257 for two rounds of consultations, respectively, and all consistency tests were statistically significant (χ² = 246.771 to 505.278, all P values < 0.001). Following two rounds of expert consultations, an index system consisting of 6 first-level indicators, 15 second-level indicators, and 49 third-level indicators was ultimately constructed. In terms of first-level indicators, “disaster situation”, “previous epidemics”, “healthcare guarantee”, “response capacity” and “emergency recovery” had the highest weights, each at 18.18%. Regarding second-level indicators, “Schistosoma japonicum infections in animals”, “S. japonicum infections in snails” and “medical treatment” had the highest weights, each at 7.35%. In terms of third-level indicators, ten items had the highest weights, including “identification of schistosomiasis cases”, “detection of S. japonicum infections in wild feces”, “detection of S. japonicum infections in snails”, “reserves of schistosomiasis diagnostic/testing reagents and consumables”, “reserves of chemotherapy agents for human and animal schistosomiasis”, “reserves of cercariacides”, “periodical surveillance on schistosomiasis”, “identification of schistosomiasis transmission risk and timely response”, “normal provision of diagnosis and treatment services” and “post-disaster schistosomiasis surveillance”, each at 2.40%. Conclusion A scientific, systematic, and practical index system has been constructed for assessment of schistosomiasis transmission risk following natural disasters, which may provide insights into rapid post-disaster identification of schistosomiasis transmission risk, formulation of targeted schistosomiasis control strategies and optimization of resource allocation.

ObjectiveTo construct an automatic measurement model for palmar tilt and radial inclination suitable for traditional Chinese medicine （TCM） clinical scenarios， and to validate its accuracy and efficiency in TCM manipulative reduction settings. MethodsData on anteroposterior （AP） and lateral X-rays of distal radius fractures were collected from patients admitted to 18 TCM/ integrated TCM and western medicine hospitals in Jiangsu province between September 1st， 2023， and September 1st， 2024， via the Jiangsu Diagnosis and Treatment Big Data Platform for TCM Dominant Diseases. A medical image segmentation framework based on multi-scale feature fusion and edge-awareness was employed， combined with anatomical knowledge specific to TCM orthopedics， to optimize the feature extraction strategy of an artificial intelligence （AI） model. This framework enabled automatic segmentation of fracture regions and measurement of distal radius palmar tilt and radial inclination. The accuracy of the AI model in measuring radial inclination and volar tilt was validated， and the measurement time and average time gain rate of the AI model were compared to those of manual measurement. ResultsA total of 15,444 AP and lateral X-ray images of distal radius fractures were collected， and were divided into a training set （11,144 images， 5066 AP and 6078 lateral）， a validation set （3700 images， 1840 AP and 1860 lateral）， and an independent test set （600 images， 300 AP and 300 lateral） after preprocessing. In the measurement of 300 AP X-rays in the independent test set for radial inclination， when the degree error between AI measurement and manual measurement was ＜3° and ＜5°， AI measurement accuracy was 83% and 93%， respectively. In 300 lateral X-rays in the test set for palmar tilt， when AI measurements had an error of ＜3° and ＜5° compared to manual measurements， corresponding accuracy rate was 78% and 90%， respectively. For 50 X-ray images， AI measurement time was （1.37±0.05） min for radial inclination while manual measurement time was （22.57±2.52） min （P＜0.001）； in terms of palmar tilt， the AI measurement time was （1.33±0.14） min， shorter than （23.70±2.80） min for manual measurement time （P＜0.001）. Average time gain rates for manual and AI measurements were 93.93% and 94.39% respectively. ConclusionAn automatic measurement model for palmar tilt and radial inclination in distal radius fractures has been established， enabling more accurate and efficient assessment as well as providing a tool to support the quantitative evaluation of the efficacy of TCM manipulative reduction and large-sample clinical research.

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 explore the clinical value of combining high-resolution CT (HRCT) with serum markers in differentiating malignant solitary pulmonary nodules (SPN). Methods Ninety-two SPN patients admitted to Baoji People’s Hospital between January 2022 and January 2024 were included. According to pathological analysis, they were divided into a benign group (30 cases) and a malignant group (62 cases). The serum levels of TUBA1B and ACSL4 were detected. Diagnostic value was determined using the receiver operating characteristic curve. Kappa test was used for consistency evaluation. Results Significant differences were observed between the benign and malignant groups in HRCT imaging, including the number of cases with spiculation, lobulation, pleural indentation, calcification, and vascular convergence sign (P<0.05). The sensitivity, specificity, and accuracy of HRCT in diagnosing malignant SPN were 74.19%, 86.67%, and 78.26%, respectively. The consistency with clinical diagnosis was moderate, as indicated by a Kappa value of 0.552 (P<0.001). The expression level of TUBA1B in the serum of SPN patients in the malignant group was higher than that in the benign group (P<0.05), while the expression level of ACSL4 was lower than that in the benign group (P<0.05). The areas under the receiver operating characteristic curves of serum TUBA1B and ACSL4 in diagnosing malignant SPN were 0.840 (95%CI = 0.749-0.908) and 0.875 (95%CI = 0.790-0.935), respectively, with sensitivities of 74.19% and 75.81% and specificities of 73.33% and 86.67%, respectively. The sensitivity, specificity, and accuracy of HRCT combined with serum TUBA1B and ACSL4 in diagnosing malignant SPN were 96.77%, 63.33%, and 85.87%, respectively. The consistency with clinical diagnosis was high, as indicated by a Kappa value of 0.721 (P<0.001). The sensitivity and negative predictive value of HRCT combined with serum TUBA1B and ACSL4 in diagnosing malignant SPN were higher than those of individual diagnostic indicators (P<0.05). Conclusion HRCT combined with serum TUBA1B and ACSL4 has high value in the differentiation of benign and malignant SPN.

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.

ObjectiveTo investigate the ameliorative effect and potential mechanisms of Kaixuan Jiedu core prescription （KXJD） on imiquimod （IMQ）-combined restraint stress-induced psoriasis-like skin lesions in mice. MethodsA total of 36 C57BL/6J mice were randomly assigned to six groups （n=6 per group）： Normal group， restraint stress blank group， model group， restraint stress model group， KXJD group （30.42 g·kg^-1）， and methotrexate group （0.001 g·kg^-1）. Psoriasis-like lesions were induced by topical IMQ application combined with restraint stress. After 5 days of intervention， the severity of skin lesions was evaluated using the Psoriasis Area and Severity Index （PASI）. The following parameters were assessed： Hypothalamic-pituitary-adrenal （HPA） axis-related hormones， including adrenocorticotropic hormone （ACTH）， corticotropin-releasing hormone （CRH）， and corticosterone （CORT）， central neuronal markers， including neuronal nuclei antigen （NeuN）， microtubule-associated protein 2 （MAP2）， and beta Ⅲ-tubulin （β-Ⅲ tubulin）， microglial activation markers， including ionized calcium-binding adapter molecule-1 （Iba-1） and cluster of differentiation 11b （CD11b）， and skin nerve fiber markers， including protein gene product 9.5 （PGP9.5） and serotonin （5-HT）， as well as the expression levels of inflammatory cytokines tumor necrosis factor-alpha （TNF-α）， interleukin-6 （IL-6）， and interleukin-1 beta （IL-1β）. ResultsCompared with the normal group， both the model group and the restraint stress model group exhibited more severe psoriasis-like skin lesions， with significantly elevated serum levels of ACTH， CRH， and CORT （P<0.01）. The expression of glucocorticoid receptor （GR） and corticotropin-releasing hormone receptor 1 （CRHR1） in the skin was significantly downregulated. The expression of NeuN， MAP2， β-Ⅲ tubulin， Iba-1， and CD11b in the hypothalamus and hippocampus was significantly upregulated， suggesting abnormal changes in neuronal number or structure and aberrant activation of glial cells （P<0.05， P<0.01）. In addition， the expression of PGP9.5 and 5-HT in the skin， as well as the mRNA levels of TNF-α， IL-6， and IL-1β were all markedly elevated （P<0.05， P<0.01）. Compared with the model group， the restraint stress model group showed further exacerbation in most parameters. Compared with the restraint stress model group， the KXJD group exhibited significantly reduced PASI scores （P<0.01）， significantly decreased serum CRH， ACTH， and CORT levels （P<0.05， P<0.01）， upregulated skin GR and CRHR1 expression， inhibited abnormal neuronal changes and microglial activation in the hypothalamus and hippocampus， attenuated skin nerve fiber hyperplasia and amine signaling molecule release， and significantly reduced pro-inflammatory cytokine mRNA expression （P<0.05， P<0.01）. ConclusionKaixuan Jiedu Core prescription can effectively ameliorate IMQ combined with restraint stress-induced psoriasis-like skin lesions in mice by modulating HPA axis function， inhibiting central neuropathological changes， and attenuating skin nerve fiber hyperplasia， amine signaling molecule （5‑HT） release and local inflammatory responses.