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.

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 As climate change intensifies and extreme temperature events become more frequent, non-optimal temperature has emerged as a significant contributor to the global disease burden, representing a pressing public health challenge. Objective To analyze the disease burden, temporal trends, and health inequalities attributable to non-optimal, high, and low temperatures in China from 1990 to 2021, and to compare these findings with global levels to provide a scientific basis for targeted prevention strategies. Methods Using data from the Global Burden of Disease 2021 (GBD 2021), we extracted mortality rates and disability-adjusted life year (DALY) rates, and other indicators attributable to non-optimal, high, and low temperatures by sex, age, region, and cause. Joinpoint regression was applied to examine temporal trends. Decomposition analysis identified driving factors of change, while the slope index of inequality (SII) and concentration index (CI) quantified disparities across socio-demographic index (SDI) levels. Results From 1990 to 2021, the age-standardized mortality rates (ASMR) and age-standardized DALY rates (ASDR) attributable to non-optimal temperature in China exhibited a downward trend, decreasing from 66.48 (95%UI: 58.09, 76.56) to 32.70 (95%UI: 27.26, 39.26) per 100000 population, and from 1219.59 (95%UI: 1056.28, 1418.37) to 493.22 (95%UI: 403.88, 609.32) per 100000 population, respectively. Burdens attributable to non-optimal temperature and low temperature were higher than the global average, whereas the high temperature burden was lower. Males consistently experienced higher ASMR and ASDR attributable to non-optimal temperature than females. Cardiovascular diseases, chronic respiratory diseases, and respiratory infections and tuberculosis were the top three causes of non-optimal temperature-attributable burdens. Decomposition analysis revealed that population aging and growth were the primary drivers of increased burden, while epidemiological changes primarily drove the decline. Health inequalities were most predominant between extreme SDI regions but narrowed over time. Conclusion Despite the overall decline in burden attributable to non-optimal temperature in China, significant challenges remain, including high risks from cold exposure, gender disparities, and the compounding effects of an aging population with cardiovascular or respiratory diseases. Policy makers should prioritize climate change adaptation, focusing on elderly health and regional equity while strengthening the public health workforce.

Background As climate change intensifies and extreme temperature events become more frequent, non-optimal temperature has emerged as a significant contributor to the global disease burden, representing a pressing public health challenge. Objective To analyze the disease burden, temporal trends, and health inequalities attributable to non-optimal, high, and low temperatures in China from 1990 to 2021, and to compare these findings with global levels to provide a scientific basis for targeted prevention strategies. Methods Using data from the Global Burden of Disease 2021 (GBD 2021), we extracted mortality rates and disability-adjusted life year (DALY) rates, and other indicators attributable to non-optimal, high, and low temperatures by sex, age, region, and cause. Joinpoint regression was applied to examine temporal trends. Decomposition analysis identified driving factors of change, while the slope index of inequality (SII) and concentration index (CI) quantified disparities across socio-demographic index (SDI) levels. Results From 1990 to 2021, the age-standardized mortality rates (ASMR) and age-standardized DALY rates (ASDR) attributable to non-optimal temperature in China exhibited a downward trend, decreasing from 66.48 (95%UI: 58.09, 76.56) to 32.70 (95%UI: 27.26, 39.26) per 100000 population, and from 1219.59 (95%UI: 1056.28, 1418.37) to 493.22 (95%UI: 403.88, 609.32) per 100000 population, respectively. Burdens attributable to non-optimal temperature and low temperature were higher than the global average, whereas the high temperature burden was lower. Males consistently experienced higher ASMR and ASDR attributable to non-optimal temperature than females. Cardiovascular diseases, chronic respiratory diseases, and respiratory infections and tuberculosis were the top three causes of non-optimal temperature-attributable burdens. Decomposition analysis revealed that population aging and growth were the primary drivers of increased burden, while epidemiological changes primarily drove the decline. Health inequalities were most predominant between extreme SDI regions but narrowed over time. Conclusion Despite the overall decline in burden attributable to non-optimal temperature in China, significant challenges remain, including high risks from cold exposure, gender disparities, and the compounding effects of an aging population with cardiovascular or respiratory diseases. Policy makers should prioritize climate change adaptation, focusing on elderly health and regional equity while strengthening the public health workforce.

ObjectiveThis paper aims to analyze the damage degree of muscle tone in rats with spasticity of cerebral apoplexy （SCA） and the expression of Nestin and β-catenin in the M1 region of the cerebral cortex， thereby investigating the action mechanism of different doses of Shaoyao Gancaotang on rats with SCA. MethodsThe rats were randomly divided into a blank group， a model group， a positive control group （baclofen， 5.25 mg·kg^-1）， and low， medium， and high-dose groups of Shaoyao Gancaotang （2.1， 4.2， 8.4 g·kg^-1）， with nine rats in each group. A rat model with SCA was established by using a modified phrenic nerve block combined with intraventricular injection of anhydrous ethanol. Following behavioral scoring to confirm model validity， drug interventions were conducted. Neurological deficits and muscle tone were evaluated by behavioral assessments. The open field test was used to measure locomotor distance. Transmission electron microscopy was employed to examine the synaptic structures. Skeletal muscle adenosine triphosphate （ATP）ase staining was used to analyze myofibrillar changes. Hematoxylin and eosin （HE） staining was used to observe the histomorphological changes. Immunohistochemistry， Real-time polymerase chain reaction （Real-time PCR）， and Western blot were employed to detect mRNA levels and protein expressions of Nestin and β-catenin in the M1 region of the cerebral cortex. ResultsCompared with the blank group， rats in the model group exhibited significantly increased neurological deficit scores （P<0.01）， markedly elevated muscle tone scores （P<0.01）， substantially reduced locomotor distance （P<0.01）， prominent structural swelling and blurring， severe destruction of cerebral cortical cells， a significant increase in the proportion of skeletal muscle ATPase type Ⅰ fibers （P<0.01）， a significant decrease in mRNA levels and protein expression of Nestin （P<0.01）， and a significant increase in mRNA levels and protein expression of β-catenin （P<0.01）. Compared with the model group， the Shaoyao Gancaotang group exhibited reduced neurological deficit scores and muscle tone scores in rats with SCA （P<0.01） and increased locomotor distance （P<0.01）. Transmission electron microscopy revealed clearer and more intact synaptic structures in the rats from the Shaoyao Gancaotang group， with increased vesicle numbers and improved morphology. HE staining revealed intact neuronal cell structures with regular arrangement and reduced vacuolated cells in the rats from Shaoyao Gancaotang. ATPase staining result indicated a decreased proportion of type Ⅰ muscle fibers in the rats from the Shaoyao Gancaotang group （P<0.01）. Real-time PCR results demonstrated increased mRNA expressions of Nestin and β-catenin in the rats from the Shaoyao Gancaotang group （P<0.01）. Immunohistochemistry and western blot analyses indicated elevated protein expressions of Nestin and β-catenin in rats with SCA from the Shaoyao Gancaotang group （P<0.05， P<0.01）. ConclusionShaoyao Gancaotang may improve neurological function impairment and limb spasticity in model rats with SCA by regulating the proliferation and differentiation of neural stem cells in the cerebral cortex M1 region.

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.

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.

This paper outlines the development of artificial intelligence （AI） and its applications in traditional Chinese medicine （TCM） research， and elucidates the roles and advantages of large language models， knowledge graphs， and natural language processing in advancing syndrome identification， prescription generation， and mechanism exploration. Using spleen-stomach diseases as an example， it demonstrates the empowering effects of AI in classical literature mining， precise clinical syndrome differentiation， efficacy and safety prediction， and intelligent education， highlighting an upgraded research paradigm that evolves from data-driven and knowledge-driven approaches to intelligence-driven models. To address challenges related to privacy protection and regulatory compliance in cross-institutional data collaboration， a "trusted federated evidence-based analysis platform for TCM spleen-stomach diseases" is proposed， integrating blockchain-based smart contracts， federated learning， and secure multi-party computation. The deep integration of AI with privacy-preserving computing is reshaping research and clinical practice in TCM spleen-stomach diseases， providing feasible pathways and a technical framework for building a high-quality， trustworthy TCM big-data ecosystem and achieving precision syndrome differentiation.