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 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.

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 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.

6-Thioguanine(6-TG)is an antineoplastic agent used in treatment of acute leukemia.However,significant interindividual variability in dosing regimens and frequent clinical manifestations of hepatotoxicity and myelosuppression as adverse effects have affected its therapeutic efficacy.Consequently,the development of rapid analytical methods for 6-TG in clinical samples,enabling continuous therapeutic drug monitoring of plasma concentrations,holds substantial significance in optimizing dosage regimens,mitigating adverse reactions,and investigating drug metabolism mechanisms.In this study,multi-tipped gold nanostars(AuNSs)were prepared.With bis-(p-sulfonylphenyl)phenylphosphine molecule as the protecting agent and internal standard molecule,the AuNSs were assembled onto a highly sensitive surface-enhanced Raman(SERS)substrate for developing a ratio-based SERS quantitative analysis method for 6-TG in serum.The AuNSs containing multiple tips and gaps exhibited strong local surface plasmon resonance effect and SERS activity,ensuring the sensitivity of the analytical method.Furthermore,the introduction of internal standard molecules could improve the reproducibility,which guaranteed this method suitable for rapid analysis of drug molecules in complex samples.Quantitative analysis of 6-TG was achieved with linear detetion range of 1.0×10?4-1.0 mmol/L.In the spiked recovery experiments of serum,the RSD was less than 5.32%,and the recoveries were 94%-104%,which proved that this method could be used for rapid quantitative determination of 6-TG in serum.This method provided a powerful tool for studying drug pharmacokinetics,which could promote the optimization of the usage methods of anti-cancer drugs,and it was expected to further enhance the clinical efficacy and safety of 6-TG,enabling it to achieve the best therapeutic effect.

Objective To compare the application effects of plankton multiplex polymerase chain reac-tion-capillary electrophoresis(PCR-CE),SYBR Green Ⅰ real-time quantitative PCR(qPCR)and microwave digestion-vacuum filtration-automated scanning electron microscopy(MD-VF-Auto SEM)in the diagnosis of drowning.Methods Lung,liver and kidney tissues from 212 drowned corpses and 30 non-drowned corpses were examined respectively by the three drowning-related plankton testing methods,and the detection rates of plankton in each tissue by three methods were compared.Results In drowned corpses,the total detection rates of PCR-CE,qPCR,and MD-VF-Auto SEM were 93.9%,96.2%,and 95.3%,respectively,with no statistically significant difference(P>0.05).The detection rate of lung tissue by MD-VF-Auto SEM(100%)was higher than those of PCR-CE and qPCR(P<0.05),and there was no significant difference in the detection rates of the three methods in liver or kidney tissues(P>0.05).In non-drowning corpses,a small number of diatoms(less than 10 cells/10 g)were detected by MD-VF-Auto SEM method,only in liver and kidney tissues,while the other two methods yielded negative results for all tissues.Conclusion All three methods have good efficacy in the examination of drowned corpses.The MD-VF-Auto SEM method directly observes diatom morpho-logical characteristics through scanning electron microscopy,and the qualitative and quantitative analy-ses are intuitive and accurate.It has great advantages in the examination of difficult degradation samples.The PCR-CE method and qPCR method have a low sample demand(0.5 g),are easy to operate and have short detection time(4-7 h).They are easy to be applied in the grassroots depart-ments and are suitable for the rapid determination of drowned corpses in routin cases.The combina-tion of the two DNA methods with the MD-VF-Auto SEM method can increase the detection rate of plankton,ensuring the reliability of examination results.This combined use is of significant importance in the application of drowning diagnosis.