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.

The initiation of adaptive immune responses relies on the precise recognition and interpretation of antigenic information. In this process, the specific binding of T cell receptors (TCRs) to peptide-major histocompatibility complex (pMHC) molecules represents one of the key molecular events in the initiation of adaptive immune responses. Accordingly, the structural features of TCR-pMHC complexes provide a fundamental basis for dissecting antigen recognition mechanisms and support rational vaccine design, therapeutic target discovery in TCR-based immunotherapy, and TCR identification and optimization. However, experimental determination of TCR-pMHC structures remains costly, time-consuming, and limited in coverage, making computational approaches essential for rapidly obtaining reliable structural information. Computational methods for predicting the structures of TCR-pMHC complexes have advanced rapidly in recent years, driven by progress in deep learning-based modeling frameworks and the increasing availability of structural and sequence resources. Despite these developments, most existing tools do not adequately distinguish the key structural and biophysical differences between MHC class I (MHC-I) and MHC class II (MHC-II) complexes during model construction. As a consequence, their predictive performance differs substantially between class I and class II complexes. In general, structural predictions for class I complexes outperform those for class II complexes. This discrepancy may be related to several fundamental differences between the two systems, including the architecture of the peptide-binding groove, the distribution of peptide lengths, and the properties of peptide flanking residues (PFRs). Compared with MHC-I molecules, MHC-II molecules usually bind longer antigenic peptides, which typically range from 13 to 25 amino acids in length. PFRs at both termini of these peptides participate in regulating the overall conformation of TCR-pMHC class II complexes and exert a pronounced effect on the geometric and physicochemical characteristics of the TCR-pMHC binding interface. Furthermore, within the TCR recognition interface, the complementarity-determining regions (CDRs) consist of segments that differ markedly in conformational behavior. They commonly include regions that are relatively rigid and structurally stable, together with highly flexible segments exhibiting substantial conformational plasticity. These rigidity-flexibility features constitute an essential structural basis enabling TCRs to recognize diverse peptide-MHC ligands and to accommodate conformational heterogeneity at the interface. However, many current modeling tools, in an effort to enforce global conformational stability or reduce structural noise, tend to over-constrain intrinsically flexible regions. Such oversimplification may lead to inappropriate rigidification of flexible CDR loops, resulting in local structural distortions, compromised interface geometry, or even complete modeling failure for specific complexes. Against this background, the review approaches the field from the perspective of computational differences between MHC-I and MHC-II complexes. We first systematically organize and summarize available resources related to TCRs and pMHCs, including structural datasets, sequence databases, prediction tools, and benchmarking studies. We then focus on five representative tools capable of predicting both class I and class II complexes—AlphaFold2, AlphaFold3, TCRmodel2, tFold-TCR, and TCR-pHLA_ModellerS. After excluding structures present in the training sets of these tools, we constructed a benchmark dataset comprising 25 class I and 10 class II TCR-pMHC complexes in the bound state and conducted a systematic evaluation using this dataset. We first employ widely used general evaluation metrics, including All-Atom Root Mean Square Deviation (All-Atom RMSD), Backbone RMSD, Template Modeling score (TM-score), and DockQ, to assess the global conformational accuracy and interface modeling quality of class I and class II complexes. For class II complexes, we propose for the first time a peptide flanking residue deviation index, including the PFRs-Deviation Index (PFRs-DI), N-PFR-Deviation Index (N-PFR-DI), and C-PFR-Deviation Index (C-PFR-DI), to quantitatively characterize conformational deviations in PFRs. In addition, we propose the CDR conformational consistency index (CCC) designed to qualitatively evaluate the ability of prediction tools to capture TCR CDR conformational flexibility. These metrics collectively assess a tool’s ability to model both overall conformation and critical functional regions, thereby addressing the limitations of existing evaluation criteria that overemphasize global structure while inadequately capturing modeling quality in key functional areas. This establishes a unified analytical framework for MHC-I and MHC-II complexes to guide data resource selection, modeling strategy formulation, and evaluation system development. The framework further advances computational modeling and provides crucial support for multi-scale analysis of TCR-pMHC recognition mechanisms and their biological functions.

The initiation of adaptive immune responses relies on the precise recognition and interpretation of antigenic information. In this process, the specific binding of T cell receptors (TCRs) to peptide-major histocompatibility complex (pMHC) molecules represents one of the key molecular events in the initiation of adaptive immune responses. Accordingly, the structural features of TCR-pMHC complexes provide a fundamental basis for dissecting antigen recognition mechanisms and support rational vaccine design, therapeutic target discovery in TCR-based immunotherapy, and TCR identification and optimization. However, experimental determination of TCR-pMHC structures remains costly, time-consuming, and limited in coverage, making computational approaches essential for rapidly obtaining reliable structural information. Computational methods for predicting the structures of TCR-pMHC complexes have advanced rapidly in recent years, driven by progress in deep learning-based modeling frameworks and the increasing availability of structural and sequence resources. Despite these developments, most existing tools do not adequately distinguish the key structural and biophysical differences between MHC class I (MHC-I) and MHC class II (MHC-II) complexes during model construction. As a consequence, their predictive performance differs substantially between class I and class II complexes. In general, structural predictions for class I complexes outperform those for class II complexes. This discrepancy may be related to several fundamental differences between the two systems, including the architecture of the peptide-binding groove, the distribution of peptide lengths, and the properties of peptide flanking residues (PFRs). Compared with MHC-I molecules, MHC-II molecules usually bind longer antigenic peptides, which typically range from 13 to 25 amino acids in length. PFRs at both termini of these peptides participate in regulating the overall conformation of TCR-pMHC class II complexes and exert a pronounced effect on the geometric and physicochemical characteristics of the TCR-pMHC binding interface. Furthermore, within the TCR recognition interface, the complementarity-determining regions (CDRs) consist of segments that differ markedly in conformational behavior. They commonly include regions that are relatively rigid and structurally stable, together with highly flexible segments exhibiting substantial conformational plasticity. These rigidity-flexibility features constitute an essential structural basis enabling TCRs to recognize diverse peptide-MHC ligands and to accommodate conformational heterogeneity at the interface. However, many current modeling tools, in an effort to enforce global conformational stability or reduce structural noise, tend to over-constrain intrinsically flexible regions. Such oversimplification may lead to inappropriate rigidification of flexible CDR loops, resulting in local structural distortions, compromised interface geometry, or even complete modeling failure for specific complexes. Against this background, the review approaches the field from the perspective of computational differences between MHC-I and MHC-II complexes. We first systematically organize and summarize available resources related to TCRs and pMHCs, including structural datasets, sequence databases, prediction tools, and benchmarking studies. We then focus on five representative tools capable of predicting both class I and class II complexes—AlphaFold2, AlphaFold3, TCRmodel2, tFold-TCR, and TCR-pHLA_ModellerS. After excluding structures present in the training sets of these tools, we constructed a benchmark dataset comprising 25 class I and 10 class II TCR-pMHC complexes in the bound state and conducted a systematic evaluation using this dataset. We first employ widely used general evaluation metrics, including All-Atom Root Mean Square Deviation (All-Atom RMSD), Backbone RMSD, Template Modeling score (TM-score), and DockQ, to assess the global conformational accuracy and interface modeling quality of class I and class II complexes. For class II complexes, we propose for the first time a peptide flanking residue deviation index, including the PFRs-Deviation Index (PFRs-DI), N-PFR-Deviation Index (N-PFR-DI), and C-PFR-Deviation Index (C-PFR-DI), to quantitatively characterize conformational deviations in PFRs. In addition, we propose the CDR conformational consistency index (CCC) designed to qualitatively evaluate the ability of prediction tools to capture TCR CDR conformational flexibility. These metrics collectively assess a tool’s ability to model both overall conformation and critical functional regions, thereby addressing the limitations of existing evaluation criteria that overemphasize global structure while inadequately capturing modeling quality in key functional areas. This establishes a unified analytical framework for MHC-I and MHC-II complexes to guide data resource selection, modeling strategy formulation, and evaluation system development. The framework further advances computational modeling and provides crucial support for multi-scale analysis of TCR-pMHC recognition mechanisms and their biological functions.

ObjectiveTo explore the mechanism of action of Paeoniae Radix Rubra and Aconiti Lateralis Radix Praeparata （CSFZ） in the treatment of acute-on-chronic liver failure （ACLF） through network pharmacology， molecular docking， and animal experiments. MethodsNetwork pharmacology was used to identify potential targets and related signaling pathways for the treatment of ACLF with CSFZ. Molecular docking was used to examine the binding activity of the core components with corresponding key targets. An ACLF rat model was established by subcutaneous and tail vein injections of bovine serum albumin combined with lipopolysaccharide （LPS） + D-galactosamine （D-GalN） intraperitoneal injection. A normal control group （NC）， a model group， a CSFZ group （CSFZ， 5.85 g·kg^-1）， and a hepatocyte growth-promoting granule group （HGFG， 4.05 g·kg^-1） were set up in this study. Pathological changes in rat liver tissue were observed using hematoxylin and eosin （HE） and Masson staining. Enzyme-linked immunosorbent assay （ELISA） was used to detect the expression levels of interleukin-6 （IL-6）， B-cell lymphoma-2 （Bcl-2）， Caspase-3， and albumin （ALB）. Real-time quantitative polymerase chain reaction （Real-time PCR） and Western blot were used to measure the mRNA and protein expression levels of phosphoinositide 3-kinase （PI3K）， protein kinase B （Akt）， phosphorylated PI3K （p-PI3K）， and phosphorylated Akt （p-Akt）. ResultsNetwork pharmacology screening identified 49 active ingredients of CSFZ， 103 action targets， and 3 317 targets related to ACLF. Among these， 74 targets overlapped with CSFZ drug targets. Key nodes in the protein-protein interaction （PPI） network included Akt1， tumor necrosis factor （TNF）， IL-6， Bcl-2， and Caspase-3. Gene Ontology （GO） functional analysis and Kyoto Encyclopedia of Genes and Genomes （KEGG） enrichment analysis identified multiple signaling pathways， with the PI3K/Akt signaling pathway being the most frequent. Molecular docking showed that the core components of the drug exhibited good binding activity with the corresponding key targets. Animal experiments confirmed that CSFZ significantly improved liver tissue pathological damage in ACLF rats， reduced the release of inflammatory factors and liver cell apoptosis， and upregulated the expression levels of the PI3K/Akt signaling pathway. ConclusionThrough network pharmacology， molecular docking， and in vivo experiments， this study confirms the effect of CSFZ in reducing liver cell inflammatory damage and inhibiting liver cell apoptosis. The specific mechanism may be related to its involvement in regulating the PI3K/Akt signaling pathway.

ObjectiveTo study the effect and mechanism of Shentong Zhuyutang in treating intervertebral disc degeneration （IDD） in rats. MethodsIn the cell experiment， male rats were administrated with normal saline or low-， medium-， and high-dose （3.38， 6.75，13.5 g·kg^-1， respectively） Shentong Zhuyutang by gavage， respectively， and serum samples were collected after 7 days of continuous administration. Another 10 male rats were selected for the isolation of nucleus pulposus cells. The cell model of IDD was established by treatment with interleukin （IL）-1β. The modeled cells were then treated with Shentong Zhuyutang-containing serum and the ferroptosis inhibitor ferrostatin-1 （Fer-1）， respectively， to investigate the effects of Shentong Zhuyutang-containing serum on the proliferation and ferroptosis of nucleus pulposus cells. To study the role of silent information regulator 1 （SIRT1）/nuclear factor erythroid 2-related factor 2 （Nrf2） in the regulation of ferroptosis in nucleus pulposus cells by Shentong Zhuyutang-containing serum， this study treated the cells with the SIRT1 inhibitor Ex 527 and the Nrf2 inhibitor ML385， respectively， in addition to the treatment with IL-1β and high-dose Shentong Zhuyutang-containing serum. The cell-counting kit-8 （CCK-8） assay and EdU staining were employed to measure the cell viability and proliferation， respectively. The Fe²⁺， glutathione （GSH）， and malondiadehyde （MDA） levels were measured by colorimetric assay. Western blot was employed to determine the protein levels of glutathione peroxidase 4 （GPX4）， acyl-CoA synthetase long-chain family 4 （ACSL4）， Collagen Ⅱ， Aggrecan， SIRT1， and Nrf2. Immunofluorescence was used detect SIRT1 expression. In the animal experiment， male rats were treated with anulus puncture for the modeling of IDD. Rats were randomly assigned into sham operation， model， Shentong Zhuyutang-containing serum （13.5 g·kg^-1）， and positive control （nimesulide dispersible tablets， 0.18 mg·kg^-1） groups. Rats in the drug intervention groups were administrated with corresponding agents at 1 mL·kg^-1， and those in the sham operation and model groups were administrated with equal volumes of normal saline， once daily for 28 consecutive days. At the end of the last administration， the histopathological changes in the intervertebral discs of rats were observed by hematoxylin-eosin staining and scored by the Masuda method. Western blot was employed to determine the protein levels of SIRT1， Nrf2， GPX4， and Collagen Ⅱ in the nucleus pulposus tissue. ResultsCompared with the control group， the IL-1β group of nucleus pulposus cells showed elevated levels of Fe²⁺， MDA， and ACSL4 （P<0.05）， decreased cell viability， lowered GSH level， and down-regulated protein levels of GPX4， Collagen Ⅱ， and Aggrecan （P<0.05）. Shentong Zhuyutang-containing serum and Fer-1 reversed the effects of IL-1β on the viability and ferroptosis of nucleus pulposus cells and up-regulated the protein levels of Collagen Ⅱ and Aggrecan in nucleus pulposus cells （P<0.05）. Compared with the control group， the IL-1β group showcased down-regulated expression of Sirt1 and Nrf2 in nucleus pulposus cells （P<0.05）. Compared with the IL-1β group， the high-dose Shentong Zhuyutang-containing serum+IL-1β group showed up-regulated expression of SIRT1 and Nrf2 in nucleus pulposus cells （P<0.05）. Compared with the high-dose Shentong Zhuyutang-containing serum+IL-1β group， the ML385 group showed down-regulated protein levels of Nrf2 and GPX4， lowered GSH level， and elevated Fe²⁺ and MDA levels （P<0.05）. In addition， the Ex 527 group showed down-regulated protein levels of SIRT1， Nrf2， and GPX4 （P<0.05）. The results of the animal experiment showed that compared with the sham operation group， the model group had severe degeneration of the intervertebral disc tissue with increased pathological score， up-regulated protein level of ACSL4 （P<0.05）， and down-regulated protein levels of SIRT1， Nrf2， GPX4， and Collagen Ⅱ （P<0.05）. Compared with the model group， the Shentong Zhuyutang group showed alleviated IDD with declined pathological score， down-regulated protein level of ACSL4 （P<0.05）， and up-regulated protein levels of SIRT1， Nrf2， GPX4， and Collagen Ⅱ （P<0.05）. ConclusionShentong Zhuyutang may activate the SIRT1/Nrf2 signaling pathway to inhibit the ferroptosis of nucleus pulposus cells， thereby delaying the process of IDD in rats.

The holistic view is the key in the study of traditional Chinese medicine(TCM). The component structure theory is based on the holistic view to investigate the correlation between material basis and efficiency, which enriches the holistic "component-effect" research of TCM. Gintonin is a newly isolated non-saponin component of ginseng. Compared to ginsenosides, gintonin has many different pharmacological activities, and it provides new knowledge for the holistic research of ginseng. Thus, taking the discovery of gintonin from ginseng as an example, this paper explored the linkage between ginsenosides and gintonin from the perspective of "component-effect" correlations and systematically sorted out the similarities and differences between them in terms of structural characteristics, modes of action, and pharmacological activities. Starting from the collaborative interaction of TCM compounds, the study discussed the application and value of the holistic view in TCM "component-effect" research in the light of the component structure theory to provide new thoughts for the development of modern TCM research.
Panax/chemistry* ; Drugs, Chinese Herbal/pharmacology* ; Medicine, Chinese Traditional ; Humans ; Ginsenosides/pharmacology* ; Animals

As a new type of production factor that can empower the development of new quality productivity, the data element is an important engine to promote the high quality development of the industry. Traditional Chinese medicine(TCM) dispensing is the most basic work of TCM clinical pharmacy, and its quality directly affects the clinical efficacy of TCM. The integration of data elements and TCM dispensing can stimulate the innovation and vitality of the TCM dispensing industry and promote the high-quality and sustainable development of the industry. A large-scale, detailed, and systematic study on TCM dispensing was conducted. The innovative practice path of data fusion construction in the whole process of TCM dispensing was investigated by integrating the digital resources "nine full activities" of TCM dispensing, creating the digital dictionary of "TCM clinical information data elements", and exploring innovative applications of TCM dispensing driven by data and technology, so as to promote the standardized, digital, and intelligent development of TCM dispensing in medical health services. The research content of this project was successfully selected as the second batch of "Data element×" typical cases of National Data Administration in 2024, which is the only selected case in the field of TCM.
Medicine, Chinese Traditional/methods* ; Drugs, Chinese Herbal ; Humans

Autoimmune hepatitis （AIH） is a chronic inflammatory liver disease. The pathogenesis of AIH remains unclear， but it is mainly autoimmune injury caused by the breakdown of autoimmune tolerance due to the abnormal activation of the immune system， while the specific molecular mechanism remains unknown. Recent studies have shown that abnormal amino acid metabolism plays an important role in the development and progression of AIH. This article reviews the research advances in amino acid metabolic reprogramming in AIH， in order to provide a theoretical basis for amino acid metabolism as a new target for the clinical diagnosis and treatment of AIH.