BACKGROUND:The inflammatory response of microglia is closely related to neuronal survival,regeneration,and functional recovery after spinal cord injury.Peroxiredoxin 1 is not only involved in the regulation of oxidative stress,but also has an important effect on cell proliferation,apoptosis,and inflammatory response.OBJECTIVE:To investigate the role and mechanism of peroxiredoxin 1 in the inflammatory response of microglia following spinal cord injury.METHODS:(1)Twelve female C57BL/6 mice were randomly divided into sham-operated(n=6)and spinal cord injury(n=6)groups.The sham-operated group was not modeled and acute spinal cord injury models were constructed in the spinal cord injury group using the modified Allen's method.Spinal cord tissue at the injured site was taken at 7 days after modeling and transcriptome sequencing was performed to identify differentially expressed genes.The expression of peroxiredoxin 1 in spinal cord tissues was verified using western blot and RT-qPCR.(2)Mouse microglia BV2 were divided into two groups:the control group was stimulated with lipopolysaccharide for 6 hours,and in the knockout group,lipopolysaccharide stimulation was applied for 6 hours at 24 hours after peroxiredoxin 1 was knocked down in the cells.RT-qPCR was performed to detect mRNA expression of peroxiredoxin 1,inflammatory factors(interleukin 1β,interleukin 6,inducible nitric oxide synthase,tumor necrosis factor α,C-C motif chemokine ligand 2,and C-X-C motif chemokine ligand 2),and western blot was performed to detect the expression of peroxiredoxin 1,inducible nitric oxide synthase,and reactive oxygen/mitogen-activated protein kinase signaling pathway proteins.Mouse microglia BV2 were treated in two groups:the control group was stimulated by hydrogen peroxide for 4 hours,and the knockout group was stimulated by hydrogen peroxide for 4 hours at 24 hours after knockdown of peroxiredoxin 1.The level of reactive oxygen species was detected by 2,7-dichlorodihydrofluorescein diacetate probe.RESULTS AND CONCLUSION:(1)Results from transcriptome sequencing,western blot and RT-qPCR confirmed that peroxiredoxin 1 expression levels in mouse spinal cord tissues were significantly higher in the spinal cord injury group than the sham-operated group(P＜0.05).(2)Peroxiredoxin 1 knockdown in microglial cells led to decreased expression of peroxiredoxin 1 mRNA and protein(P＜0.05),increased mRNA expression of interleukin 1β,interleukin 6,inducible nitric oxide synthase,tumor necrosis factor α,C-C motif chemokine ligand 2,and C-X-C motif chemokine ligand 2(P＜0.05),increased protein expression of inducible nitric oxide synthase,P-P38,P-JNK and P-ERK proteins(P＜0.05),and increased level of reactive oxygen species(P＜0.05).To conclude,peroxiredoxin 1 regulates microglial inflammation by targeting the reactive oxygen species/mitogen-activated protein kinase signaling pathway.

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.

ObjectiveTo investigate the expression of prefrontal cortical neurons， methyltransferase-like 3 （METTL3）， fat mass and obesity-associated gene （FTO）， and AlkB homolog 5 （ALKBH5） proteins in a mouse model of post-traumatic stress disorder （PTSD）. MethodsA PTSD mouse model was established using a single prolonged stress and foot shock stimulation （SPSS） method. The despair， anxiety， and learning and memory functions of PTSD mice were assessed through the open field test， Y-maze test， and forced swimming test. Neuronal damage was detected via HE and Nissl staining. The expression levels of METTL3， FTO， ALKBH5， and neuronal nuclear protein （NEUN） were assessed by Western blot and immunofluorescence staining. ResultsCompared to control group， PTSD mice subjected to SPSS exhibited signs of despair， anxiety， and impaired learning and memory. HE and Nissl staining results showed neuronal damage in the prefrontal cortex of PTSD mice. Western blot and immunofluorescence staining results showed that the expression of the m6A-related proteins METTL3 and FTO decreased， while the expression of ALKBH5 increased in the prefrontal cortex. Additionally， NEUN protein levels showed a declining trend. ConclusionThe pathogenesis of PTSD may be associated with neuronal damage in the prefrontal cortex and alterations in m6A methylation proteins.

SMYD5 is a ribosomal methyltransferase with SET and MYND structural domains， which is a member of the SMYD family and is expressed in a variety of tissues， including ovary and testis. This enzyme participates in biological processes such as gene expression regulation， cell development and differentiation， and maintenance of genomic stability through ribosomal protein methylation modification. In recent years， research on SMYD5 has increased in cancers including hepatocellular carcinoma， gastric adenocarcinoma， and lung cancer. Studies have revealed that SMYD5 exhibits high expression levels in various diseases including hepatocellular carcinoma， gastric adenocarcinoma， lung cancer， and inflammatory bowel disease， influencing the progression of these conditions. This review summarizes the role of SMYD5 in hepatocellular carcinoma， inflammatory bowel disease， and other biological functions， aiming to provide a reference for related disease research.

BACKGROUND:The inflammatory response of microglia is closely related to neuronal survival,regeneration,and functional recovery after spinal cord injury.Peroxiredoxin 1 is not only involved in the regulation of oxidative stress,but also has an important effect on cell proliferation,apoptosis,and inflammatory response.OBJECTIVE:To investigate the role and mechanism of peroxiredoxin 1 in the inflammatory response of microglia following spinal cord injury.METHODS:(1)Twelve female C57BL/6 mice were randomly divided into sham-operated(n=6)and spinal cord injury(n=6)groups.The sham-operated group was not modeled and acute spinal cord injury models were constructed in the spinal cord injury group using the modified Allen's method.Spinal cord tissue at the injured site was taken at 7 days after modeling and transcriptome sequencing was performed to identify differentially expressed genes.The expression of peroxiredoxin 1 in spinal cord tissues was verified using western blot and RT-qPCR.(2)Mouse microglia BV2 were divided into two groups:the control group was stimulated with lipopolysaccharide for 6 hours,and in the knockout group,lipopolysaccharide stimulation was applied for 6 hours at 24 hours after peroxiredoxin 1 was knocked down in the cells.RT-qPCR was performed to detect mRNA expression of peroxiredoxin 1,inflammatory factors(interleukin 1β,interleukin 6,inducible nitric oxide synthase,tumor necrosis factor α,C-C motif chemokine ligand 2,and C-X-C motif chemokine ligand 2),and western blot was performed to detect the expression of peroxiredoxin 1,inducible nitric oxide synthase,and reactive oxygen/mitogen-activated protein kinase signaling pathway proteins.Mouse microglia BV2 were treated in two groups:the control group was stimulated by hydrogen peroxide for 4 hours,and the knockout group was stimulated by hydrogen peroxide for 4 hours at 24 hours after knockdown of peroxiredoxin 1.The level of reactive oxygen species was detected by 2,7-dichlorodihydrofluorescein diacetate probe.RESULTS AND CONCLUSION:(1)Results from transcriptome sequencing,western blot and RT-qPCR confirmed that peroxiredoxin 1 expression levels in mouse spinal cord tissues were significantly higher in the spinal cord injury group than the sham-operated group(P＜0.05).(2)Peroxiredoxin 1 knockdown in microglial cells led to decreased expression of peroxiredoxin 1 mRNA and protein(P＜0.05),increased mRNA expression of interleukin 1β,interleukin 6,inducible nitric oxide synthase,tumor necrosis factor α,C-C motif chemokine ligand 2,and C-X-C motif chemokine ligand 2(P＜0.05),increased protein expression of inducible nitric oxide synthase,P-P38,P-JNK and P-ERK proteins(P＜0.05),and increased level of reactive oxygen species(P＜0.05).To conclude,peroxiredoxin 1 regulates microglial inflammation by targeting the reactive oxygen species/mitogen-activated protein kinase signaling pathway.

Objective To systematically analyze the spatiotemporal distribution characteristics and epidemiological trends of tracheal, bronchus, and lung cancer (TBL) disease burden attributed to air pollution globally and in China and the United States from 1990 to 2021, and to assess the patterns of disease burden changes from 2022 to 2031 based on predictive models, providing a scientific basis for formulating targeted TBL prevention and control strategies. Methods Based on the Global Burden of Disease (GBD) 2021 database, we analyzed the disease burden data of TBL attributed to air pollution globally and in China and the United States from 1990 to 2021. R Studio 4.3.2 software was used to analyze the corresponding trends and the Bayesian age-period-cohort (BAPC) prediction model was used to predict the status of the disease burden of TBL attributed to air pollution in the world and in China and the United States from 2022 to 2031. Results In 2021, China had the highest number of deaths and disability-adjusted life years attributed to air pollution (211 400 patients and 4.8947 million person-years), followed by the United States (6 000 patients and 124 300 person-years). The age-standardized mortality rate (ASMR) and age-standardized disability-adjusted life years rate (ASDR) of TBL due to air pollution in the world and in China and the United States showed a decreasing trend. From 1990 to 2021, the ASMR and ASDR of TBL in China due to air pollution were much higher than those in the United States and the global average. In terms of gender, from 1990 to 2021, the disease burden of male patients with TBL attributed to air pollution was much higher than that of female patients. The BAPC prediction model showed that from 2022 to 2031, the ASMR and ASDR of TBL attributed to air pollution showed an upward trend globally, while they showed a downward trend in China and the United States. Conclusion Over the past 30 years, the air pollution-related TBL disease burden in the world and in China and the United States has continued to decline, but China's disease burden is still significantly higher than the global average. The disease burden in men far exceeds that in women, with men and the population aged ≥50 years being high-risk groups. In the future, the global disease trend may reverse and rise, while China and the United States are expected to continuously decline. However, precise prevention and control for high-risk groups remains a key challenge.

ObjectiveThis study aimed to investigate whether Bushen Tongluo prescription inhibits macrophage polarization by regulating the Wnt3a/β-catenin signaling pathway， thereby reducing epithelial-mesenchymal transition and excessive extracellular matrix deposition， in order to elucidate the anti-pulmonary fibrosis mechanisms of Bushen Tongluo prescription and provide a new theoretical basis for the clinical treatment of pulmonary fibrosis. MethodsFifty male Sprague-Dawley （SD） rats were randomly divided into a blank group， model group， pirfenidone group， and high- and low-dose Bushen Tongluo prescription groups. Except for the blank group， the pulmonary fibrosis model was established by intratracheal instillation of bleomycin. Intervention was initiated on day 28 after modeling. The high- and low-dose Bushen Tongluo prescription groups were administered Bushen Tongluo prescription at doses of 30.88， 15.44 g·kg^-1， respectively， by intragastric gavage. The pirfenidone group was administered pirfenidone capsules at 110 mg·kg^-1 by intragastric gavage. The blank and model groups were given an equal volume of normal saline by gavage， once daily for 90 days. After treatment， the level of transforming growth factor-β₁ （TGF-β₁） in bronchoalveolar lavage fluid （BALF） was detected by enzyme-linked immunosorbent assay （ELISA）. Morphological changes in lung tissue and the collagen volume fraction were compared. The protein distribution and expression of E-cadherin， cytokeratin 19， α-smooth muscle actin （α-SMA）， vimentin， collagen type Ⅰ （Col Ⅰ）， and collagen type Ⅲ （Col Ⅲ） in lung tissue were detected by immunohistochemistry. The protein distribution and expression of CD68， arginase-1 （Arg-1）， inducible nitric oxide synthase （iNOS）， Wnt3a， and β-catenin in lung tissue were detected by immunofluorescence. The protein expression of Wnt3a and β-catenin in lung tissue was detected by Western blot， and the mRNA expression of Wnt3a and β-catenin was detected by Real-time fluorescence quantitative polymerase chain reaction （Real-time PCR）. ResultsCompared with the blank group， a large number of inflammatory cells infiltrated the airway walls， alveolar spaces， and interstitial tissue in the model group， with obvious fibrous tissue hyperplasia. The level of TGF-β₁ in BALF was significantly increased. The protein expression of E-cadherin and cytokeratin 19 in lung tissue was decreased， whereas the protein expression of α-SMA， Vimentin， Wnt3a， β-catenin， Col Ⅰ， and Col Ⅲ was increased. The fluorescence-positive area ratios of CD68， Arg-1， iNOS， Wnt3a， and β-catenin in lung tissue were increased. The protein and mRNA expression levels of Wnt3a and β-catenin in lung tissue were significantly increased （P<0.01）. Compared with the model group， all treatment groups showed varying degrees of improvement in inflammatory cell infiltration and fibrous tissue hyperplasia in the airway walls， alveolar spaces， and interstitial tissue， decreased TGF-β₁ levels in BALF， increased protein expression of E-cadherin and cytokeratin 19 in lung tissue， decreased protein expression of α-SMA， Vimentin， Col Ⅰ， and Col Ⅲ， decreased fluorescence-positive area ratios of CD68， Arg-1， iNOS， Wnt3a， and β-catenin in lung tissue， and decreased protein and mRNA expression levels of Wnt3a and β-catenin in lung tissue （P<0.05， P<0.01）. ConclusionBushen Tongluo prescription can improve bleomycin-induced pulmonary fibrosis in rats by inhibiting epithelial-mesenchymal transition and reducing excessive extracellular matrix deposition. The mechanism may be related to inhibition of the Wnt3a/β-catenin signaling pathway and the macrophage polarization mediated by this pathway.

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.