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 mechanism by which Shaoyaotang regulates the miRNA-155-mediated suppressor of cytokine signaling 1 （SOCS1）/Janus kinase 1 （JAK1）/signal transducer and activator of transcription 1 （STAT1） signaling pathway and thereby affects macrophage polarization. MethodsThe cell-counting kit-8 （CCK-8） assay was used to detect the effect of drug-containing serum of Shaoyaotang at different concentrations on the viability of RAW 264.7 cells. A cell model of inflammation was established by stimulating RAW264.7 cells with lipopolysaccharide （LPS） at a concentration of 10 mg·L^-1 The modeled cells were assigned by the random number table method into seven groups： LPS-induced M1 polarization （model）， M1+miRNA-155 mimics， M1+miRNA-155 inhibitor， M1+Shaoyaotang-containing serum， M1+miRNA-155 mimics+Shaoyaotang-containing serum， M1+miRNA-155 inhibitor+Shaoyaotang-containing serum， and M1+blank serum. Enzyme-linked immunosorbent assay was employed to measure the levels of inflammatory factors ［tumor necrosis factor-α （TNF-α）， interleukin-6 （IL-6）， and interleukin-1β （IL-1β）］. Immunofluorescence assay was used to detect the expression of macrophage polarization markers ［inducible nitric oxide synthase （iNOS） and macrophage mannose receptor 1 （CD206）］. Real-time PCR was employed to measure the expression of miRNA-155 in cells. Western blot was performed to determine the protein levels of SOCS1， STAT1， and JAK1. ResultsCompared with the LPS-induced M1 polarization （model） group， the M1+miRNA-155 mimics group showed up-regulated expression of miRNA-155， JAK1， STAT1， TNF-α， IL-6， IL-1β， and iNOS （P<0.05） and down-regulated expression of CD206 （P<0.05）. In both the M1+miRNA-155 inhibitor group and the M1+Shaoyaotang-containing serum group， the expression levels of miRNA-155， JAK1， STAT1， TNF-α， IL-6， IL-1β， and iNOS were down-regulated （P<0.05）， while those of SOCS1 and CD206 were up-regulated （P<0.05）. Compared with the M1+miRNA-155 mimics group， the M1+miRNA-155 mimics+Shaoyaotang-containing serum group showed down-regulated expression of miRNA-155， JAK1， STAT1， TNF-α， IL-6， IL-1β， and iNOS （P<0.05） and up-regulated expression of SOCS1 and CD206 （P<0.05）. Compared with the M1+miRNA-155 inhibitor group， the M1+miRNA-155 inhibitor+Shaoyaotang-containing serum group showed down-regulated expression of miRNA-155， JAK1， STAT1， TNF-α， IL-6， IL-1β， and iNOS （P<0.05） and up-regulated expression of SOCS1 and CD206 （P<0.05）. ConclusionShaoyaotang regulates macrophage polarization by modulating miRNA-155 expression and interfering with the SOCS1/JAK1/STAT1 signaling pathway. The findings provide new experimental evidence for the treatment of ulcerative colitis with Shaoyaotang.

ObjectiveTo investigate the potential role and underlying mechanisms of Shaoyaotang in intervening macrophage glycolytic reprogramming in ulcerative colitis （UC）. MethodsForty-eight C57BL/6 mice were randomly divided into six groups： Normal control group， model group， mesalazine group （0.39 g·kg^-1）， Shaoyaotang group （15.54 g·kg^-1）， 2-deoxy-D-glucose （2-DG） group （glycolysis inhibitor， 100 mg·kg^-1）， and 2-DG + Shaoyaotang combined group （100 mg·kg^-1+15.54 g·kg^-1）. Except for the normal control group， mice in the other five groups were induced to establish UC models using dextran sulfate sodium （DSS）. The normal control group was administered pure water via intragastric gavage， while the other groups received intragastric gavage of mesalazine solution， intragastric gavage of Shaoyaotang， and the 2-DG group was treated with 2-DG via intraperitoneal injection. After 7 consecutive days of treatment， colonic tissues were extracted. Hematoxylin and eosin （HE） staining was performed to evaluate histopathological changes and tissue injury in the colon. Enzyme-linked immunosorbent assay （ELISA） was used to detect the expression of interleukin-10 （IL-10） and tumor necrosis factor-α （TNF-α） in colonic tissues. Western blot analysis was employed to determine the expression levels of hypoxia-inducible factor-1α （HIF-1α）， glucose transporter （GLUT1）， lactate dehydrogenase A （LDHA）， pyruvate kinase M2 （PKM2）， and 6-phosphofructo-2-kinase/fructose-2，6-biphosphatase 3 （PFKFB3） in colonic tissues. Immunofluorescence was conducted to detect the expression of CD206 and inducible nitric oxide synthase （iNOS） in colonic tissues. Liquid chromatography-mass spectrometry （LC-MS） was utilized to measure lactate and citrate levels in colonic tissues. ResultsCompared with the normal control group， mice in the model group exhibited a significant increase in disease activity index （DAI） scores， accompanied by colonic mucosal congestion， edema， and inflammatory cell infiltration， significantly elevated expression of the inflammatory cytokine TNF-α （P<0.05）， significantly decreased IL-10 expression （P<0.05）， significantly increased levels of HIF-1α， GLUT1， LDHA， PKM2， and PFKFB3 in colonic tissues （P<0.05）， markedly elevated iNOS expression （P<0.05）， significantly decreased CD206 expression （P<0.05）， and significantly elevated lactate and citrate levels in colonic tissues （P<0.05）. In contrast to the model group， the Shaoyaotang group， inhibitor group， and Shaoyaotang combined with inhibitor group demonstrated amelioration of mucosal injury in colonic tissues， markely decreased expression levels of the inflammatory cytokine TNF-α （P<0.05）， elevated IL-10 expression levels， significantly decreased expression of HIF-1α， GLUT1， LDHA， PKM2， and PFKFB3 （P<0.05）， markedly reduced iNOS expression levels （P<0.05）， significantly increased CD206 expression （P<0.05） and significantly decreased lactate and citrate levels （P<0.05）. ConclusionShaoyaotang ameliorates symptoms of DSS-induced UC in mice， and its therapeutic mechanism may be associated with regulating macrophage glycolytic reprogramming via modulation of the HIF-1α signaling pathway.

ObjectiveTo investigate the role of microRNA-155-5p （miR-155-5p） in ulcerative colitis （UC） and study the molecular mechanism of Shaoyaotang in the treatment of UC by regulating miR-155-5p. MethodsForty-eight SPF-grade male C57BL/6 mice were selected and assigned via the random number table method into 6 groups （n=8）： A blank control group， a model group， a mesalazine （0.39 g·kg^-1） group， a Shaoyaotang （31.08 g·kg^-1） group， a Janus kinase 1 （JAK1） inhibitor （baricitinib， 10 mg·kg^-1） group， and a Shaoyaotang combined with inhibitor （baricitinib 10 mg·kg^-1 + Shaoyaotang 31.08 g·kg^-1） group. After successful modeling of UC by gavage of 3% dextran sulphate sodium solution， each group received corresponding drug intervention for 7 days. Shaoyaotang and mesalazine were administered by gavage， and baricitinib by intraperitoneal injection. Twenty-four hours after the last administration， mice were anesthetized by intraperitoneal injection of pentobarbital sodium， and blood was collected for determination of white blood cell count and erythrocyte sedimentation rate （ESR）. Mice were then sacrificed for measurement of colon length. Hematoxylin-eosin staining was used to observe colonic pathological changes and perform pathological scoring. Real-time fluorescence quantitative polymerase chain reaction （Real-time PCR） was employed to determine the relative expression of miR-155-5p in the colonic tissue， and Western blot was used to determine the protein levels of JAK1， phosphorylated JAK1 （p-JAK1）， suppressor of cytokine signaling 1 （SOCS1）， signal transducer and activator of transcription 1 （STAT1）， and phosphorylated STAT1 （p-STAT1）. ResultsCompared with the blank control group， the model group showed increased disease activity index （DAI） score and pathological score， shortened colon， upregulated relative expression of miR-155-5p and protein levels of p-JAK1 and p-STAT1， downregulated protein level of SOCS1 in the colonic tissue， prolonged time of erythrocyte sedimentation， and increased white blood cell count （P<0.01）. Compared with the model group， all drug-treated groups exhibited improvements in the above indicators （P<0.01）. Moreover， the Shaoyaotang group showed better therapeutic effects than the mesalazine group in regulating miR-155-5p expression， related protein levels， DAI score， and colonic pathological score （P<0.01）. ConclusionShaoyaotang may downregulate miR-155-5p to relieve its inhibition on SOCS1， thereby suppressing the excessive activation of the JAK1/STAT1 signaling pathway and ultimately alleviating intestinal inflammatory damage.

ObjectiveTo investigate the role of the Toll-like receptor 4 （TLR4）/myeloid differentiation factor 88 （MyD88）/nuclear factor-κB （NF-κB） signaling pathway in intestinal mucosal barrier damage in ulcerative colitis， as well as the intervention mechanism of Shaoyaotang. MethodsSixty SD rats were allocated into a blank group， a model group， a mesalazine （0.42 g·kg^-1） group， and low-， medium-， and high-dose （11.1， 22.2， 44.4 g·kg^-1， respectively） Shaoyaotang groups. A model of ulcerative colitis was induced by 2，4，6-trinitrobenzenesulfonic acid （TNBS）. After successful modeling， rats were administrated with corresponding agents via gavage for 7 days. Changes in colon length and colon weight were observed. Hematoxylin-eosin staining was performed to examine the pathological changes of the colon， and immunohistochemistry was employed to detect the expression of the inflammatory cytokine interleukin-8 （IL-8）， cyclooxygenase-2 （COX-2）， junction adhesion molecule-1 （JAM-1）， and claudin-1 in the colon. Western blot analysis was performed to determine the protein levels of TLR4， MyD88， and NF-κB in the colon. ResultsCompared with the blank group， the model group showed elevated DAI score （P<0.01）， reduced colon length and colon weight （P<0.01）， down-regulated protein levels of JAM-1 and claudin-1 （P<0.01）， and up-regulated protein levels of IL-8， COX-2， TLR4， MyD88， and NF-κB p65 （P<0.01） in the colon tissue. Compared with the model group， each treatment group showed decreased DAI score （P<0.05， P<0.01）， increased colon length and colon weight （P<0.05， P<0.01）， up-regulated protein levels of JAM-1 and claudin-1 （P<0.01）， and down-regulated protein levels of IL-8， COX-2， TLR4， MyD88， and NF-κB p65 （P<0.01） in the colon tissue. ConclusionShaoyaotang alleviates intestinal inflammation and intestinal mucosal damage to protect intestinal barrier integrity by regulating the TLR4/MyD88/NF-κB signaling pathway.

ObjectiveTo analyze the processing mechanism underlying the enhanced effect of invigorating spleen and stopping diarrhea of soil-fried Atractylodis Macrocephalae Rhizoma（AMR） by analyzing the changes of microstructure， chemical composition and anti-ulcerative colitis（UC） activity before and after soil stir-frying. MethodsThe microstructure and elemental composition of AMR before and after soil stir-frying were analyzed by scanning electron microscopy-energy dispersive spectroscopy（SEM-EDS）， to investigate the differences in microstructure and the underlying causes. Ultra-high performance liquid chromatography-quadrupole-time-of-flight mass spectrometry（UPLC-Q-TOF-MS） coupled with UNIFI 1.9.2 natural product analysis platform were used to analyze and identify the chemical constituents in raw and soil-fried products， and multivariate statistical methods including principal component analysis（PCA） and orthogonal partial least squares-discriminant analysis（OPLS-DA） were used to explore the differences and sources of chemical constituents between them. A dextran sulfate sodium（DSS）-induced UC mouse model was established. The method of disease activity index（DAI） was used to evaluate the severity of intestinal inflammation. Hematoxylin-eosin（HE） staining was used to observe the pathological changes of colon tissue， enzyme-linked immunosorbent assay（ELISA） was used to detect the levels of inflammatory factors， Real-time quantitative polymerase chain reaction（Real-time PCR） and Western blot were used to analyze the expressions of key genes and proteins involved in the intestinal mucosal barrier. The 16S rRNA sequencing was used to evaluate the diversity of intestinal flora， headspace gas chromatography-mass spectrometry（HS-GC-MS） was used to explore the levels of short-chain fatty acids（SCFAs） in feces. Base on the above findings， this paper investigated the effects of raw and soil-fried AMR on the biological， chemical， mechanical and immune barriers of model animals， and the differences in pharmacological effects and underlying mechanisms from the perspective of regulating the intestinal mucosal barrier in UC mice. ResultsSEM observation revealed numerous hearth soil particles on the surface of soil-fried AMR， accompanied by bubble-like bulges. At the same time， there were many cracks and folds on the surface of the hearth soil. EDS analysis revealed that the contents of Si， Al， Mg and Ca in soil-fried AMR were significantly higher than those of raw products， and these elements constituted the primary components of hearth soil. UPLC-Q-TOF-MS combined with database comparison was used to identify the chemical constituents of raw and soil-fried AMR. In positive ion mode， a total of 132 components were identified， primarily comprising three categories of terpenoids， polyphenols and amino acids. In negative ion mode， a total of 40 components were identified， primarily polyphenolic and glycoside compounds. Among them， the contents of sesquiterpenes and polyphenolic acids were changed significantly before and after processing. Soil-fried AMR could reduce the DAI score of UC mice， alleviate the shortening of colon length， reduce the levels of pro-inflammatory factors such as interleukin（IL）-17， IL-18， γ-interferon（IFN-γ） and tumor necrosis factor（TNF）-α in serum， increase the levels of anti-inflammatory factors such as secretory immunoglobulin A（sIgA）， IL-10， IL-4 and transforming growth factor-β（TGF-β） in serum， increase the expressions of key genes and proteins of intestinal mucosal barrier such as tight junction protein-1（ZO-1）， Occludin， Claudin-1 and mucin 2（MUC2） in colonic mucosa， and improve the disorders of intestinal flora diversity and the levels of SCFAs（P<0.05， P<0.01）. The raw and stir-fried products of AMR also exhibited the aforementioned effects， but they were weaker than the soil-fried products. Additionally， the auxiliary material hearth soil also had a certain pharmacodynamic effect. ConclusionSoil-fried AMR can enhance the protective effect on intestinal mucosal barrier in UC mice. These changes or heating-induced alterations in the microscopic structure and chemical composition of AMR may be attributed to the dual effects of adsorption of hearth soil.

ObjectiveTo analyze the processing mechanism underlying the enhanced effect of invigorating spleen and stopping diarrhea of soil-fried Atractylodis Macrocephalae Rhizoma（AMR） by analyzing the changes of microstructure， chemical composition and anti-ulcerative colitis（UC） activity before and after soil stir-frying. MethodsThe microstructure and elemental composition of AMR before and after soil stir-frying were analyzed by scanning electron microscopy-energy dispersive spectroscopy（SEM-EDS）， to investigate the differences in microstructure and the underlying causes. Ultra-high performance liquid chromatography-quadrupole-time-of-flight mass spectrometry（UPLC-Q-TOF-MS） coupled with UNIFI 1.9.2 natural product analysis platform were used to analyze and identify the chemical constituents in raw and soil-fried products， and multivariate statistical methods including principal component analysis（PCA） and orthogonal partial least squares-discriminant analysis（OPLS-DA） were used to explore the differences and sources of chemical constituents between them. A dextran sulfate sodium（DSS）-induced UC mouse model was established. The method of disease activity index（DAI） was used to evaluate the severity of intestinal inflammation. Hematoxylin-eosin（HE） staining was used to observe the pathological changes of colon tissue， enzyme-linked immunosorbent assay（ELISA） was used to detect the levels of inflammatory factors， Real-time quantitative polymerase chain reaction（Real-time PCR） and Western blot were used to analyze the expressions of key genes and proteins involved in the intestinal mucosal barrier. The 16S rRNA sequencing was used to evaluate the diversity of intestinal flora， headspace gas chromatography-mass spectrometry（HS-GC-MS） was used to explore the levels of short-chain fatty acids（SCFAs） in feces. Base on the above findings， this paper investigated the effects of raw and soil-fried AMR on the biological， chemical， mechanical and immune barriers of model animals， and the differences in pharmacological effects and underlying mechanisms from the perspective of regulating the intestinal mucosal barrier in UC mice. ResultsSEM observation revealed numerous hearth soil particles on the surface of soil-fried AMR， accompanied by bubble-like bulges. At the same time， there were many cracks and folds on the surface of the hearth soil. EDS analysis revealed that the contents of Si， Al， Mg and Ca in soil-fried AMR were significantly higher than those of raw products， and these elements constituted the primary components of hearth soil. UPLC-Q-TOF-MS combined with database comparison was used to identify the chemical constituents of raw and soil-fried AMR. In positive ion mode， a total of 132 components were identified， primarily comprising three categories of terpenoids， polyphenols and amino acids. In negative ion mode， a total of 40 components were identified， primarily polyphenolic and glycoside compounds. Among them， the contents of sesquiterpenes and polyphenolic acids were changed significantly before and after processing. Soil-fried AMR could reduce the DAI score of UC mice， alleviate the shortening of colon length， reduce the levels of pro-inflammatory factors such as interleukin（IL）-17， IL-18， γ-interferon（IFN-γ） and tumor necrosis factor（TNF）-α in serum， increase the levels of anti-inflammatory factors such as secretory immunoglobulin A（sIgA）， IL-10， IL-4 and transforming growth factor-β（TGF-β） in serum， increase the expressions of key genes and proteins of intestinal mucosal barrier such as tight junction protein-1（ZO-1）， Occludin， Claudin-1 and mucin 2（MUC2） in colonic mucosa， and improve the disorders of intestinal flora diversity and the levels of SCFAs（P<0.05， P<0.01）. The raw and stir-fried products of AMR also exhibited the aforementioned effects， but they were weaker than the soil-fried products. Additionally， the auxiliary material hearth soil also had a certain pharmacodynamic effect. ConclusionSoil-fried AMR can enhance the protective effect on intestinal mucosal barrier in UC mice. These changes or heating-induced alterations in the microscopic structure and chemical composition of AMR may be attributed to the dual effects of adsorption of hearth soil.

Objective To investigate the causal relationship between gut microbiota and idiopathic pulmonary fibrosis (IPF). Methods Genome-wide association studies (GWAS) data of gut microbiota and IPF were obtained from MiBioGen and IEU OpenGWAS, respectively. Instrumental variables were screened by means of significance, linkage disequilibrium, weak instrumental variable screening, and removal of confounding factors (genetics, smoking, host characteristics). Inverse variance weighted (IVW) was used as the main Mendelian randomization (MR) analysis method, and the weighted median, simple mode, MR-Egger, and weighted mode were used to perform MR to reveal the causal effect of gut microbiota and IPF. The Cochrane's Q, leave-one-out, MR-Egger-intercept, and Mendelian randomization pleiotropy residual sum and outlier (MR-PRESSO) and Steiger tests were used to analyze the heterogeneity, horizontal pleiotropy, outliers, and directionality, respectively. Results IVW analysis results showed that Actinobacteria [OR=1.773, 95%CI (1.323, 2.377), P<0.001], Erysipelatoclostridium [OR=2.077, 95%CI (1.107, 3.896), P=0.023], and Streptococcus [OR=1.35, 95%CI (1.100, 1.657), P=0.004] could increase the risk of IPF. Bifidobacterium [OR=0.668, 95%CI (0.620, 0.720), P<0.001], Ruminococcus [OR=0.434, 95%CI (0.222, 0.848), P=0.015], and Tyzzerella [OR=0.479, 95%CI (0.304, 0.755), P=0.001] could reduce the risk of IPF. No significant heterogeneity, horizontal pleiotropy, outliers, and reverse causality were found. Conclusion Actinobacteria, Erysipelatoclostridium and Streptococcus may increase the risk of IPF, while Bifidobacterium, Ruminococcus and Tyzzerella may reduce the risk of IPF. Regulation of the above gut microbiota may become a new direction in the study of the pathogenesis of IPF.

ObjectiveCleft palate (CP) is a common congenital deformity often associated with velopharyngeal insufficiency (VPI), which disrupts the physiological coupling between respiration and speech. Conventional clinical assessments, such as nasometry and spirometry, provide limited static data and fail to visualize the dynamic spatiotemporal distribution of lung ventilation during phonation. This study introduces spatiotemporal electrical impedance tomography (ST-EIT) to evaluate speech-respiratory functional features in CP patients compared to normal controls (NC). The aim is to characterize multi-domain respiratory patterns and to validate an interpretable machine learning framework for providing objective, quantitative evidence for clinical assessment. MethodsSeventy-five participants were enrolled in this study, comprising 37 patients with surgically repaired CP and 38 healthy volunteers matched for age, gender, and body mass index (BMI). All subjects performed standardized sustained phonation tasks while undergoing synchronous monitoring with a 16-electrode EIT system and a pneumotachograph. A comprehensive feature engineering pipeline was developed to extract physiological parameters across 3 complementary domains. (1) Temporal domain: including inspiratory/expiratory phase duration (tPhase), time constants (Tau), and inspiratory-to-expiratory time ratios (TI/TE); (2) airflow domain: comprising mean flow, peak flow, and instantaneous flow at 25%, 50%, and 75% of tidal volume; and (3) spatial domain: quantifying global and regional tidal impedance variation (TIV), global inhomogeneity (GI), and center of ventilation (CoV). Extreme Gradient Boosting (XGBoost) classifiers were trained using 5 distinct data sources (Spirometry, Nasometry, Inspiratory-EIT, Expiratory-EIT, and fused ST-EIT). Model performance was rigorously evaluated via stratified 5-fold cross-validation, and Shapley additive explanations (SHAP) were employed to quantify global and local feature contributions. ResultsThe CP group exhibited a distinct respiratory phenotype compared to controls. In the temporal domain, CP patients showed significantly shorter inspiratory (1.60 s vs.1.85 s, P<0.001) and expiratory phase durations (2.45 s vs. 3.95 s, P<0.001), indicating a rapid, shallow breathing rhythm. In the airflow domain, while inspiratory flows were comparable, the CP group demonstrated significantly elevated mean and peak flows during the expiratory phase (P<0.001), reflecting compensatory respiratory effort. Spatially, CP patients presented significant ventilation redistribution, characterized by higher regional TIV in the right-anterior (ROI1) and left-posterior (ROI4) quadrants, but lower TIV in the left-anterior (ROI2) quadrant. In terms of diagnostic accuracy, the multi-modal ST-EIT model achieved the highest performance (AUC: 0.915±0.012, Accuracy: 0.843±0.019, F1-score: 0.872±0.017), substantially outperforming models based on spirometry (AUC: 0.721) or nasometry (AUC: 0.625) alone. Interpretability analysis revealed that spatial domain features were the most critical, contributing 53.4% to the model’s decision-making, followed by temporal (25.0%) and airflow (21.6%) features. ConclusionST-EIT successfully captures the temporal, airflow, and spatial deviations in CP speech respiration that are undetectable by conventional methods—specifically, rapid phase transitions, hyperdynamic expiratory airflow, and regional ventilation heterogeneity. This study validates ST-EIT as a robust, non-invasive, and radiation-free tool for characterizing speech-respiratory dysfunction, offering high clinical value for bedside screening, rehabilitation planning, and longitudinal monitoring of patients with cleft palate.