ObjectiveTo investigate the mechanism of Xiezhuo Jiedu prescription in the treatment of ulcerative colitis （UC） by inhibiting ferroptosis and alleviating intestinal mucosal injury based on the nuclear factor E₂ related factor 2/solute carrier family 7 member/glutathione peroxidase 4 （Nrf2/SLC7A11/GPX4） signaling pathway. MethodsA total of 60 male SD rats were divided into a normal group， a model group， high- and low-dose Xiezhuo Jiedu prescription groups （26.64 and 13.32 g·kg^-1， respectively）， a ferroptosis inhibitor group （Ferrostatin-1， 0.005 g·kg^-1）， and a mesalazine group （0.27 g·kg^-1）， with 10 rats in each group. A UC rat model was established by intrarectal administration of trinitrobenzene sulfonic acid （TNBS）-ethanol. The normal group and the model group were intragastrically administered normal saline. The other groups were given intragastric administration according to the corresponding dosage for 7 d. The general condition， disease activity index （DAI） score， colon length， and mucosal injury index （CDMI） score were observed in each group. The pathological changes of colon tissue in each group were observed by hematoxylin-eosin （HE） staining. The intestinal mucosa and mitochondrial morphology in each group were observed by transmission electron microscopy. The expression levels of Occludin， Claudin-1， mucin 2 （MUC2）， and E-cadherin in intestinal tissue were detected by immunofluorescence （IF）. Enzyme-linked immunosorbent assay （ELISA） was used to detect the expression levels of serum tumor necrosis factor-α （TNF-α）， interleukin-6 （IL-6）， and interleukin-10 （IL-10） in each group， and a lactic acid assay kit or ELISA was employed to detect the expression levels of reactive oxygen species （ROS）， ferrous ions （Fe²⁺）， glutathione （GSH）， malondialdehyde （MDA）， 4-hydroxynonenal （4-HNE）， diamine oxidase （DAO）， and D-lactate （D-LA）. Real-time quantitative polymerase chain reaction （Real-time PCR） was applied to detect the mRNA expression levels of Nrf2， SLC7A11， GPX4， Occludin， Claudin-1， MUC2， and E-cadherin in each group， and Western blot was adopted to detect the protein expression levels of Nrf2， p-Nrf2， SLC7A11， and GPX4 in each group. ResultsCompared with the normal group， rats in the model group exhibited listlessness， sluggish response， and mucopurulent and bloody stools. The model group also showed significantly increased DAI score， colon length， CDMI score， and expression levels of TNF-α， IL-6， ROS， Fe²⁺， MDA， 4-HNE， DAO， and D-LA （P<0.01）. In addition， it presented significantly decreased IF values of Occludin， Claudin-1， MUC2， and E-cadherin and mRNA and protein expression levels of IL-10， GSH， Nrf2， p-Nrf2， SLC7A11， and GPX4 （P<0.01）. There were different degrees of improvement in each administration group after treatment， and the improvement was the most significant in the high-dose Xiezhuo Jiedu prescription group （P<0.01）. ConclusionXiezhuo Jiedu prescription may alleviate intestinal mucosal injury by inhibiting ferroptosis of intestinal epithelial cells via regulating the Nrf2/SLC7A11/GPX4 signaling pathway， thereby exhibiting efficacy in the treatment of UC.

ObjectiveThis paper aims to explore the risk factors for chronic atrophic gastritis （CAG） with turbidity toxin accumulation syndrome and establish a prediction model. MethodsClinical data of 180 patients with CAG who participated in the "clinical study of Xianglian Huazhuo Particles blocking CAG cancer transformation" of Hebei Sheng Zhong Yi Yuan from July 2021 to March 2022 were collected. After confounding factors were controlled by propensity score matching， patients were divided into a training set （namely dev） and a validation set （namely vad） in a seven to three ratio. The risk factors for CAG with turbidity toxin accumulation syndrome in the training set were investigated by using univariate Logistic regression analysis and least absolute shrinkage and selection operator （namely Lasso） regression algorithms. Subsequently， a model， named model 1se， was developed by using the training set data to predict the risk factors for CAG with turbidity toxin accumulation syndrome. The accuracy of the prediction model was assessed by using various methods， including the receiver operating characteristic （ROC） curve， Hosmer-Lemeshow test （H-L）， calibration plot， and decision curve analysis （DCA）. ResultsAge， body mass index （BMI）， family history of cancer， job and life satisfaction， yellow and greasy fur with slippery pulse， and heavy body sensation were independent risk factors of the model. The prediction model showed excellent predictive value for both the training and validation sets. ConclusionThe established prediction model for CAG with turbidity toxin accumulation syndrome has high discrimination and excellent calibration， which could provide an excellent clinical basis for disease diagnosis and individualized treatment of patients.

ObjectiveThis paper aims to explore the risk factors for chronic atrophic gastritis （CAG） with turbidity toxin accumulation syndrome and establish a prediction model. MethodsClinical data of 180 patients with CAG who participated in the "clinical study of Xianglian Huazhuo Particles blocking CAG cancer transformation" of Hebei Sheng Zhong Yi Yuan from July 2021 to March 2022 were collected. After confounding factors were controlled by propensity score matching， patients were divided into a training set （namely dev） and a validation set （namely vad） in a seven to three ratio. The risk factors for CAG with turbidity toxin accumulation syndrome in the training set were investigated by using univariate Logistic regression analysis and least absolute shrinkage and selection operator （namely Lasso） regression algorithms. Subsequently， a model， named model 1se， was developed by using the training set data to predict the risk factors for CAG with turbidity toxin accumulation syndrome. The accuracy of the prediction model was assessed by using various methods， including the receiver operating characteristic （ROC） curve， Hosmer-Lemeshow test （H-L）， calibration plot， and decision curve analysis （DCA）. ResultsAge， body mass index （BMI）， family history of cancer， job and life satisfaction， yellow and greasy fur with slippery pulse， and heavy body sensation were independent risk factors of the model. The prediction model showed excellent predictive value for both the training and validation sets. ConclusionThe established prediction model for CAG with turbidity toxin accumulation syndrome has high discrimination and excellent calibration， which could provide an excellent clinical basis for disease diagnosis and individualized treatment of patients.

ObjectiveTo explore the potential mechanism of Xiezhuo Jiedu prescription in regulating autophagy in the treatment of ulcerative colitis （UC） by bioinformatics and animal experiments. MethodsThe differentially expressed genes （DEGs） in the colonic mucosal tissue of UC patients was obtained from the Gene Expression Omnibus （GEO）， and those overlapped with autophagy genes were obtained as the differentially expressed autophagy-related genes （DEARGs）. DEARGs were imported into Metascape and STRING， respectively， for gene ontology/Kyoto Encyclopedia of Genes and Genomics （GO/KEGG） enrichment analysis and protein-protein interaction （PPI） analysis. Finally， 15 key DEARGs were obtained. The core DEARGs were obtained by least absolute shrinkage and selection operator （LASSO） regression and receiver operating characteristic curve （ROC） analysis. The CIBERSORT deconvolution algorithm was used to analyze the immunoinfiltration of UC patients and the correlations between core DEARGs and immune cells. C57BL/6J mice were assigned into a normal group and a modeling group. The mouse model of UC was established by free drinking of 2.5% dextran sulfate sodium. The modeled mice were assigned into low-， medium-， and high-dose Xiezhuo Jiedu prescription and mesalazine groups according to the random number table method and administrated with corresponding agents by gavage for 7 days. The colonic mucosal morphology was observed by hematoxylin-eosin staining. The protein and mRNA levels of cysteinyl aspartate-specific proteinase 1 （Caspase-1）， cathepsin B （CTSB）， C-C motif chemokine-2 （CCL2）， CXC motif receptor 4 （CXCR4）， and hypoxia-inducing factor-1α （HIF-1α） in the colon tissue were determined by Western blot and real-time fluorescence quantitative polymerase chain reaction， respectively. ResultsThe dataset GSE87466 was screened from GEO and interlaced with autophagy genes. After PPI analysis， LASSO regression， and ROC analysis， the core DEARGs （Caspase-1， CCL2， CTSB， and CXCR4） were obtained. The results of immunoinfiltration analysis showed that the counts of NK cells， M0 macrophages， M1 macrophages， and dendritic cells in the colonic mucosal tissue of UC patients had significant differences， and core DEARGs had significant correlations with these immune cells. This result， combined with the prediction results of network pharmacology， suggested that the HIF-1α signaling pathway may play a key role in the regulation of UC by Xiezhuo Jiedu prescription. The animal experiments showed that Xiezhuo Jiedu prescription significantly alleviated colonic mucosal inflammation in UC mice. Compared with the normal group， the model group showed up-regulated protein and mRNA levels of caspase-1， CCL2， CTSB， CXCR4， and HIF-1α， which were down-regulated after treatment with Xiezhuo Jiedu prescription or mesalazine. ConclusionCaspase-1， CCL2， CTSB， and CXCR4 are autophagy genes that are closely related to the onset of UC. Xiezhuo Jiedu prescription can down-regulate the expression of core autophagy genes to alleviate the inflammation in the colonic mucosa of mice.

Objective: To evaluate the feasibility of dynamic contrast-enhanced MRI (DCE-MRI) in differentiating tumor deposits (TDs) from metastatic lymph nodes (MLNs) in rectal cancer. Materials and Methods: A retrospective analysis was conducted on 70 patients with rectal cancer, including 168 lesions (70 TDs and 98 MLNs confirmed by histopathology), who underwent pretreatment MRI and subsequent surgery between March 2019 and December 2022. The morphological characteristics of TDs and MLNs, along with quantitative parameters derived from DCE-MRI (K trans , kep, and v e) and DWI (ADCmin, ADCmax, and ADCmean), were analyzed and compared between the two groups.Multivariable binary logistic regression and receiver operating characteristic (ROC) curve analyses were performed to assess the diagnostic performance of significant individual quantitative parameters and combined parameters in distinguishing TDs from MLNs. Results: All morphological features, including size, shape, border, and signal intensity, as well as all DCE-MRI parameters showed significant differences between TDs and MLNs (all P < 0.05). However, ADC values did not demonstrate significant differences (all P > 0.05). Among the single quantitative parameters, v e had the highest diagnostic accuracy, with an area under the ROC curve (AUC) of 0.772 for distinguishing TDs from MLNs. A multivariable logistic regression model incorporating short axis, border, v e, and ADC mean improved diagnostic performance, achieving an AUC of 0.833 (P = 0.027). Conclusion The combination of morphological features, DCE-MRI parameters, and ADC values can effectively aid in the preoperative differentiation of TDs from MLNs in rectal cancer.

ObjectiveThis study aims to explore the potential mechanism of the Xiezhuo Jiedu formula in regulating pyroptosis for the treatment of ulcerative colitis （UC） using bioinformatics and in vivo animal experiments. MethodsDifferentially expressed genes （DEGs） in colon tissues of UC patients were retrieved from the Gene Expression Omnibus （GEO） database. Pyroptosis-related genes were obtained from the GEO and GeneCards databases. The intersection of these datasets yielded pyroptosis-related DEGs （Pyro-DEGs）. Pyro-DEGs were subjected to Gene Ontology （GO） and Kyoto Encyclopedia of Genes and Genomes （KEGG） enrichment analysis using the Metascape database. A protein-protein interaction （PPI） network was constructed using the STRING database. Least absolute shrinkage and selection operator （LASSO） prediction model and receiver operating characteristic （ROC） analysis were conducted to identify core Pyro-DEGs with diagnostic and therapeutic potential. Immune infiltration analysis of the UC datasets was performed using the deconvolution method （CIBERSORT）， along with correlation analysis with core Pyro-DEGs. Sixty male Sprague-Dawley （SD） rats were randomly divided into a control group， a model group， high-， medium-， and low-dose groups of Xiezhuo Jiedu formula （26.64， 13.32， 6.66 g·kg^-1）， and a mesalazine group （0.27 g·kg^-1）， with 10 rats in each group. UC was established by intrarectal administration of 3，5-trinitrobenzenesulfonic acid （TNBS） dissolved in ethanol. The control and model groups were given distilled water by gavage， while the treatment groups were administered the corresponding drugs for 7 consecutive days. Hematoxylin-eosin （HE） staining was used to observe the colon histopathology. Enzyme-linked immunosorbent assay （ELISA） was used to detect the levels of inflammatory factors such as interleukin-1β （IL-1β）， IL-10， IL-18， and transforming growth factor-β （TGF-β）. Immunohistochemistry （IHC） and Western blot were applied to detect the expression of Caspase-1， gap junction alpha-1 protein （GJA1）， peroxisome proliferator-activated receptor gamma （PPARG）， and S100 calcium-binding protein A8 （S100A8）. Real-time quantitative polymerase chain reaction （Real-time PCR） was utilized to measure mRNA expression of Caspase-1， GJA1， PPARG， and S100A8. Western blot was performed to assess protein expression levels of Caspase-1， GJA1， PPARG， and S100A8. ResultsGEO datasets GSE87466 and GSE87473 yielded 64 Pyro-DEGs. KEGG analysis indicated that these genes were enriched in the NOD-like receptor signaling pathway， tumor necrosis factor （TNF） signaling pathway， and hypoxia-inducible factor 1 （HIF-1） signaling pathway. Four core Pyro-DEGs （Caspase-1， GJA1， PPARG， and S100A8） were identified. Immune infiltration analysis showed that expression of these genes was positively correlated with mast cells， neutrophils， M0 macrophages， M1 macrophages， and dendritic cells. Animal experimental results indicated that compared with the control group， the model group had significantly increased levels of IL-1β and IL-18， significantly decreased levels of IL-10 and TGF-β. The model group showed enhanced Caspase-1， GJA1， and S100A8 staining， and significantly increased mRNA and protein expression of Caspase-1， GJA1， and S100A8 （P<0.01）. In contrast， the expression of PPARG was reduced in the model group （P<0.01）. After treatment， all dosage groups showed varying degrees of improvement （P<0.05， P<0.01）， with the high-dose group showing the most significant improvement （P<0.01）. ConclusionCaspase-1， GJA1， PPARG， and S100A8 are core Pyro-DEGs closely associated with the pathogenesis of UC. These genes may collaborate with immune cells such as mast cells， neutrophils， and M0 macrophages to mediate disease development. The Xiezhuo Jiedu formula may regulate the expression of core Pyro-DEGs through the NOD-like receptor， TNF， and HIF-1 core signaling pathways， thereby modulating immune homeostasis in UC rats and effectively alleviating UC.

Objective: To evaluate the feasibility of dynamic contrast-enhanced MRI (DCE-MRI) in differentiating tumor deposits (TDs) from metastatic lymph nodes (MLNs) in rectal cancer. Materials and Methods: A retrospective analysis was conducted on 70 patients with rectal cancer, including 168 lesions (70 TDs and 98 MLNs confirmed by histopathology), who underwent pretreatment MRI and subsequent surgery between March 2019 and December 2022. The morphological characteristics of TDs and MLNs, along with quantitative parameters derived from DCE-MRI (K trans , kep, and v e) and DWI (ADCmin, ADCmax, and ADCmean), were analyzed and compared between the two groups.Multivariable binary logistic regression and receiver operating characteristic (ROC) curve analyses were performed to assess the diagnostic performance of significant individual quantitative parameters and combined parameters in distinguishing TDs from MLNs. Results: All morphological features, including size, shape, border, and signal intensity, as well as all DCE-MRI parameters showed significant differences between TDs and MLNs (all P < 0.05). However, ADC values did not demonstrate significant differences (all P > 0.05). Among the single quantitative parameters, v e had the highest diagnostic accuracy, with an area under the ROC curve (AUC) of 0.772 for distinguishing TDs from MLNs. A multivariable logistic regression model incorporating short axis, border, v e, and ADC mean improved diagnostic performance, achieving an AUC of 0.833 (P = 0.027). Conclusion The combination of morphological features, DCE-MRI parameters, and ADC values can effectively aid in the preoperative differentiation of TDs from MLNs in rectal cancer.

Objective: To evaluate the feasibility of dynamic contrast-enhanced MRI (DCE-MRI) in differentiating tumor deposits (TDs) from metastatic lymph nodes (MLNs) in rectal cancer. Materials and Methods: A retrospective analysis was conducted on 70 patients with rectal cancer, including 168 lesions (70 TDs and 98 MLNs confirmed by histopathology), who underwent pretreatment MRI and subsequent surgery between March 2019 and December 2022. The morphological characteristics of TDs and MLNs, along with quantitative parameters derived from DCE-MRI (K trans , kep, and v e) and DWI (ADCmin, ADCmax, and ADCmean), were analyzed and compared between the two groups.Multivariable binary logistic regression and receiver operating characteristic (ROC) curve analyses were performed to assess the diagnostic performance of significant individual quantitative parameters and combined parameters in distinguishing TDs from MLNs. Results: All morphological features, including size, shape, border, and signal intensity, as well as all DCE-MRI parameters showed significant differences between TDs and MLNs (all P < 0.05). However, ADC values did not demonstrate significant differences (all P > 0.05). Among the single quantitative parameters, v e had the highest diagnostic accuracy, with an area under the ROC curve (AUC) of 0.772 for distinguishing TDs from MLNs. A multivariable logistic regression model incorporating short axis, border, v e, and ADC mean improved diagnostic performance, achieving an AUC of 0.833 (P = 0.027). Conclusion The combination of morphological features, DCE-MRI parameters, and ADC values can effectively aid in the preoperative differentiation of TDs from MLNs in rectal cancer.

Objective: To evaluate the feasibility of dynamic contrast-enhanced MRI (DCE-MRI) in differentiating tumor deposits (TDs) from metastatic lymph nodes (MLNs) in rectal cancer. Materials and Methods: A retrospective analysis was conducted on 70 patients with rectal cancer, including 168 lesions (70 TDs and 98 MLNs confirmed by histopathology), who underwent pretreatment MRI and subsequent surgery between March 2019 and December 2022. The morphological characteristics of TDs and MLNs, along with quantitative parameters derived from DCE-MRI (K trans , kep, and v e) and DWI (ADCmin, ADCmax, and ADCmean), were analyzed and compared between the two groups.Multivariable binary logistic regression and receiver operating characteristic (ROC) curve analyses were performed to assess the diagnostic performance of significant individual quantitative parameters and combined parameters in distinguishing TDs from MLNs. Results: All morphological features, including size, shape, border, and signal intensity, as well as all DCE-MRI parameters showed significant differences between TDs and MLNs (all P < 0.05). However, ADC values did not demonstrate significant differences (all P > 0.05). Among the single quantitative parameters, v e had the highest diagnostic accuracy, with an area under the ROC curve (AUC) of 0.772 for distinguishing TDs from MLNs. A multivariable logistic regression model incorporating short axis, border, v e, and ADC mean improved diagnostic performance, achieving an AUC of 0.833 (P = 0.027). Conclusion The combination of morphological features, DCE-MRI parameters, and ADC values can effectively aid in the preoperative differentiation of TDs from MLNs in rectal cancer.

Objective: To evaluate the feasibility of dynamic contrast-enhanced MRI (DCE-MRI) in differentiating tumor deposits (TDs) from metastatic lymph nodes (MLNs) in rectal cancer. Materials and Methods: A retrospective analysis was conducted on 70 patients with rectal cancer, including 168 lesions (70 TDs and 98 MLNs confirmed by histopathology), who underwent pretreatment MRI and subsequent surgery between March 2019 and December 2022. The morphological characteristics of TDs and MLNs, along with quantitative parameters derived from DCE-MRI (K trans , kep, and v e) and DWI (ADCmin, ADCmax, and ADCmean), were analyzed and compared between the two groups.Multivariable binary logistic regression and receiver operating characteristic (ROC) curve analyses were performed to assess the diagnostic performance of significant individual quantitative parameters and combined parameters in distinguishing TDs from MLNs. Results: All morphological features, including size, shape, border, and signal intensity, as well as all DCE-MRI parameters showed significant differences between TDs and MLNs (all P < 0.05). However, ADC values did not demonstrate significant differences (all P > 0.05). Among the single quantitative parameters, v e had the highest diagnostic accuracy, with an area under the ROC curve (AUC) of 0.772 for distinguishing TDs from MLNs. A multivariable logistic regression model incorporating short axis, border, v e, and ADC mean improved diagnostic performance, achieving an AUC of 0.833 (P = 0.027). Conclusion The combination of morphological features, DCE-MRI parameters, and ADC values can effectively aid in the preoperative differentiation of TDs from MLNs in rectal cancer.