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.

ObjectiveTo evaluate the clinical efficacy and safety of Qidan Tangshen Granules （芪丹糖肾颗粒， QTG） in the treatment of early diabetic kidney disease （DKD） with qi deficiency， blood stasis， and kidney deficiency syndrome， and to explore its mechanism. MethodsA double-blind， double-simulated method was used to enroll 200 patients with early DKD and qi deficiency， blood stasis， and kidney deficiency syndrome. Patients were randomly assigned in a 1∶1 ratio to the treatment group （100 cases） and the control group （100 cases）. The treatment group received QTG plus a valsartan capsule simulant， while the control group received valsartan capsules plus a QTG simulant， both for 12 weeks. The primary outcome was the urinary albumin-to-creatinine ratio （UACR）. Secondary outcomes included estimated glomerular filtration rate （eGFR）， fasting blood glucose （FBG）， 2-hour postprandial blood glucose （PBG）， glycated hemoglobin （HbA1c）， and traditional Chinese medicine （TCM） syndrome scores （including individual symptom scores for fatigue， dull complexion， soreness and weakness of the waist and knees， headache and chest pain， irritability， spontaneous sweating， thirst and polydipsia， polyphagia， polyuria， numbness of the limbs， and the total TCM syndrome score）. Oxidative stress markers including serum 8-hydroxy-2'-deoxyguanosine （8-OHDG）， 3-nitrotyrosine （3-NT）， and superoxide dismutase （SOD） were also assessed. Clinical efficacy and TCM syndrome efficacy were evaluated after treatment， and routine blood tests， urinalysis， and liver function tests were conducted and adverse reaction during the tria was recorded to assess safety. ResultsA total of 191 patients completed the study （95 in the treatment group and 96 in the control group）. The treatment group showed significant reductions in UACR， FBG， PBG， and HbA1c levels after treatment （P＜0.05 or P＜0.01）. The single TCM symptom scores except for polyphagia and total TCM syndrome scores significantly decreased （P＜0.05 or P＜0.01）. Compared to the control group， the treatment group had signi-ficantly lower UACR， FBG， PBG levels， and total TCM syndrome scores， sinlge symptoms scores except for polyphagia and limb numbness （P＜0.05 or P＜0.01）. Among 40 randomly selected patients （21 cases in the treatment group and 19 cases in the control group） for oxidative stress analysis， there were no significant differences in SOD， 3-NT， and 8-OHDG levels before and after treatment within or between groups （P＞0.05）. The overall effective rate in the treatment group was 64.2% （61/95） and 39.6% （38/96） in the control group， while the TCM syndrome efficacy rates were 80.0% （76/95） and 24.0% （23/96）， respectively， with the treatment group showing superior efficacy （P＜0.01）. No significant differences were observed in routine blood tests， urinalysis， or liver function indices before and after treatment in either group （P＞0.05）. The incidence of adverse reactions was 8.4% （8/95） in the treatment group and 9.4% （9/96） in the control group， with no statistically significant difference （P＞0.05）. ConclusionQTG can effectively reduce UACR and blood glucose levels， alleviate clinical symptoms， and improve clinical efficacy in patients with early DKD with qi deficiency， blood stasis， and kidney deficiency syndrome. The treatment is well-tolerated and safe， with no significant impact on oxidative stress markers.

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.

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.