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.

Objective To investigate the influence of plasma infusion during orthotopic liver transplantation on the incidence of acute kidney injury (AKI) in recipients. Methods Cinical data of 473 liver transplant recipients who underwent orthotopic liver transplantation at Beijing Chaoyang Hospital Affiliated to Capital Medical University from January 2016 to December 2020 were retrospectively collected. The study included 354 recipients who received plasma infusion during the operation (plasma group) and 119 recipients who did not receive plasma infusion during the operation (control group). Preoperative conditions, donor conditions, intraoperative conditions, main outcome indicators and secondary outcome indicators of the two groups were analyzed and compared. Receiver operating characteristic curve was drawn to calculate the maximum cut-off value of intraoperative plasma infusion volume that affected the occurrence of AKI within 7 days after surgery. Logistic regression analysis was performed to analyze the correlation between intraoperative plasma infusion volume and the incidence of AKI within 7 days after surgery. Results Before propensity score matching, the incidence of AKI within 7 days after surgery and the incidence of grade Ⅲ AKI in the plasma group were higher than those in the control group (both P<0.05). After propensity score matching, 62 recipients were included in each group. There was no statistically significant difference in the incidence of AKI within 7 days after surgery between the plasma group and the control group, but the incidence of grade Ⅲ AKI within 7 days after surgery in the plasma group was higher than that in the control group (P=0.041). Logistic regression analysis showed that intraoperative plasma infusion volume >900 mL was a potential risk factor for AKI within 7 days after surgery (odds ratio=1.936, 95% confidence interval 1.193-3.142, P=0.007). There were no statistically significant differences in the incidence of 365-day postoperative fatality, reperfusion syndrome, and postoperative 30-day complications between the two groups before and after propensity score matching. In addition, the postoperative albumin, fibrinogen levels, and international normalized ratio in the plasma group were better than those in the control group before and after matching (all P<0.05). Conclusions Large amount of intraoperative plasma infusion is associated with an increased risk of grade Ⅲ AKI after orthotopic liver transplantation. Intraoperative plasma infusion volume >900 mL may increase the risk of AKI within 7 days after surgery.

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 construct programmed cell death protein-ligand 1(PD-LI)^hi tolerogenic dendritic cell (tol-DC) derived from bone marrow of DA rats and identify its immunological function. Methods DA rat bone marrow cells were extracted, combined with recombinant mouse granulocyte macrophage colony-stimulating factor and recombinant mouse interleukin (IL)-4, and cultured for 6 days in vitro to induce the differentiation of bone marrow cells into immature dendritic cells (imDC). Lipopolysaccharide was used to stimulate cell maturation and cultured for 2 days to collect mature dendritic cells (mDC). PD-L1 lentiviral vector virus stock solution or equivalent dose lentiviral stock solution was added, and PD-L1^hitol-DC and Lv-imDC were collected after culture for 2 days. The morphology of PD-L1^hitol-DC was observed by inverted phase contrast microscope and transmission electron microscope. Real-time fluorescence quantitative reverse transcription polymerase chain reaction, Western blotting and flow cytometry were used to detect the expression level of specific markers on cell surface. CD8⁺T cells derived from Lewis rat spleen were co-cultured with imDC, mDC, Lv-imDC and PD-L1^hitol-DC, respectively. The levels of inflammatory factors in the supernatant of each group were detected by enzyme-linked immunosorbent assay. The apoptosis of T cells and the differentiation of regulatory T cells (Treg) in each group were analyzed by flow cytometry. Results The morphology of PD-L1^hitol-DC modified by PD-L1 gene was consistent with tol-DC characteristics, and the expression levels of CD80, CD86 and major histocompatibility complex (MHC) on the surface were low. After mixed culture with CD8⁺ T cells, the levels of IL-10 and transforming growth factor (TGF) -β1 in the supernatant of PD-L1^hitol-DC group were higher, the levels of tumor necrosis factor (TNF) -α and IL-17A were lower, and the apoptosis of T cells and Treg differentiation were increased. Conclusions Overexpression of PD-L1 through lentiviral vectors may successfully induce the construction of bone-marrow derived PD-L1^hitol-DC in DA rats, promote the secretion of anti-inflammatory factors and T cell apoptosis, induce the differentiation of Treg, and inhibit the immune response of allogeneic CD8⁺T cells, which provides experimental basis for the next organ transplantation immune tolerance study.

Traditional Chinese medicine (TCM) represents a paradigmatic approach to personalized medicine, developed through the systematic accumulation and refinement of clinical empirical data over more than 2000 years, and now encompasses large-scale electronic medical records (EMR) and experimental molecular data. Artificial intelligence (AI) has demonstrated its utility in medicine through the development of various expert systems (e.g., MYCIN) since the 1970s. With the emergence of deep learning and large language models (LLMs), AI's potential in medicine shows considerable promise. Consequently, the integration of AI and TCM from both clinical and scientific perspectives presents a fundamental and promising research direction. This survey provides an insightful overview of TCM AI research, summarizing related research tasks from three perspectives: systems-level biological mechanism elucidation, real-world clinical evidence inference, and personalized clinical decision support. The review highlights representative AI methodologies alongside their applications in both TCM scientific inquiry and clinical practice. To critically assess the current state of the field, this work identifies major challenges and opportunities that constrain the development of robust research capabilities-particularly in the mechanistic understanding of TCM syndromes and herbal formulations, novel drug discovery, and the delivery of high-quality, patient-centered clinical care. The findings underscore that future advancements in AI-driven TCM research will rely on the development of high-quality, large-scale data repositories; the construction of comprehensive and domain-specific knowledge graphs (KGs); deeper insights into the biological mechanisms underpinning clinical efficacy; rigorous causal inference frameworks; and intelligent, personalized decision support systems.
Medicine, Chinese Traditional/methods* ; Artificial Intelligence ; Humans ; Precision Medicine ; Decision Support Systems, Clinical

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: This study investigates the potential of radiomics to predict postoperative progression of ossification of the posterior longitudinal ligament (OPLL) after posterior cervical spine surgery. Methods: This retrospective study included 473 patients diagnosed with OPLL at Peking University Third Hospital between October 2006 and September 2022. Patients underwent posterior spinal surgery and had at least 2 computed tomography (CT) examinations spaced at least 1 year apart. OPLL progression was defined as an annual growth rate exceeding 7.5%. Radiomic features were extracted from preoperative CT images of the OPLL lesions, followed by feature selection using correlation coefficient analysis and least absolute shrinkage and selection operator, and dimensionality reduction using principal component analysis. Univariable analysis identified significant clinical variables for constructing the clinical model. Logistic regression models, including the Rad-score model, clinical model, and combined model, were developed to predict OPLL progression. Results: Of the 473 patients, 191 (40.4%) experienced OPLL progression. On the testing set, the combined model, which incorporated the Rad-score and clinical variables (area under the receiver operating characteristic curve [AUC] = 0.751), outperformed both the radiomics-only model (AUC = 0.693) and the clinical model (AUC = 0.620). Calibration curves demonstrated good agreement between predicted probabilities and observed outcomes, and decision curve analysis confirmed the clinical utility of the combined model. SHAP (SHapley Additive exPlanations) analysis indicated that the Rad-score and age were key contributors to the model’s predictions, enhancing clinical interpretability. Conclusion Radiomics, combined with clinical variables, provides a valuable predictive tool for assessing the risk of postoperative progression in cervical OPLL, supporting more personalized treatment strategies. Prospective, multicenter validation is needed to confirm the utility of the model in broader clinical settings.