OBJECTIVE To confirm trigger items for adverse drug reaction （ADR） induced by bevacizumab， to identify and analyze the occurrence of related ADR， and to establish a predictive model for severe adverse reaction （SAR） caused by this drug. METHODS Based on the global trigger tool （GTT） theory， and referencing the GTT White Paper， drug package inserts and relevant literature， trigger items for bevacizumab-related ADR were confirmed using a single-round Delphi method. Utilizing these established items， electronic medical records of relevant patients at Guilin People’s Hospital from January 2020 to September 2024 were actively screened via the China Hospital Pharmacovigilance System. Pharmacists then identified and tallied the occurrence of bevacizumab-induced ADR. Data from patients with any positive trigger item served as the study subjects （divided into training and test sets at a ratio of 7∶3）， candidate feature variables were selected from 39 related variables using the Boruta algorithm， and the multivariable Logistic regression analysis was performed with the occurrence of SAR as the dependent variable. Based on these candidate features， Logistic Regression， Extreme Gradient Boosting， Light Gradient Boosting Machine， Random Forest， and Categorical Boosting models were constructed. Model performance was evaluated using metrics including the area under the curve （AUC） of receiver operating characteristic curve and recall rate. The Shapley Additive exPlanations （SHAP） method was applied to analyze and interpret the contribution of each variable. A nomogram was constructed based on the optimal model. RESULTS A total of 38 trigger items for active monitoring of bevacizumab-related ADR were determined， comprising 17 laboratory indicators， 13 clinical manifestations， and 8 intervention measures. In total， 483 patients with positive trigger items were included， and 318 patients with bevacizumab-induced ADR were identified， including 83 SARs. The positive predictive values for the trigger items and cases were 43.57% （708/1 625） and 63.84% （318/483）， respectively. Bevacizumab-induced ADR involved 7 systems/organs， with the hematological system being the most frequently involved （64.15%）. The Boruta algorithm selected 7 vari ables： serum potassium， hematocrit， albumin-to-globulin ratio， prealbumin， hypertension history， age and red blood cell count. Multivariable Logistic regression showed that elevated serum potassium levels were associated with a decreased risk of bevacizumab-induced SAR （OR＝0.234， P ＝0.002）， while a history of hypertension （OR＝2.642， P ＝0.006） and increased age （OR＝1.040， P ＝0.025） were associated with an increased risk. The Logistic Regression model demonstrated superior performance with higher AUC， F1 score and recall rate （0.761， 0.447， 0.607）， compared to other models. SHAP evaluation results indicated that variables such as serum potassium， hematocrit， and age ranked highest in importance. CONCLUSIONS Totally 38 trigger entries have been successfully identified for active screening of bevacizumab-related ADR. Elevated serum potassium levels are a protective factor against bevacizumab-induced SAR， whereas the hypertension history and increased age are risk factors. The Logistic Regression model is the optimal predictive model.

Background/Aims: Acetyl coenzyme A (acetyl-CoA) is one of the most essential metabolites in cell metabolism but its function and concentration in hepatocellular carcinoma (HCC) remain elusive and controversial. Methods: A comprehensive analysis of acetyl-CoA levels and acetyl-CoA synthetase 2 (ACSS2) expression across a range of samples, including patient specimens from both hepatitis B virus (HBV) positive and HBV negative HCC individuals, HBV-transgenic mouse HCC models, and multiple cell lines. Furthermore, to evaluate the functional significance of ACSS2 in HBV-related HCC, we implemented both genetic and pharmacological inhibition strategies targeting ACSS2. Molecular mechanism and mitophagy assessment were revealed by cleavage under target and tagmentation sequencing, RNA sequencing, bioinformatic analyses, transmission electron microscopy and JC-1 staining. Results: Our study revealed a distinct metabolic signature of HBV-related HCC, marked by elevated acetyl-CoA, which was driven by ACSS2. ACSS2 was upregulated by the carbohydrate response element-binding protein in HBV-related HCC. Furthermore, ACSS2 improved tumor cell proliferation, an effect that was dependent on its enzymatic activity. Mechanistically, ACSS2-induced acetyl-CoA accumulation activated voltage-dependent anion channels 1 transcription through increased H3K27ac occupancy, which subsequently promoted mitophagy and HBV-related HCC tumorigenesis. Notably, targeting ACSS2 by depletion or inhibition with a catalytic inhibitor significantly suppressed tumor growth. Conclusions These findings not only illustrate the interplay between metabolic reprogramming, epigenetic modification, and tumorigenesis in the context of HBV infection, but also highlight ACSS2 as a novel metabolic vulnerability in HBV-related HCC. Therefore, targeting ACSS2 could be a novel strategy against HBV-related HCC.

Intelligent prescription recommendation has become an important research direction in traditional Chinese medicine (TCM), offering new opportunities to support clinical decision-making and promote the modernization of TCM practice. With the rapid development of artificial intelligence (AI), a variety of computational approaches have been proposed to learn prescription patterns from clinical data and generate personalized treatment recommendations. However, despite increasing research activity, systematic and comprehensive reviews of AI-driven methods for TCM prescription recommendation remain limited. In this study, we present a comprehensive review of computational approaches for herbal prescription recommendation (HPR) in TCM. Existing methods are systematically categorized into several major paradigms, including traditional machine learning methods, topic model methods, sequential generative methods, deep learning and graph-based methods, and large language model–based frameworks. In addition to summarizing methodological developments, we also review commonly used public datasets and evaluation metrics in this field. Furthermore, representative models with publicly available implementations are experimentally evaluated on multiple benchmark datasets to provide a comparative analysis of their performance on the HPR task. Finally, we discuss the key challenges that hinder the practical deployment of intelligent prescription recommendation systems, including data heterogeneity, limited interpretability, and insufficient integration of TCM domain knowledge. Future research directions are outlined to facilitate the development of more reliable, interpretable, and clinically applicable AI-assisted HPR systems for TCM.

Objective To compare and find an optimal model for predicting the risk of DKD occurrence in patients with type 2 diabetes mellitus(T2DM).Methods A total of 2005 patients with T2DM were enrolled in this study from The Second Hospital of Shijiazhuang City during December 2017 to December 2022.All the subjects were divided into a training set(n=1403)and a validation set(n=602)according to the ratio of 3∶1 by simple random sampling.With the occurrence of DKD as the outcome variablein the training set,important feature variables were screened by LASSO regression.Six different machine learning models were established according to the feature variables,thenthe optimal model was determined by comparison,and anonlinerisk predictor for DKD occurrence was constructed in patients with T2DM.Results Taking the occurrence of DKD as the outcome variable in the training set,the results of LASSO regression analysis showed that the optimal value of the model was 10-fold cross validation lambda.1se=0.01662473,and 15 characteristic variables with nonzero coefficient were screened out to be related to the occurrence of DKD.The data included sex,age,family history of DM,DM duration,LDL-C,HbA1c,WBC,PDW,Scr,urine α1-microglobulin,urine β2-microglobulin,urine microalbumin,hypertension,hypokalemia,and DR.In the training set and validation set,the prediction performance of XGBoost model was better than that of other models(AUC=0.872,0.893,95%CI 0.853～0.891,0.865～0.921),the sensitivity was 0.779,0.863,and the specificity was 0.721,0.758,respectively.The F1 scores were 0.774 and 0.787.DCA analysis showed that the XGBoost model had a greater net benefit and threshold probability.According to the XGBoost model,the online predictor of DKD risk in T2DM patients was laid out,and two patients were selected for application,the results showed that the predictive value of the model was 0.185 in non-DKD patients,and the predictive value was 0.510 in DKD patients.Conclusions The XGBoost model is the best model for predicting the occurrence of DKD in T2DM patients,and an online predictor was successfully built.

Objective:This multicenter retrospective study aimed to analyze the clinicopathological features of duodenal adenocarcinoma (DA) and identify prognostic factors for postoperative survival.Methods:Demographic characteristics, clinicopathological features, treatment outcomes and survival of DA patients undergoing surgical treatment at 18 Chinese medical centers from January 2012 to December 2023 were retrospectively analyzed.Results:Among the 2 056 DA patients included, 46.8% (963) had extra-ampullary DA (EA-DA), and 53.2% (1 093) had peri-ampullary DA (PA-DA). The 1-, 3-, and 5-year overall survival (OS) rates for patients who underwent radical surgery were 93.2%, 71.0%, and 57.2%, respectively. The median overall survival was 76 months, and the median progression-free survival (PFS) was 65 months. No differences in survival were observed between the laparotomy group and minimally invasive surgery (MIS) group either before or after propensity score matching (OS: 76 vs. 75 months before PSM, P=0.986; OS: 75 vs. 75 months after PSM, P=0.602). Furthermore, there were no significant differences between-group in operation time and postoperative complications ( P＞0.05). The MIS group experienced less intraoperative blood loss and shorter hospital stays. The multivariate Cox regression analysis revealed that advanced age ( HR=1.43,95% CI:1.18-1.73), elevated carbohydrate antigen 19-9 levels ( HR=1.24,95% CI:1.02-1.51), perineural invasion ( HR=1.44,95% CI:1.14-1.81), vascular invasion ( HR=1.35,95% CI:1.07-1.71), advanced T stage (T3-4 vs. T1-2: HR=1.86,95% CI:1.49-2.31), regional lymph node metastasis ( HR=1.93,95% CI:1.58-2.36), preoperative biliary drainage ( HR=1.26,95% CI:1.04-1.53), intraoperative blood loss ( HR=1.34,95% CI:1.11-1.62), clinically significant postoperative pancreatic fistulas ( HR=1.53,95% CI:1.12-2.09), and postoperative hemorrhage ( HR=1.62,95% CI:1.14-2.29) were independent risk factors for poor prognosis after surgery (all P＜0.05). Conclusions:Radical surgery is associated with favorable overall survival among DA patients, and no difference in survival is observed between EA-DA and PA-DA patients. MIS is a reliable alternative for DA treatment.

Gastric cancer is a high-incidence malignancy that poses a serious threat to public health in China, ranking among the top three cancers in both incidence and mortality. The majority of patients are diagnosed at an advanced stage, resulting in limited treatment options and poor prognosis. To address key challenges in gastric cancer diagnosis and treatment, a research team led by Professor Jiafu Ji at Peking University Cancer Hospital has focused on the project "Development and Dissemination of Precision Medicine Approaches in Gastric Cancer Management". Through a series of high-quality multicenter clinical studies, the team established a set of new international standards in perioperative treatment, individua-lized drug selection, intelligent noninvasive diagnostics, and novel immunotherapy strategies. These advances have significantly improved treatment efficacy and reduced surgical trauma, achieving key technological breakthroughs in diagnosis, therapy, and mechanistic understanding, and systematically enhancing outcomes for gastric cancer patients. The project ' s findings had a broad international impact, including hosting China ' s first International Gastric Cancer Congress. Through nationwide dissemination, they have promoted the development of precision diagnosis and treatment of gastric cancer as a discipline, and led the formulation of the National Health Commission's guidelines for gastric cancer diagnosis and treatment. In recognition of its achievements, the project was awarded the First Prize of the 2024 Chinese Medical Science and Technology Award.
Stomach Neoplasms/genetics* ; Humans ; Precision Medicine/methods* ; China ; Immunotherapy/methods*

OBJECTIVE: To evaluate the dynamic changes of glucocorticoid (GC) dose and the feasibility of GC discontinuation in rheumatoid arthritis (RA) patients under the background of Chinese medicine (CM). METHODS: This multicenter retrospective cohort study included 1,196 RA patients enrolled in the China Rheumatoid Arthritis Registry of Patients with Chinese Medicine (CERTAIN) from September 1, 2019 to December 4, 2023, who initiated GC therapy. Participants were divided into the Western medicine (WM) and integrative medicine (IM, combination of CM and WM) groups based on medication regimen. Follow-up was performed at least every 3 months to assess dynamic changes in GC dose. Changes in GC dose were analyzed by generalized estimator equation, the probability of GC discontinuation was assessed using Kaplan-Meier curve, and predictors of GC discontinuation were analyzed by Cox regression. Patients with <12 months of follow-up were excluded for the sensitivity analysis. RESULTS: Among 1,196 patients (85.4% female; median age 56.4 years), 880 (73.6%) received IM. Over a median 12-month follow-up, 34.3% (410 cases) discontinued GC, with significantly higher rates in the IM group (40.8% vs. 16.1% in WM; P<0.05). GC dose declined progressively, with IM patients demonstrating faster reductions (median 3.75 mg vs. 5.00 mg in WM at 12 months; P<0.05). Multivariate Cox analysis identified age <60 years [P<0.001, hazard ratios (HR)=2.142, 95% confidence interval (CI): 1.523-3.012], IM therapy (P=0.001, HR=2.175, 95% CI: 1.369-3.456), baseline GC dose ⩽7.5 mg (P=0.003, HR=1.637, 95% CI: 1.177-2.275), and absence of non-steroidal anti-inflammatory drugs use (P=0.001, HR=2.546, 95% CI: 1.432-4.527) as significant predictors of GC discontinuation. Sensitivity analysis (545 cases) confirmed these findings. CONCLUSIONS RA patients receiving CM face difficulties in following guideline-recommended GC discontinuation protocols. IM can promote GC discontinuation and is a promising strategy to reduce GC dependency in RA management. (Trial registration: ClinicalTrials.gov, No. NCT05219214).
Adult ; Aged ; Female ; Humans ; Male ; Middle Aged ; Arthritis, Rheumatoid/drug therapy* ; Glucocorticoids/therapeutic use* ; Medicine, Chinese Traditional ; Retrospective Studies

Traditional Chinese medicine formula (TCMF) represents a fundamental component of Chinese medical practice, incorporating medical knowledge and practices from both Han Chinese and various ethnic minorities, while providing comprehensive insights into health and disease. The foundation of TCMF lies in its holistic approach, manifested through herbal compatibility theory, which has emerged from extensive clinical experience and evolved into a highly refined knowledge system. Within this framework, Chinese herbal medicines exhibit intricated characteristics, including multi-component interactions, diverse target sites, and varied biological pathways. These complexities pose significant challenges for understanding their molecular mechanisms. Contemporary advances in artificial intelligence (AI) are reshaping research in traditional Chinese medicine (TCM), offering immense potential to transform our understanding of the molecular mechanisms underlying TCMFs. This review explores the application of AI in uncovering these mechanisms, highlighting its role in compound absorption, distribution, metabolism, and excretion (ADME) prediction, molecular target identification, compound and target synergy recognition, pharmacological mechanisms exploration, and herbal formula optimization. Furthermore, the review discusses the challenges and opportunities in AI-assisted research on TCMF molecular mechanisms, promoting the modernization and globalization of TCM.
Artificial Intelligence ; Drugs, Chinese Herbal/pharmacokinetics* ; Humans ; Medicine, Chinese Traditional ; Animals

In current clinical practice, various dermal templates and skin substitutes are used to enhance wound healing. However, the role of wound commensal microbiome in regulating scaffold performance and the healing process remains unclear. In this study, we investigated the influence of both natural and synthetic scaffolds on the wound commensal microbiome and wound repair in three distinct models including diabetic wounds, burn injuries, and germ-free (GF) wounds. Remarkably, synthetic electrospun polycaprolactone (PCL) scaffolds were observed to positively promote microbiome diversity, leading to enhanced diabetic wound healing compared to the natural scaffolds Integra® (INT) and MatriDerm® (MAD). In contrast, both natural and synthetic scaffolds exhibited comparable effects on the diversity of the microbiome and the healing of burn injuries. In GF wounds with no detectable microorganisms, a reversed healing rate was noted showing natural scaffold (MAD) accelerated wound repair compared to the open or the synthetic scaffold (PCL) treatment. Furthermore, the response of the wound commensal microbiome to PCL scaffolds appears pivotal in promoting anti-inflammatory factors during diabetic wound healing. Our results emphasize that the wound commensal microbiome, mediated by different scaffolds plays an important role in the wound healing process.

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