Simultaneous management of intestinal mucosal barrier dysfunction and gut microbiota dysregulation represents a significant challenge in the treatment of inflammatory bowel disease (IBD). Herein, we report a novel system that integrates multi-enzyme mimicking cerium single-atom nanocatalysts (CeSACs) with Lactobacillus reuteri probiotics (LR@CeSACs) for multipronged management of IBD. In this system, CeSACs demonstrate robust multi-enzyme activities across a broad pH range, effectively scavenging elevated reactive oxygen species, downregulating pro-inflammatory cytokines, and suppressing the expression of fibrosis-related genes. Moreover, probiotics promote the targeting and retention of the CeSACs for sustained catalytic antioxidant therapy. In turn, the inflammation relief enabled by CeSACs promotes bacterial viability, allowing for the rapid reshaping of intestinal barrier function and the restoration of gut microbiota. Therefore, LR@CeSACs exhibit excellent catalytic anti-inflammatory and anti-fibrotic therapeutic effects, as well as a certain prophylactic effect, as demonstrated in several murine models.

Inflammatory bowel disease (IBD) is an autoimmune disorder involving complex immune regulation, where balancing localized and systemic immunosuppression is a key challenge. This study aimed to enhance the therapeutic efficacy by engineering the probiotic Escherichia coli Nissle 1917 (EcN). We removed endogenous plasmids pMUT1 and pMUT2 from wild-type EcN and expressed the mPD-L1 (19‒238 aa)-mFc fusion protein on the bacterial surface using a cytolysin A (ClyA) fragment. This modification stabilized mPD-L1 (19‒238 aa) protein expression and promoted its recruitment to outer membrane vesicles (OMVs). The engineered strain, EcNΔ_pMUT1/2-ClyA-mPD-L1-mFc (EcN-ePD-L1-mFc), features conditional ePD-L1-mFc expression under the araBAD promoter, enhancing gut-targeted release and reducing systemic side effects. This strain improved treatment targeting and efficiency by enabling direct ePD-L1-mFc interaction with immune cells at inflammation sites. OMVs from this strain induced Treg proliferation, inhibited effector T cell proliferation in vitro, and significantly improved intestinal inflammation and colonic epithelial barrier repair in vivo. Additionally, the bacterium restored intestinal microbiota balance, increasing Lactobacillaceae and reducing Bacteroides. This study highlights the engineered bacterium's potential for targeted intestinal immune modulation and offers a novel local IBD treatment approach with promising clinical prospects.

BACKGROUND: Postoperative pulmonary complications (PPCs) are major adverse events in neurosurgical patients. This study aimed to develop and validate machine learning models predicting PPCs after neurosurgery. METHODS: PPCs were defined according to the European Perioperative Clinical Outcome standards as occurring within 7 postoperative days. Data of cases meeting inclusion/exclusion criteria were extracted from the anesthesia information management system to create three datasets: The development (data of Huashan Hospital, Fudan University from 2018 to 2020), temporal validation (data of Huashan Hospital, Fudan University in 2021) and external validation (data of other three hospitals in 2023) datasets. Machine learning models of six algorithms were trained using either 35 retrievable and plausible features or the 11 features selected by Lasso regression. Temporal validation was conducted for all models and the 11-feature models were also externally validated. Independent risk factors were identified and feature importance in top models was analyzed. RESULTS: PPCs occurred in 712 of 7533 (9.5%), 258 of 2824 (9.1%), and 207 of 2300 (9.0%) patients in the development, temporal validation and external validation datasets, respectively. During cross-validation training, all models except Bayes demonstrated good discrimination with an area under the receiver operating characteristic curve (AUC) of 0.840. In temporal validation of full-feature models, deep neural network (DNN) performed the best with an AUC of 0.835 (95% confidence interval [CI]: 0.805-0.858) and a Brier score of 0.069, followed by Logistic regression (LR), random forest and XGBoost. The 11-feature models performed comparable to full-feature models with very close but statistically significantly lower AUCs, with the top models of DNN and LR in temporal and external validations. An 11-feature nomogram was drawn based on the LR algorithm and it outperformed the minimally modified Assess respiratory RIsk in Surgical patients in CATalonia (ARISCAT) and Laparoscopic Surgery Video Educational Guidelines (LAS VEGAS) scores with a higher AUC (LR: 0.824, ARISCAT: 0.672, LAS: 0.663). Independent risk factors based on multivariate LR mostly overlapped with Lasso-selected features, but lacked consistency with the important features using the Shapley additive explanation (SHAP) method of the LR model. CONCLUSIONS: The developed models, especially the DNN model and the nomogram, had good discrimination and calibration, and could be used for predicting PPCs in neurosurgical patients. The establishment of machine learning models and the ascertainment of risk factors might assist clinical decision support for improving surgical outcomes. TRIAL REGISTRATION ChiCTR 2100047474; https://www.chictr.org.cn/showproj.html?proj=128279 .
Adult ; Aged ; Female ; Humans ; Male ; Middle Aged ; Algorithms ; Lung Diseases/etiology* ; Machine Learning ; Neurosurgical Procedures/adverse effects* ; Postoperative Complications/diagnosis* ; Risk Factors ; ROC Curve

Drug toxicity is closely related to both clinical drug safety and new drug development. Therefore, it is vital to understand the mechanisms of drug toxicity fully and to use appropriate research models with advanced technologies. Zebrafish has become an important vertebrate animal model for high-throughput drug screening and toxicity assessment. At the same time, zebrafish has an intact biological complexity, reflecting the whole organism's toxicity, which gives it an advantage over other high-throughput models in toxicity studies. Despite the gradual increase in toxicity studies utilizing zebrafish, a comprehensive and systematic review of the underlying mechanisms and new techniques is still lacking. This review aims to analyze common toxicity mechanisms in zebrafish models, such as oxidative stress, endoplasmic reticulum stress, inflammation, and apoptosis, and macroscopic changes in biological processes like lipid metabolism disorders and neurotransmitter expression abnormalities. It also introduces new technologies applied in toxicity assessment, such as gene editing, novel fluorescence imaging technology, 3D imaging technology, and novel automated technology for high-throughput screening, such as fish capsules. In addition, it also summarizes the advantages and disadvantages of the model. By doing so, it will provide new suggestions for the development and improvement of the model, make it better serve the toxicity study of clinical drugs and provide a more comprehensive perspective for drug toxicity study, thus promoting the development of the field of drug toxicity study.

ObjectiveTo compare the effects and differences of Yiqi Liangxue Shengji Formula （益气凉血生肌方） and atorvastatin on the repair of vascular injury in rats from the perspective of metabolomics. MethodsTwenty-four male SD rats were randomly divided into sham-surgery， model， traditional Chinese medicine （TCM）， and ator-vastatin groups， with 6 rats in each group. The rat model was established by balloon-induced abdominal aorta injury. Gavage was started on the day after surgery in all groups of rats. The sham and model groups were given with deio-nized water， TCM group received Yiqi Liangxue Shengji Formula 6 g/（kg·d）， and the atorvastatin group treated with atorvastatin suspension 2 mg/（kg·d） for 4 weeks. HE staining was used to observe the pathological morphology of the injured segment of the abdominal aorta； ELISA detection was used to test serum nitric oxide （NO） and C-reactive protein （CRP） levels； UPLC MS/MS technology was used for widely targeted metabolomics detection in serum， and multivariate statistical analysis was used to screen metabolic markers and pathways of two drugs； finally， compare serum levels of key metabolic markers of the above two medications in rats of each group. ResultsCompared with the sham-surgery group， the neointima significantly thickened， the level of NO decreased significantly and the level of CRP increased in serum of the model group （P＜0.01）； compared with the model group， the degree of arterial intimal hyperplasia in TCM group and atorvastatin group reduced， with an increase in NO levels and a decrease in CRP levels （P＜ 0.05 or P＜0.01）. The results of serum metabolomics showed that TCM group obtained 49 metabolic markers and 6 metabolic pathways， while atorvastatin group obtained 41 metabolic markers and 4 metabolic pathways. The two medications jointly regulated 38 metabolites. Glycerophospholipid metabolism and arginine-related metabolism were common metabolic pathways for both medications. Lysophosphatidylcholine （16∶1/0∶0） ［LPC （16∶1/ 0∶0）］， phosphatidylcholine （15∶0/15∶0） ［PC （15∶0/15∶0）］ were the key metabolites of glycerophospholipid metabolic pathway； ornithine， spermidine were the key metabolites of arginine-related metabolic pathway. The tricarboxylic acid cycle and glutathione metabolism were the unique metabolic pathways of Yiqi Liangxue Shengji Formula. Compared with the sham-surgery group， LPC （16∶1/0∶0）， ornithine， and spermidine levels elevated and PC （15∶0/15∶0） levels decreased in the model group （P＜0.05 or P＜0.01）. Compared with the model group， LPC （16∶1/0∶0）， ornithine， and spermidine levels decreased， and PC （15∶0/15∶0） levels increased in both TCM group and atorvastatin group （P＜0.05 or P＜0.01）. The degree of LPC reduction （16∶1/0∶0） was more significant in atorvastatin group compared with that in the TCM group （P＜0.01）. ConclusionsBoth sham-surgery and atorvastatin could regulate lipid metabolism and arginine-related metabolism， exert the characteristics of lipid-lowering， anti-inflammatory， improve arginine/NO bioavailability， and improve endothelial dysfunction. Atorvastatin showed more advantages in lipid-lowering and anti-inflammatory， while Yiqi Liangxue Shengji Formula has unique characteristics in regulating energy metabolism and improving oxidative stress.

Fanconi anemia (FA) is an inheritable disorder that presents with bone marrow failure, developmental anomalies, and an increased susceptibility to cancer. The etiology of this condition stems from a genetic mutation that disrupts the proper repair of interstrand DNA cross-links (ICLs). The resultant dysregulation of the DNA damage response mechanism can induce genomic instability, thereby elevating the mutation rates and the likelihood of developing cancer. The FA pathway assumes a pivotal role in safeguarding genome stability through its involvement in the repair of DNA cross-links and the maintenance of overall genomic integrity. A mutation in the germ line of any of the genes responsible for encoding the FA protein results in the development of FA. The prevalence of aberrant FA gene expression in somatic cancer, coupled with the identification of a connection between FA pathway activation and resistance to chemotherapy, has solidified the correlation between the FA pathway and cancer. Consequently, targeted therapies that exploit FA pathway gene abnormalities are being progressively developed and implemented. This review critically examines the involvement of the FA protein in the repair of ICLs, the regulation of the FA signaling network, and its implications in cancer pathogenesis and prognosis. Additionally, it explores the potential utility of small-molecule inhibitors that target the FA pathway.

Berberine is an antipyretic and detoxicating drug commonly used in clinical practice,and it is currently used for the routine treatment of gastrointestinal diseases such as bacterial gastroenteritis and diarrhea.However,several recent studies have shown that berberine can exert a therapeutic effect on the diseases such as autoimmune hepatitis,viral hepatitis,nonalcoholic fatty liver disease,and liver cancer by regulating the AMPK and TGF-β pathways and altering the composition of intestinal flora.This provides new drugs for the treatment of these diseases,expands the potential indications of berberine,and provides clues for the follow-up research and development of similar drugs.This article summarizes the therapeutic effect and mechanism of berberine on various liver diseases,in order to provide a reference for effective clinical application.

Promoting wound healing is a common clinical challenge faced by surgeons, with a variety of repair method and materials currently available for clinical use. In recent years, with the continuous development of disciplines such as tissue engineering, regenerative medicine, and materials science, research on wound repair materials has progressed rapidly. This article will organize the classification, advantages and disadvantages, and the latest advancements in the preparation processes of wound repair materials. It will also discuss the current challenges faced by wound repair materials and future research directions, with the aim of providing a reference for related studies in wound repair.

Promoting wound healing is a common clinical challenge faced by surgeons, with a variety of repair method and materials currently available for clinical use. In recent years, with the continuous development of disciplines such as tissue engineering, regenerative medicine, and materials science, research on wound repair materials has progressed rapidly. This article will organize the classification, advantages and disadvantages, and the latest advancements in the preparation processes of wound repair materials. It will also discuss the current challenges faced by wound repair materials and future research directions, with the aim of providing a reference for related studies in wound repair.

Objective:To establish a risk prediction model of liver failure after liver resection for hepatocellular carcinoma.Method:A retrospective case-control study was designed. Clinical data and laboratory results, including gender, age, and preoperative 18 laboratory indicators, were collected from 320 patients with hepatocellular carcinoma undergoing liver resection in Eastern Hepatobiliary Surgery Hospital Affiliated to Naval Medical University from January 1, 2013 to December 31, 2023. According to the surgical time, 252 cases in the training cohort were divided into 62 and 190 cases with and without postoperative liver failure, respectively. Of the 68 cases in validation cohort, 34 developed postoperative liver failure and 34 did not. Binary Logistic regression analysis was used to conduct univariate analysis of gender, age, and 18 preoperative laboratory indicators, and multivariate analysis was carried out for significant results to determine the influencing factors of liver failure after liver resection for hepatocellular carcinoma, and Logistic regression model was established.Result:In the training cohort, indicators significantly associated with liver failure after liver resection for hepatocellular carcinoma included age ( P=0.016), platelets ( P=0.005), prealbumin ( P<0.001), and alkaline phosphatase ( P<0.001). Logistic regression was used to construct a nomogram model and draw a calibration curve by combining these four indicators. In the training cohort, the nomogram model showed good discriminability in predicting the risk of liver failure after hepatectomy for hepatocellular carcinoma. The area under the curve of was 0.82 (95% CI 0.76-0.88), and the sensitivity was 73% and specificity was 80% when the optimal cut-off value was 0.2646. In the validation cohort, the predictive performance of the nomogram model was comparable to that of the training cohort, with an area under the curve of 0.81 (95% CI 0.71-0.92), sensitivity of 82%, and specificity of 77%. Conclusion:Preoperative platelet and prealbumin decreases, alkaline phosphatase increases, and elderly patients are prone to liver failure after liver resection. The nomogram model constructed with preoperative test data has shows good discriminatory ability and accuracy in predicting liver failure after liver resection for hepatocellular carcinoma.