Objective: To construct a scientific and perfect evaluation index system of infectious disease prevention and control ability in colleges and universities, so as to provide reference tools for colleges and universities to effectively respond to infectious disease. Methods: The initial framework of the evaluation index system of infectious disease prevention and control ability in colleges and universities was constructed by using literature analysis method. Experts familiar with infectious disease prevention and control or school health work were selected to conduct two rounds( n =16，18) of Delphi expert consultation for determining the evaluation index system. Analytical hierarchy process was used to calculate the index weights and combined weights. About 198 prevention and control personnel were conveniently selected from 3 universities in Inner Mongolia Autonomous Region to comprehensively evaluate the evaluation indicators by using fuzzy comprehensive evaluation method. Results: After two rounds of Delphi consultation questionnaire, the effective recovery rates were 80.0% and 90.0%, the expert authority levels were 0.89 and 0.86, the expert harmony coefficients for Kendall W were 0.166 and 0.310, and the variation coefficient of each index was <0.25. Finally, the evaluation index system of infectious disease prevention and control ability of colleges and universities included 4 first level indicators, 14 second level indicators and 75 third level indicators. The weights of prevention and monitoring and early warning, organizational system guarantee, emergency management, rehabilitation and summary were 0.176, 0.476, 0.268 and 0.080, respectively. The top 3 weights of the secondary indexes were 0.623 for infectious disease surveillance and early warning, 0.595 for loss assessment and 0.370 for emergency response. The score of fuzzy comprehensive evaluation of the index system of infectious disease prevention and control ability in colleges and universities was 79.148, suggesting a high level. Conclusion The established evaluation index system of infectious disease prevention and control ability in colleges and universities is scientific and reasonable, which is conducive to provide tool reference for the evaluation of infectious disease prevention and control ability in colleges and universities.

BACKGROUND: Pre-transplant exposure to immune checkpoint inhibitors (ICIs) significantly increases the risk of allograft rejection after liver transplantation (LT); however, whether ICI-related rejection leads to increased graft loss remains controversial. Therefore, this study aimed to investigate the association between ICI-related allograft rejection and perioperative graft loss. METHODS: This was a retrospective analysis of adult liver transplant recipients with early biopsy-proven T-cell-mediated rejection (TCMR) at Liver Transplantation Center of Sun Yat-sen Memorial Hospital from June 2019 to September 2024. The pathological features, clinical characteristics, and perioperative graft survival were analyzed. RESULTS: Twenty-eight patients who underwent early TCMR between June 2019 and September 2024 were included. Based on pre-LT ICI exposure, recipients were categorized into ICI-related TCMR (irTCMR, n = 12) and conventional TCMR (cTCMR, n = 16) groups. Recipients with irTCMR had a higher median Banff rejection activity index (RAI) (6 vs . 5, P = 0.012) and more aggressive tissue damage and inflammation. Recipients with irTCMR showed higher proportion of treatment resistance, achieving a complete resolution rate of only 8/12 compared to 16/16 for cTCMR. Graft loss occurred in 5/12 of irTCMR recipients within 90 days after LT, with no graft loss in cTCMRs recipients. Cox analysis demonstrated that irTCMR with an ICI washout period of <30 days was an independent risk factor for perioperative graft loss (hazard ratio [HR], 6.540; 95% confidence interval [CI], 1.067-40.067, P = 0.042). CONCLUSION IrTCMR is associated with severe pathological features, increased resistance to treatment, and higher graft loss in adult liver transplant recipients.
Humans ; Liver Transplantation/adverse effects* ; Male ; Female ; Middle Aged ; Retrospective Studies ; Graft Rejection/immunology* ; Immune Checkpoint Inhibitors/therapeutic use* ; Adult ; T-Lymphocytes/drug effects* ; Graft Survival/immunology* ; Aged

Emerging evidences have indicated the role of ferroptosis in the progression of metabolic-associated fatty liver disease (MAFLD); thus, inhibiting ferroptosis is a promising strategy for the development of MAFLD therapeutics. Recent studies have demonstrated the antioxidative effect of the gut commensal bacterium Akkermansia muciniphila (A. muc); however, whether it can alleviate ferroptosis remains unclear. The current study indicates A. muc intervention efficiently reversed high-fat high-fructose diet (HFHFD)-induced lipid peroxidation and ferroptosis in the liver. These beneficial effects were mediated by activation of the hepatic AMPK/SIRT1/PGC-1α axis, as evidenced by the finding that AMPK deficiency abrogated the amelioration of lipid peroxidation in vitro and in vivo. Furthermore, the short-chain fatty acids (SCFAs) were enriched upon A. muc treatment, and acetate was identified as a key activator of hepatic AMPK signalling. Mechanistically, microbiota-derived acetate was transported to the liver and metabolized to adenosine monophosphate (AMP), which triggered AMPK activation. Furthermore, a colonization assay in germ-free mice confirmed that A. muc mediated antiferroptotic effects in the absence of other microbes. These data indicated that A. muc exerts antiferroptotic effects against MAFLD, at least partially by producing acetate, which activates the hepatic AMPK/SIRT1/PGC-1α axis to alleviate ferroptosis via the inhibition of polyunsaturated fatty acid (PUFA) synthesis.

Various therapeuti modailities have been engineered for lung cancer treatment, but their clinic application is severely impeded by the poor therapy efficiency and immunosuppressive microenvironment. Herein, we fabricated a library of small molecule redox-labile nanoparticles (NPs) (i.e., diPTX-2C NPs, diPTX-2S NPs, and diPTX-2Se NPs) by the self-assembly of dimer paclitaxel (PTX) prodrug, and then utilized these NPs with the traditional Chinese medicine (TCM) Qi-Yu-San-Long-Fang (Q) for effective chemoimmunotherapy on Lewis lung carcinoma (LLC)-bearing mice models. Under the high concentration of glutathione (GSH) and H₂O₂, diPTX-2Se NPs could specifically release PTX in cancer cells and exert a higher selectivity and toxicity than normal cells. In LLC tumor-bearing mice, oral administration of Q not only effectively downregulated programmed death ligand-1 (PD-L1) expression, but also remodeled the immunosuppressive tumor immune microenvironment via the increase of CD4⁺ T and CD8⁺ T cell proportion and the repolarization of M2 into M1 macrophages in tumor tissues, collectively achieving superior synergistic treatment outcomes in combination with intravenous PTX prodrug NPs. Besides, we found that the combination regimen also demonstrated excellent chemoimmunotherapeutic performances on low-dose small established tumor and high-dose large established tumor models. This study may shed light on the potent utilization of Chinese and Western-integrative strategy for efficient tumor chemoimmunotherapy.

Objective:To investigate the effect of circulating plasma cell(CPC)on the prognosis of multiple myeloma(MM),and to es-tablish and validate a modified CPC-RISS staging system based on CPC and RISS.Methods:A retrospective analysis was performed for the clinical data of 639 treatment-na?ve patients with MM who were treated in Department of Hematology,Xijing Hospital,from January 2006 to June 2023.Peripheral blood smear was used to calculate the percentage of CPC in patients,and the impact of CPC and other related factors on the prognosis of MM patients was analyzed.A CPC-RISS staging system was established based on RISS stage and the percentage of CPC,and the differences in survival and prognosis were analyzed between patients with different stages.Results:Compared with the patients without CPC,detectable CPC was significantly associated with various high-risk factors for MM,and the MM patients with CPC had a lower complete remission rate and shorter overall survival time and progression-free survival time.The modified CPC-RISS staging system was used to classify the patients with MM into four stages,and there were significant differences in median survival time and progression-free survival time between the patients with different stages of MM.Conclusion:The MM pa-tients with the presence of CPC exhibit more aggressive features,worse response to treatment,and a reduction in long-term survival rate.The modified CPC-RISS staging system can effectively predict the prognosis of treatment-na?ve MM patients.

With the increasing maturity of artificial intelligence(AI),the number of clinical cases ap-plying AI in cerebrovascular interventional diagnosis and treatment has been progressively increasing.After reviewing relevant literature published since 2011,this article provides a preliminary summary of AI applica-tions in cerebrovascular interventional diagnosis and treatment to date.Current AI applications in this field predominantly focus on preoperative planning,with some postoperative applications and limited intraoperative applications,while demonstrating diversified algorithmic approaches.For future algorithm development,main-stream scholars predominantly focus on constructing intraoperative automated decision-making models and de-veloping training algorithms for junior interventional physicians.AI undoubtedly holds broad application pros-pects in cerebrovascular interventional procedures.

In the past, aortic dissection, aortic aneurysm, and other aortic diseases, primarily rely on surgical intervention. In recent years, due to breakthroughs in materials science, endovascular therapy has become the first choice for the surgical treatment of most aortic diseases. However, traditional endovascular repair cannot fully meet the clinical needs for certain complex lesions involving the aortic arch and the originations of visceral arteries. The emergence of physician-modified stent technology has brought new hope for the treatment of complex aortic diseases. This article provides a detailed introduction to the concept, development, technical characteristics, and applications of physician-modified stents in the treatment of aortic diseases, analyzing their advantages and limitations. Physician-modified stents serve as a powerful complement to traditional endovascular interventions and commercial branched stents, yet further research and refinement are still required.

Background and aims:Despite growing evidence linking pretransplant exposure to immune checkpoint inhibitors(ICIs)to increased allograft rejection risk after liver transplantation(LT),a lack of comparative studies to definitively establish the correlation between ICI exposure and adverse short-term outcomes after LT exists.This study aimed to analyze the impact of preoperative ICI exposure on short-term post-LT prognosis and allograft rejection risk.Methods:This retrospective cohort study included 121 recipients who underwent LT for hepatocellular carcinoma(HCC)between June 2019 and March 2023.The recipients were categorized into ICI(n=35)and non-ICI(n=86)exposure groups based on pretransplant ICI exposure.Demographics,clinical characteristics,and short-term outcomes were compared between the cohorts.Kaplan-Meier analysis evaluated the impact of ICI exposure on graft survival.Univariate and multivariate logistic regression models assessed the impact of patient characteristics on allograft rejection.Results:Recipients with or without ICI exposure exhibited comparable demographic baseline charac-teristics.The incidences of early allograft dysfunction and biliary and vascular complications were similar between both groups.Post-transplant infection incidence was 37.1％and 20.9％in the ICI and non-ICI groups,respectively(P=0.064).Allograft rejection rates were significantly higher in the ICI group than in the non-ICI group(22.9％vs.5.8％,P=0.015).The ICI group exhibited a higher 90-day post-transplant mortality rate than that of the non-ICI group(14.3％vs.2.3％,P=0.034).Logistic regression analyses demonstrated that allograft rejection independently correlated with 90-day post-transplant mortality,with ICI exposure being an independent risk factor for allograft rejection.In recipients with ICI exposure,a shorter interval between ICIs and LT(washout period)was significantly associated with a higher allograft rejection risk,with the optimal washout period identified as 21 days for predicting 90-day rejection-free survival(P=0.0001).Moreover,in recipients with allograft rejection,the peripheral CD4+/CD8+T cell ratio was much lower in the ICI group than in the non-ICI group.Conclusions:Pretransplant ICI exposure was an independent risk factor for allograft rejection and was significantly associated with 90-day post-transplant mortality after LT for HCC.A ≤21-day washout period was significantly associated with allograft rejection.Future multicenter studies with larger cohorts and prospective designs are essential to validate these findings,confirm causality,and establish standardized clinical guidelines for ICI use before transplantation.Trail registration:ClinicalTrials.gov NCT05913583.

ObjectiveTo explore the method of optimizing the performance of traditional Chinese medicine （TCM） syndrome diagnostic models using Stacking ensemble learning. MethodsTaking the construction of TCM syndrome diagnostic model for lung cancer as an example， 2598 cases of clinical symptoms and signs from lung cancer patients in 9 hospitals were used as independent variables （i.e.， feature variables）， TCM syndrome information as dependent variables， and the clinical data were divided into training set and testing set in 8：2 ratio according to random number table method using Python 3.7 software. The stable features of TCM syndrome of lung cancer were screened using chi-square test， Spearman's correlation test， and Least Absolute Shrinkage and Selection Operator （LASSO） logistic regression analysis； nine machine learning algorithms are trained， including support vector machines （SVMs）， k-nearest neighbors （KNN） algorithm， Random Forest （RF）， Extremely Randomized Trees， Extreme Gradient Boosting （XGBoost）， Lightweight Gradient Boosting （LightGBM）， Adaptive Boosting （AdaBoost）， Gradient Boosting （GB） and the multi-layer perceptron （MLP）， to obtain 9 basic models. Four models with better performance were screened out from the above basic models and fused to form a fusion model by using the Stacking ensemble learning， and the fusion model was trained twice by the above nine machine learning algorithms and evaluated by accuracy rate， micro-average ROC curves， area under the curve （AUC）， and confusion matrix metrics， to screen the optimal diagnostic model. ResultsAfter data processing， 79 stable features and 13 TCM syndromes were obtained. In the basic model training， the comprehensive performance of RF， ExtraTrees， MLP and SVM basic models were better， so the predicted distributions of the syndromes of these four models were used as the secondary training data， and nine fusion models were obtained based on the Stacking ensemble learning （SVM， KNN， RF， ExtraTree， XGBoost， LightGBM， GB， AdaBoost， MLP）. Among them， the XGBoost fusion model performed the best， with an accuracy of 0.850 and 0.838 in the training set and test set， respectively， an overfitting difference of 0.012， and an area under the micro-average ROC curve of 0.996. All fusion models showed an improvement in accuracy and area under the micro-average ROC curve compared with the base model in the test set. ConclusionTaking the TCM syndrome information of lung cancer as an example， the XGBoost fusion model has significant advantages in improving the diagnostic performance of TCM syndrome information of lung cancer through Stacking ensemble learning. It can be seen that the advantages of Stacking ensemble learning to integrate multiple models and effectively improve the diagnostic efficiency of TCM diagnostic models， which provided a methodological reference for similar studies.

Objective To systematically analyze the effect of virtual reality(VR)and augmented reality(AR)on cognition,emotion and adaptive behavior in children and adolescents with autism spectrum disorder(ASD). Methods The randomized controlled trials(RCT)about the effect of VR or AR interventions on cognition,emotion and adaptive behavior in children and adolescents with ASD were retrieved from databases of PubMed,Cochrane Li-brary,Embase,Web of Science,CNKI,VIP and Wanfang data from January,2008 to August,2024,and a system-atic review was conducted. Results Nine RCT from China,Italy,Spain,Iran and the United Kingdom,involving a total of 384 participants,were in-cluded,published between 2016 and 2024.Participants aged two to 15 years.The approaches included VR im-mersive headsets,Xbox 360 Kinect game consoles,CAVE-style immersive virtual environments and AR;cov-ered daily life scenario experiences,cognitive and social scenario training,and emotion recognition tasks.Out-come measures involved cognitive function,emotional function,attention,memory,basic interpersonal communi-cation and adaptive behavior.Interventions typically lasted 15 to 60 minutes a time,one to three times a week,for three to 24 weeks.VR-based training improved overall cognition,emotion recognition,emotional control and expression,basic interpersonal communication and adaptive behavior;while AR-based training improved execu-tive function,spatial orientation,task focus and memory. Conclusion VR and AR are effective on overall cognitive function,emotional function,attention,memory,basic inter-personal communication and adaptive behavior in ASD children and adolescents.