OBJECTIVE To systematically evaluate the influential factors for all-cause mortality in patients with carbapenem-resistant Gram-negative bacteria （CR-GNB） treated with polymyxin B. METHODS PubMed， Embase， the Cochrane Library， Web of Science， Wanfang Data， VIP， CBM and CNKI were searched to collect clinical studies on all-cause death within 30 days or 28 days after treatment with polymyxin B in patients with CR-GNB infection from database establishment to July 2025. After literature screening， data extraction and evaluation of literature quality， meta-analysis was performed using RevMan 5.4 software. RESULTS A total of 12 studies were included， involving 1 326 patients， among whom 529 patients died， with a mortality rate of 39.89%. Meta-analysis results showed that combined with cardiovascular disease [OR＝2.06， 95%CI （1.37， 3.09）， P ＝0.005 ] ， increased Sequential Organ Failure Assessment （SOFA） score [OR＝1.20， 95%CI （1.07， 1.35）， P ＝0.003 ] ， mechanical ventilation [OR＝2.35， 95%CI （1.65， 3.34）， P ＜0.001 ] ， continuous renal replacement therapy （CRRT） [OR＝2.58， 95%CI （1.67， 3.97）， P ＜0.001 ] ， bloodstream infection [OR＝3.24， 95%CI （2.19， 4.78）， P ＜0.001 ] ， multiple-site infection [OR＝1.51， 95%CI （1.03， 2.20）， P ＝0.03 ] ， septic shock [OR＝3.19， 95%CI （1.94， 5.24）， P ＜0.001 ] ， use of vasoactive drugs [OR＝2.90， 95%CI （1.97， 4.27）， P ＜0.001 ] ， and the occurrence of acute kidney injury （AKI） [OR＝2.17， 95%CI （1.41， 3.36）， P ＜0.001 ] were risk factors for all-cause mortality in patients with CR-GNB infection treated with polymyxin B. Conversely， an extended duration of polymy xin B treatment [OR＝0.92， 95%CI （0.86， 0.99）， P ＝0.03 ] and early administration after CR-GNB infection [OR＝0.47， 95%CI （0.25， 0.85）， P ＝0.01 ] were protective factors. CONCLUSIONS Patients with cardiovascular disease， receiving mechanical ventilation or CRRT， having bloodstream infection， multiple-site infection or septic shock， combining with vasoactive drugs， with AKI and increased SOFA scores have a higher risk of all-cause mortality. Conversely， extending the duration of polymyxin B treatment （beyond 7 days） and early administration within 48 hours after confirmed CR-GNB infection can significantly reduce the risk of all-cause mortality.

BACKGROUND: Regulatory dendritic cell (DCreg) subset exhibits a unique capacity for inducing immune tolerance among the variety subsets of dendritic cells (DCs) within gut-associated lymphoid tissues (GALTs). Fms-like tyrosine kinase 3 ligand (FLT3L) is involved in the differentiation of DCregs and the subsequent expansion of regulatory T-cells (Tregs) mediated by DCregs, though the precise mechanism remains poorly understood. This study aimed to explore the expansion mechanism of Treg induced by DCreg and the role of FLT3L in this process. METHODS: DCregs were distinguished from other DC subsets isolated from GALTs of BALB/c mice through a mixed lymphocyte reaction assay. The functions and mechanisms by which FLT3L promoted Treg expansion via DCregs were investigated in vitro through co-culture experiments involving DCregs and either CD4 + CD25 - T-cells or CD4 + CD25 + T-cells. Additionally, an in vivo experiment was conducted using a dextran sulfate sodium (DSS)-induced colitis model in mice. RESULTS: CD103 + CD11b + DC exhibited DCreg-like functionality and was identified as DCreg for subsequent investigation. Analysis of Foxp3 + Treg percentages within a co-culture system of CD4 + CD25 - T-cells and DCregs, with or without FLT3L, demonstrated the involvement of the FLT3/FLT3L axis in driving the differentiation of precursor T-cells into Foxp3 + Tregs induced by DCregs. Cell migration and co-culture assays revealed that the FLT3/FLT3L axis enhanced DCreg migration toward Tregs via the Rho pathway. Additionally, it was observed that DCregs could promote Treg proliferation through the Notch pathway, as inhibition of Notch signaling by DAPT (N-[N-(3,5-difluorophenacetyl)-l-alanyl]-S-phenylglycine t-butyl ester) suppressed Treg expansion within the co-culture system of DCregs and CD4 + T-cells or CD4 + CD25 + T-cells. Furthermore, the FLT3/FLT3L axis influenced JAG1 expression in DCregs, indirectly modulating Treg expansion. In vivo experiments further established that FLT3L promoted DCreg expansion and restored Treg balance in DSS-induced colitis models, thereby ameliorating colitis symptoms in mice. CONCLUSION The FLT3/FLT3L axis is integral to the maintenance of DCreg function in Treg expansion.
Animals ; T-Lymphocytes, Regulatory/immunology* ; Dendritic Cells/immunology* ; Mice ; Mice, Inbred BALB C ; Membrane Proteins/metabolism* ; Receptors, Notch/metabolism* ; Lymphoid Tissue/metabolism* ; Signal Transduction/physiology* ; Coculture Techniques ; Flow Cytometry

Immune checkpoint inhibitors (ICIs) have been approved for the treatment of a variety of solid tumors and hematological malignancies. Adverse reactions caused by ICIs have been gradually focused on. Cytomegalovirus (CMV) gastritis after ICIs treatment is relatively rare. Here we reported a case of advanced lung adenocarcinoma who experienced recurrent upper abdominal pain and vomiting after Pembrolizumab treatment. CMV gastritis was diagnosed through gastroscopy. The patient's symptoms improved after antiviral treatment. During the treatment of ICIs, attention should be paid to the differential diagnosis of upper abdominal pain symptoms, and vigilance should be maintained against CMV gastritis. It is difficult to differentiate CMV gastritis and immune-related gastritis judging from symptoms, and gastroscopy is important for differential diagnosis.
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Humans ; Adenocarcinoma of Lung/drug therapy* ; Cytomegalovirus/physiology* ; Cytomegalovirus Infections ; Gastritis/diagnosis* ; Immune Checkpoint Inhibitors/therapeutic use* ; Lung Neoplasms/drug therapy*

OBJECTIVE To systematically evaluate the influential factors for the clinical efficacy of tigecycline in the treatment of severe infections caused by multidrug-resistant Gram-negative bacteria （MDR-GNB）. METHODS Retrieved from PubMed， Embase， The Cochrane Library， CNKI database， Wanfang database and VIP database， the studies about the influential factors for the clinical efficacy of tigecycline in the treatment of adult patients with severe infections caused by MDR-GNB from the database construction to April 30， 2025. After screening literature， extracting data and evaluating the quality of literature， systematic review was performed by using RevMan 5.3 software. RESULTS A total of 14 studies involving 2 033 patients were included， of which 1 355 patients showed effective treatment outcomes. The meta-analysis results showed that Acute Physiology and Chronic Health Evaluation （APACHE）-Ⅱ ＞ 20 [OR＝4.50， 95%CI （2.28， 8.85）， P＜0.001]， malignant tumor [OR＝1.96， 95%CI （1.41， 2.72）， P＜0.001]， hemodialysis [OR＝2.09， 95%CI （1.40， 3.12）， P＜0.001]， septic shock [OR＝3.07， 95%CI （2.00， 4.72）， P＜0.001]， mechanical ventilation [OR＝2.31， 95%CI （1.57， 3.39）， P＜0.001]， coagulation dysfunction [OR＝ 3.03， 95%CI （2.09， 4.37）， P＜0.001]， glucocorticoids use＞3 days [OR＝2.26， 95%CI （1.14， 4.45）， P＝0.020]， and longer hospitalization time before using tigecycline [OR＝3.33， 95%CI （1.34， 8.30）， P＝0.010] were risk factors for the clinical efficacy of tigecycline in treatment of severe infections caused by MDR-GNB. Tigecycline initial double-dose regimen [OR＝0.23， 95%CI （0.13， 0.42）， P＜0.001]， combination therapy [OR＝0.15， 95%CI （0.05， 0.48）， P＝0.001]， and prolonged treatment course [OR＝0.91， 95%CI （0.88， 0.95）， P＜0.001] were protective factors. CONCLUSIONS There are many influential factors for the clinical efficacy of tigecycline in the treatment of severe infections caused by MDR-GNB， among which APACHE-Ⅱ score＞20， malignant tumor， hemodialysis， septic shock， mechanical ventilation， coagulation dysfunction， glucocorticoids＞3 d and longer hospitalization time before using tigecycline before tigecycline （No.20251606） use are risk factors； tigecycline double dose， combined medication and longer treatment course are protective factors.

Objective: To understand the changing trends and related factors of knowledge, attitude and practice (KAP) regarding the three major infectious diseases (acquired immunodeficiency syndrome, tuberculosis, hepatitis B) among freshmen in Jiangsu from 2019 to 2022, so as to provide a reference basis for the health education of infectious diseases in schools. Methods: From 2019 to 2022, a total of 33 944 freshmen from 20 universities in Jiangsu Province were randomly selected for four consecutive years to investigate their KAP levels online through self designed questionnaires on three major infectious diseases. The multiple linear regression model was used to analyze the changing trends of students KAP levels of the three major infectious diseases, and to explore the influencing factors of KAP. Results: From 2019 to 2022, the knowledge scores(18.0±3.1,18.4±3.2,18.7±3.2,18.8±3.2), related to the three major infectious diseases showed an upward trend ( F=436.50, P <0.01), and the positive attitude reporting rates were 81.77%, 81.46%, 82.68% and 81.74%, respectively. The reporting rates of positive practice were 80.11%, 79.25%, 79.08 % and 79.04%, respectively. Multiple linear regression showed that school type, parental education level, mother s occupation, average income per person in family and living arrangements during high school all had an impact on the knowledge ( β = -1.510 -0.559), attitudes ( β =-0.043-0.065) and practice ( β =-0.028-0.027) of the three major infectious diseases ( P < 0.05 ). The family residence areas only affected the reporting rate of positive attitude scores ( β =0.002-0.065), and whether only children or not affected the reporting rate of positive practice scores ( β =0.009)( P <0.05). The knowledge score showed an upward trend ( β= 0.297, P <0.01), the positive attitude reporting rate showed no statistically significant change ( β=0.001, P =0.22), and the positive practice reporting rate showed a downward trend ( β=-0.005, P <0.01). Conclusions Freshman in Jiangsu Province from 2019 to 2022 have shown a separation in KAP scores regarding the three major infectious diseases. Targeted measures should be taken to improve their health practice level.

Objective:To analyze the epidemiological and clinical features of infectious diseases of the central nervous system (CNS).Methods:A cross-sectional study and analysis were conducted to summarize the epidemiological and clinical characteristics of 9 918 patients with CNS infectious diseases, who were diagnosed and treated at 29 hospitals across China from January 1, 2001 to December 31, 2020. Data collected included demographic data, clinical manifestations, health economic indicators, and prognostic outcomes.Results:Among the 9 918 collected cases of CNS infectious diseases, 5 559 were male (56.0%) and 4 359 were female (44.0%), with an onset age of 38 (25, 53) years. Education level: slightly more junior high school education (2 651 cases, 26.7%), and less elementary school education and below (2 181 cases, 22.0%) were found. Occupational distribution: farmers were found predominant (3 215 cases, 32.4%), followed by workers (1 826 cases, 18.4%) and students (1 633 cases, 16.5%). Clinical manifestations: headache (6 074 cases, 61.2%), fever (5 869 cases, 59.2%) and positive meningeal irritation signs (2 273 cases, 22.9%) were the 3 most common clinical manifestations, followed by nausea and (or) vomiting (2 095 cases, 21.1%), impaired consciousness (2 077 cases, 20.9%), psychiatric symptom (1 866 cases, 18.8%) and epilepsy (1 627 cases, 16.4%), etc., and cranial nerve involvement was found in 669 cases (6.7%). Major pathogens included viruses in 6 814 cases (68.7%), Mycobacterium tuberculosis in 1 677 cases (16.9%), common bacteria in 864 cases (8.7%), fungi in 254 cases (2.6%), spirochetes of syphilis in 183 cases (1.8%), parasites in 121 cases (1.2%), and rickettsiae in 5 cases (0.1%). Urban-rural distribution: slightly more cases were found in the countryside (5 418 cases, 54.6%) than in the towns (4 500 cases, 45.4%). Distribution of onset by season: 2 412 cases (24.3%) fell ill in spring, 2 835 cases (28.6%) in summer, 2 187 cases (22.1%) in fall, and 2 484 cases (25.0%) in winter. Health economics: the duration of hospitalization was 15 (8, 27) days, and the cost of hospitalization was 1.53 (0.91, 3.02)×10 000 yuan. Prognosis: 9 531 cases (96.1%) were cured or improved, and 92 cases (0.9%) died. Conclusions:The pathogens responsible for CNS infectious diseases are predominantly viruses. Although the incidence is slightly higher during the summer months, the overall seasonal pattern is not particularly pronounced. These infections are more commonly observed in young and middle-aged males and present with a diverse range of clinical manifestations, contributing to a significant disease burden.

Objective: To establish a machine learning-based precision blood donor recruitment model at the county level and assess its generalizability and applicability. Methods: A retrospective study was conducted using blood donation and SMS recruitment data from the Taicang Branch of the Suzhou Blood Center between 2019 and 2024. Multiple machine learning algorithms were employed, including extreme gradient boosting, support vector machine, k-nearest neighbor, logistic regression, decision tree, random forest, and multilayer perceptron. These were combined with techniques such as synthetic minority oversampling, undersampling, and cost-sensitive learning (using MFE and MSFE loss functions). Model parameters were optimized through grid search to identify the best-performing model. Results: In a prospective comparative study against conventional methods, the machine learning models increased the recruitment success rate among high-willingness donors by an average of 129.15%, and the recruitment efficiency per SMS improved by 125.02% compared with the traditional method. Under full-scale SMS sending, the recruitment rate per SMS increased by 42.61%, and SMS sending efficiency improved by 31.77%, significantly enhancing recruitment performance. Conclusion: This study represents the first application of a machine learning-based precision donor recruitment model at the county-level in China. The precise recruitment framework not only improves recruitment efficiency and reduces recruitment costs but also demonstrates strong scalability and generalizability. It provides a scientific and feasible intelligent pathway to ensure the safety and sustainability of the blood supply.

Objective To construct and validate a prediction model for urosepsis in patients after upper urinary tract stone surgery using various machine learning algorithms.Methods A total of 7 464 upper urinary tract stone patients who underwent surgery at the Sixth Affiliated Hospital of Guangxi Medical University from Jun.2018 to Jun.2023 were enrolled and randomly assigned to training(5 224 cases)or validation sets(2 240 cases)at a ratio of 7∶3.Among them,622(8.33%)cases developed urosepsis postoperatively.Six machine learning algorithms,including extreme gradient boosting(XGBoost),logistic regression,light gradient boosting machine(LightGBM),random forest(RF),adaptive boosting(AdaBoost),and gradient boosting decision tree(GBDT),were used to construct prediction models for postoperative urosepsis.The model's predictive ability and clinical benefits were evaluated using receiver operating characteristic(ROC)curves,Shapley additive explanation(SHAP)analysis,calibration curves,and decision curve analysis(DCA).Results The clinical features included body mass index(BMI),number of surgeries,heart rate,Barthel index,venous thrombo embolism(VTE)risk assessment,gender,American Society of Anesthesiologists(ASA)grade,urinary nitrite,and urinary leukocyte in the models.In the training set,the XGBoost,LightGBM,and RF models performed excellently,with area under curve(AUC)values of ROC curves reaching 1.00.In the validation set,the logistic regression model performed the best,with an AUC value of ROC curve of 0.76,showing good predictive stability and calibration.The AdaBoost and GBDT models followed with AUC values of 0.74 and 0.75,respectively,while the AUC values of the LightGBM,XGBoost,and RF models were 0.71,0.70,and 0.68.In terms of model interpretability,SHAP analysis showed the contribution of variables in a descending order as:heart rate,urinary leukocytes,gender,BMI,Barthel index,VTE risk assessment,urinary nitrite,number of surgeries,and ASA grade.Conclusion A logistic regression model for early risk prediction of postoperative urosepsis in upper urinary tract stone patients has been successfully constructed.This model has good predictive performance and calibration,and can effectively assist clinical diagnosis.

Objective To retrieve,evaluate and summarise the best evidence on screening and management of psychological distress in patients who had ostomy for colorectal cancer.Methods Literatures on psychological distress screening and management in patients undergoing colorectal cancer ostomy were systematically searched across various websites and databases of home and abroad from inception to 20th June,2024 including Cochrane Library,PubMed,Embase,CINAHL,Web of Science,CNKI,Wangfang,VIP,UpToDate,National Guideline Clearing-house,National Comprehensive Cancer Network,the National Institute for Health and Care Excellence,Scottish Intercollegiate Guidelines Network,the International Guideline Collaboration Network,Wound,Ostomy and Continence Nurses Society,World Council of Enterostomal Therapists,Medlive,Chinese Nursing Association.The search aimed to identify evidence on psychological distress screening and management in patients with colorectal cancer and stomas,including guidelines,evidence summaries,expert consensuses,systematic reviews,and Meta-analyses.The search was limited to articles published from the inception of each database to 20th June,2024.The searched literature included guidelines,evidence summary,expert consensus,systematic review,meta-analysis,etc..Two researchers independently evaluated the quality of literature and extracted evidences,as well as checked and integrated the evidences together with a panel of evidence-based research experts.Results A total of 14 articles were included,comprising four guidelines,one expert consensus,two evidence summaries,four meta-analyses and three systematic reviews.The articles encompassed 7 dimensions with a total of 18 pieces of evidence,including one in screening timing,one in screening tools,3 in screening content,3 in drug intervention,3 in self-management,5 in psychological support and 2 in health education.Conclusion The evidence summarised in this study on the screening and management of psychological distress in patients who had ostomy for colorectal cancer is scientifically comprehensive.It provides an evidence-based basis for medical staff in prevention and management of psychological distress in patients.It is necessary to formulate a scientific management plan in clinical practice,according to the specific requirements of departments,the patients and their family members.

Deep learning-based polyp image segmentation is helpful for assessing precancerous lesions. A colorectal polyp image segmentation model (RDG-Net) based on a dual-stage decoder is proposed to addresses the issues of poor segmentation performance due to unclear boundaries in colorectal polyp images and insufficient generalization ability for new samples. The model uses Res2Net-50 as the encoder to enhance the precision of image segmentation. The decoder has two stages:the first stage utilizes a 4-layer multi-scale feature aggregation module to integrate features extracted by the encoder at different stages,while the second stage enhances the image features output by the first stage of the decoder through a 3-layer parallel convolution fusion module and decodes them to a higher resolution as the model's final output. The model is trained using the CVC-ClinicDB and Kvasir-SEG training datasets,and tested using the CVC-ClinicDB and Kvasir-SEG test datasets,as well as the CVC300 and ETIS-LaribPolypDB datasets that are not involved in the training. The test results show that the proposed method has an average accuracy,precision,recall rate,Dice similarity coefficient,intersection over union and F2 score of 98.41％,94.25％,92.62％,93.42％,87.69％and 92.93％on the CVC-ClinicDB and Kvasir-SEG datasets,while 99.05％,87.79％,89.13％,88.39％,79.33％ and 88.82％ on the CVC300 and ETIS-LaribPolypDB datasets,respectively,demonstrating that RDG-Net model performs well in colorectal polyp region segmentation and has a high generalization performance on new datasets.