Objective:To investigate the effects of Bcl3 gene knockout on the composition of spleen immune cells and antitumor ability of mice.Methods:Bcl3 gene knockout mice (Bcl3 -/-) were established by CRISPR/Cas9 genome editing technology. Blood routine test and flow cytometry were used to detect the immune cell composition in Bcl3 -/- mice. Lung metastasis models were established by injecting mice with B16F10 melanoma cells. The number of tumor nodules in lung and the survival time of mice were used to assess the antitumor ability of wild-type (WT) and Bcl3 -/- mice. Results:Bcl3 -/- mice were successfully bred to a strain with normal growth rate and normal breeding performance. Furthermore, no embryonic death occurred. Compared with WT mice, Bcl3 -/- mice showed splenomegaly and a significant increase in the number of spleen immune cells ( P<0.05). The counts and percentages of platelets and neutrophils in Bcl3 -/- mice were significantly lower than those in WT mice. The proportion of CD19 + B cells showed no significant change, while the proportions of CD3 + T cells and T cell subsets (CD4 + , CD8 + , Treg) increased significantly ( P<0.05). The proportions of NK cells (NK1.1 + ) and neutrophils (Gr1 + ) decreased ( P<0.05), while no significant change in the proportion of DC (CD11b + ) was observed. There were a large number of tumor nodules formed by melanoma cells in the lung of Bcl3 -/- tumor bearing mice, and their survival time was shortened dramatically. Conclusions:Knockout of Bcl3 gene affected the development, differentiation and function of immune cells, thereby reducing the antitumor ability of mice.

BACKGROUND:Sepsis is one of the main causes of mortality in intensive care units(ICUs).Early prediction is critical for reducing injury.As approximately 36%of sepsis occur within 24 h after emergency department(ED)admission in Medical Information Mart for Intensive Care(MIMIC-IV),a prediction system for the ED triage stage would be helpful.Previous methods such as the quick Sequential Organ Failure Assessment(qSOFA)are more suitable for screening than for prediction in the ED,and we aimed to find a light-weight,convenient prediction method through machine learning. METHODS:We accessed the MIMIC-IV for sepsis patient data in the EDs.Our dataset comprised demographic information,vital signs,and synthetic features.Extreme Gradient Boosting(XGBoost)was used to predict the risk of developing sepsis within 24 h after ED admission.Additionally,SHapley Additive exPlanations(SHAP)was employed to provide a comprehensive interpretation of the model's results.Ten percent of the patients were randomly selected as the testing set,while the remaining patients were used for training with 10-fold cross-validation. RESULTS:For 10-fold cross-validation on 14,957 samples,we reached an accuracy of 84.1%±0.3%and an area under the receiver operating characteristic(ROC)curve of 0.92±0.02.The model achieved similar performance on the testing set of 1,662 patients.SHAP values showed that the five most important features were acuity,arrival transportation,age,shock index,and respiratory rate. CONCLUSION:Machine learning models such as XGBoost may be used for sepsis prediction using only a small amount of data conveniently collected in the ED triage stage.This may help reduce workload in the ED and warn medical workers against the risk of sepsis in advance.

Objective: To investigate the effects of human adult T lymphoblastic leukemia virus typeⅠ (HTLV-1) infection on the production of reactive oxygen species (ROS) and mitochondrial damage in host cells. Methods: A cell model of HTLV-1 infection was established by co-culturing HTLV-1-positive cell line MT2 with HeLa cells. ROS, mitochondrial membrane potential (MMP) and total mitochondria were detected using specific fluorescence probe labeling method. Cell apoptosis was detected by Annexin V-FITC/PI method. Western blot was performed to detect viral proteins Tax and p19, as well as mitochondrial proteins TIM23 and TOM20. After the treatment of MT2 cells with different concentrations of reverse transcription inhibitors (ZDV), relative viral loads were detected by quantitative real-time PCR and Western blot, and the mass of mitochondria was analyzed by flow cytometry. Results: After co-culturing HeLa cells with MT2 cells for 24 h, the ROS level in host cells increased without obvious cell apoptosis, while the mitochondrial membrane potential, mitochondrial protein expression and total mitochondria decreased significantly. When the replication of HTLV-1 in MT2 cells was inhibited by ZDV, the ROS level and total mitochondria increased. Conclusions HTLV-1 infection can cause oxidative stress in host cells, resulting in mitochondrial damage. Autophagy might be activated to degrade mitochondrial damage and maintain cell homeostasis during the infection.