Effect of combination of all-trans retinoic acid and arsenic trioxide on apoptosis of acute promyelocytic leukemia cells.

To study the effect of all-trans retinoic acid (ATRA) and arsenic trioxide (ATO) combination treatment on apoptosis of acute promyelocytic leukemia cells (NB4), inflammation and prognosis. The effect of ATRA - ATO combination on the proliferation of NB4 was determined using MTT assay. Apoptosis of NB4 cells was assessed with TUNEL assay. The effect of ATRA-As2O3 combination on the expressions of IL-6 and TNF-α in NB4 cells was determined using ELISA kits, while its effect on the quality of life of 25 acute promyelocytic leukemia patients admitted to our hospital was scored, as an index of prognosis. The combination treatment with ATRA and ATO significantly inhibited the proliferation of NB4 cells and promoted their apoptosis, relative to the model group. In addition, the combination treatment reduced serum IL-6 and TNF-α levels in patients with acute promyelocytic leukemia, and improve their quality of life and survival. Combination treatment with ATRA and ATO significantly inhibits the proliferation of NB4 cells and promotes their apoptosis, and reduces inflammatory responses in patients with acute promyelocytic leukemia, while improving their quality of life and prognosis.

Development and validation of machine learning-based prediction model for severe pneumonia: A multicenter cohort study.

Severe pneumonia (SP) is a prevalent respiratory ailment characterized by high mortality and poor prognosis. Current scoring systems for pneumonia are not only time-consuming but also exhibit limitations in early SP prediction. To address this gap, this study aimed to develop a machine-learning model using inflammatory markers from peripheral blood for early prediction of SP. A total of 204 pneumonia patients from seven medical centers were studied, with 143 (68 SP cases) in the training cohort and 61 (32 SP cases) in the test cohort. Clinical characteristics and laboratory test results were collected at diagnosis. Various models including Logistic Regression, Random Forest, Naïve Bayes, XGBoost, Support Vector Machine, and Decision Tree were built and evaluated. Seven predictors-age, sex, WBC count, T-lymphocyte count, NLR, CRP, TNF-α, IL-4/IFN-γ ratio, IL-6/IL-10 ratio-were selected through LASSO regression and clinical insight. The XGBoost model, exhibiting best performance, achieved an AUC of 0.901 (95 % CI: 0.827 to 0.985) in the test cohort, with an accuracy of 0.803, sensitivity of 0.844, specificity of 0.759, and F1_score of 0.818. Indeed, SHAP analysis emphasized the significance of elevated WBC counts, older age, and elevated CRP as the top predictors. The use of inflammatory biomarkers in this concise predictive model shows significant potential for the rapid assessment of SP risk, thereby facilitating timely preventive interventions.