The objective of the current study was to evaluate the antiproliferative activity of sclareol against MG63 osteosarcoma cells. A 3‑(4,5‑dimethylthiazol‑2‑yl)‑2,5‑diphenyltetrazolium bromide assay was used to evaluate the cell viability of cells following treatment with sclareol. The extent of cell death induced by sclareol was evaluated using a lactate dehydrogenase (LDH) assay. The effect of sclareol on cell cycle progression and mitochondrial membrane potential (ΛΨm) was evaluated with flow cytometry using the DNA‑binding fluorescent dyes propidium iodide and rhodamine‑123, respectively. Fluorescence microscopy was used to detect the morphological changes in the MG63 osteosarcoma cancer cells and the appearance of apoptotic bodies following sclareol treatment. The results revealed that sclareol induced dose‑ and time‑dependent growth inhibition of MG63 cancer cells with an IC50 value of 65.2 µM following a 12‑h incubation. Furthermore, sclareol induced a significant increase in the release of LDH from MG63 cell cultures, which was much more pronounced at higher doses. Fluorescence microscopy revealed that sclareol induced characteristic morphological features of apoptosis and the appearance of apoptotic bodies. Flow cytometry revealed that sclareol induced G1‑phase cell cycle arrest, which showed significant dose‑dependence. Additionally, sclareol induced a progressive and dose‑dependent reduction in the ΛΨm. In summary, sclareol inhibits the growth of osteosarcoma cancer cells via the induction of apoptosis, which is accompanied by G1‑phase cell cycle arrest and loss of ΛΨm.

Osteosarcoma is composed of tumor osteoblasts and bone-like tissues, with malignant tumors originating from osteogenesis organization. Osteosarcoma is a primary malignant bone tumor. Invasion and metastasis of osteosarcoma affect the prognosis of patients. However, effective therapeutic treatments remain to be identified. The aim of the present study was to investigate the possible inhibitory and apoptotic effects of ginkgetin in osteosarcoma cells. 3.3-(4,5-Dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) and lactate dehydrogenase (LDH) assays were used to determine the effect ginkgetin exerted on the growth of osteosarcoma cells. Flow cytometry was used to determine cell apoptosis. STAT3 protein expression and activation of caspase-3/9 were measured using western blot analysis and the MTT and LDH assays, respectively. The results showed that ginkgetin inhibited cell growth and induced cell cytotoxicity in osteosarcoma cells in a dose-dependent manner. Treatment with ginkgetin significantly activated the apoptosis of osteosarcoma cells in a concentration-dependent manner. The anticancer activity of ginkgetin significantly inhibited STAT3 and promoted caspase-3/9 activation in osteosarcoma cells. The findings demonstrated that ginkgetin exerts growth inhibitory and apoptotic effects on osteosarcoma cells through the inhibition of STAT3 and activation of caspase-3/9.

Backgrounds The in-hospital mortality in lung cancer patients admitted to intensive care unit (ICU) is extremely high. This study intended to adopt machine learning algorithm models to predict in-hospital mortality of critically ill lung cancer for providing relative information in clinical decision-making. Methods Data were extracted from the Medical Information Mart for Intensive Care-IV (MIMIC-IV) for a training cohort and data extracted from the Medical Information Mart for eICU Collaborative Research Database (eICU-CRD) database for a validation cohort. Logistic regression, random forest, decision tree, light gradient boosting machine (LightGBM), eXtreme gradient boosting (XGBoost), and an ensemble (random forest+LightGBM+XGBoost) model were used for prediction of in-hospital mortality and important feature extraction. The AUC (area under receiver operating curve), accuracy, F1 score and recall were used to evaluate the predictive performance of each model. Shapley Additive exPlanations (SHAP) values were calculated to evaluate feature importance of each feature. Results Overall, there were 653 (24.8%) in-hospital mortality in the training cohort, and 523 (21.7%) in-hospital mortality in the validation cohort. Among the six machine learning models, the ensemble model achieved the best performance. The top 5 most influential features were the sequential organ failure assessment (SOFA) score, albumin, the oxford acute severity of illness score (OASIS) score, anion gap and bilirubin in random forest and XGBoost model. The SHAP summary plot was used to illustrate the positive or negative effects of the top 15 features attributed to the XGBoost model. Conclusion The ensemble model performed best and might be applied to forecast in-hospital mortality of critically ill lung cancer patients, and the SOFA score was the most important feature in all models. These results might offer valuable and significant reference for ICU clinicians’ decision-making in advance.

Background We hypothesized that the blood urea nitrogen (BUN) to serum albumin ratio (BAR) could serve as an independent predictor for incident acute kidney injury (AKI) in intensive care unit (ICU) patients with rib fracture. Methods Rib fracture patients in ICU were extracted from Medical Information Mart for Intensive Care IV (MIMIC-IV v1.0) database. The primary outcome in this study was the incidence of AKI. Univariate and multivariate logistic regression analyses were used to determine the relationship between BAR and AKI and propensity score matching (PSM) and inverse probability of treatment weighting (IPTW) were also applied to assure the robustness of our results. Results The optimal cut-off value for BAR was 5.26 based on receiver operator characteristic curve. Among the 953 patients who diagnosed with rib fracture, 197 high-BAR group (≥5.26) patients and 197 low-BAR group (<5.26) patients who had similar propensity scores were finally included in the matched cohort. High-BAR group patients had a significantly higher incidence of AKI (odds ratio, OR, 3.85, 95% confidence index, 95% CI, 2.58–5.79, P<0.001) in the original cohort, in the matched cohort (OR, 4.47, 95% CI 2.71–7.53, P<0.001), and in the weighted cohort (OR, 4.28, 95% CI 2.80–6.53, P<0.001). Furthermore, BAR was superior to that of acute physiology score III for predicting AKI and could add more net benefit for incident AKI in critical care patients with rib fracture. Conclusion As an easily access and cost-effective parameter, BAR could serve as a good diagnostic predictor for AKI in ICU patients with rib fracture.