Kinase-Based Screening of Marine Natural Extracts Leads to the Identification of a Cytotoxic High Molecular Weight Metabolite from the Mediterranean Sponge Crambe tailliezi.

Regulated cell death (RCD) results from the activation of one or more signal transduction modules both in physiological or pathological conditions. It is now established that RCD is involved in numerous human diseases, including cancer. As regulated cell death processes can be modulated by pharmacological tools, the research reported here aims to characterize new marine compounds acting as RCD modulators. Protein kinases (PKs) are key signaling actors in various RCDs notably through the control of either mitosis (e.g., the PKs Aurora A and B) or necroptosis (e.g., RIPK1 and RIPK3). From the primary screening of 27 various extracts of marine organisms collected in the Mediterranean Sea, an extract and subsequently a purified high molecular weight compound dubbed P3, were isolated from the marine sponge Crambe tailliezi and characterized as a selective inhibitor of PKs Aurora A and B. Furthermore, P3 was shown to induce apoptosis and to decrease proliferation and mitotic index of human osteosarcoma U-2 OS cells.

In vitro inhibition of 5-α reductase and in vivo suppression of benign prostatic hyperplasia by Physalis angulata ethyl acetate extract.

The inhibitory effect against 5-α reductase of the ethyl acetate (EA) extract from Physalis angulata was evaluated in vitro using mouse prostate homogenates, and the suppression of benign prostatic hyperplasia (BPH) was assessed in a mouse model of testosterone-induced BPH. The EA extract exhibited a potentially inhibitory effect on 5-α reductase with an IC50 of 197 µg/ml. In BPH mice, the EA extract at a dose of 12 mg/kg was comparable to finasteride 5 mg/kg in suppressing BPH in terms of reducing absolute enlarged prostate weight (p < 0.05 vs. BPH group) and mitigating the hypertrophy of glandular elements and prostate connective tissue. Identification of chemical ingredients in the EA extract by UPLC-QTOF-MS revealed 37 substances belonging chiefly to flavonoids and physalins. Further quantification of the EA extract by HPLC-PDA methods revealed that chlorogenic acid, and rutin were the main components. Molecular docking studies of chlorogenic acid and rutin on 5-α reductase showed their high affinity to the enzyme with binding energies of -9.3 and - 9.2 kcal/mol, respectively compared with finasteride (- 10.3 kcal/mol). Additionally, chlorogenic acid inhibited 5-α reductase with an IC50 of 12.07 µM while rutin did not. The presence of chlorogenic acid in the EA extract may explain the inhibitory effects of the EA extract on 5-α reductase, and thus the suppression of BPH.

Predicting Antibiotic Resistance in ICUs Patients by Applying Machine Learning in Vietnam.

Introduction: Artificial Intelligence (AI) and machine learning (ML) are used extensively in HICs to detect and control antibiotic resistance (AMR) in laboratories and clinical institutions. ML is designed to predict outcome variables using an algorithm to enable "machines" to learn the "rules" from the data. ML is increasingly being applied in intensive care units to identify AMR and to assist empiric antibiotic therapy. This study aimed to evaluate the performance of ML models for predicting AMR bacteria and resistance to antibiotics in two Vietnamese hospitals. Patients and Methods: A cross-sectional study combined with retrospective was conducted from 1st January 2020 to 30th June 2022. Five models were developed to predict antibiotic resistance of bacterial infections of ICU patients. Two datasets were prepared to predict AMR bacteria and antibiotics with ML models. The performance of the prediction models was evaluated by various indicators (sensitivity, specificity, precision, accuracy, F1-score, PRC, AuROC, and NormMCC) to determine the optimal time point for data selection. Python version 3.8 was used for statistical analyses. Results: The accuracy, F1-score, AuROC, and normMMC of LightGBM, XGBoost, and Random Forest models were higher than those of other models in both datasets. In both datasets 1 and 2, accuracy, F1-score, AuROC and normMCC of the XGBoost model were the highest among five models (from 0.890 to 1.000). Only Random Forest models had specificity scores higher than 0.850. High scores of sensitivity, accuracy, precision, F1-score, and normMCC indicated that the models were making accurate predictions for datasets 1 and 2. Conclusion: XGBoost, LightGBM, and Random Forest were the best-performed machine learning models to predict antibiotic resistance of bacterial infections of ICUs patients using the patients' EMRs.

Relationships of BRAF V600E Gene Mutation With Some Immunohistochemical Markers and Recurrence Rate in Patients With Thyroid Carcinoma.

Background: The B-type rafkinase (BRAF) V600E gene mutation plays an important role in the pathogenesis, diagnosis, and prognosis of thyroid carcinoma. This study was conducted to investigate the rate of the BRAF V600E mutation, the relationships between the BRAF V600E gene mutation and some immunohistochemical markers, and recurrence rate in patients with differentiated thyroid cancer. Method: The study was conducted by a descriptive and longitudinal follow-up method on 102 thyroid carcinoma patients at 103 Military Hospital, Hanoi, Vietnam. All patients were identified with the BRAF V600E gene mutation by real-time polymerase chain reaction. Results: The rate of BRAF V600E gene mutation in patients with thyroid cancer was 60.8%. Patients with BRAF V600E gene mutation had a significantly higher rate of positive cyclooxygenase 2 (COX-2) and Ki67 markers than those without the mutation (COX-2: odds ratio [OR] = 2.93; 95% confidence interval [CI] = 1.27-6.74, P = .011; Ki67: OR = 3.41; 95% CI = 1.31-8.88, P = .01). A statistically significant relationship was identified between the rate of BRAF V600E mutation and the rate of positive Hector Battifora mesothelial 1 (HBME-1) (B = -1.040; P = .037) and COX-2 (B = -1.123; P = .023) markers. The recurrence rate in patients with BRAF V600E gene mutation was significantly higher than that in those without the mutation (P = .007). The mean of the recurrence time of patients with BRAF V600E mutation was significantly lower than that in those without the mutation (P = .011). Conclusions: A high prevalence of BRAF V600E gene mutation was found in thyroid carcinoma patients. The rates of positive HBME-1, COX-2, and Ki67 markers were significantly correlated to BRAF V600E gene mutation. Patients with BRAF V600E gene mutation showed a significantly higher relapse rate and earlier relapse time than those without the mutation.