Machine learning of cell population data, complete blood count, and differential count parameters for early prediction of bacteremia among adult patients with suspected bacterial infections and blood culture sampling in emergency departments.

BACKGROUND: Bacteremia is a life-threatening complication of infectious diseases. Bacteremia can be predicted using machine learning (ML) models, but these models have not utilized cell population data (CPD). METHODS: The derivation cohort from emergency department (ED) of China Medical University Hospital (CMUH) was used to develop the model and was prospectively validated in the same hospital. External validation was performed using cohorts from ED of Wei-Gong Memorial Hospital (WMH) and Tainan Municipal An-Nan Hospital (ANH). Adult patients who underwent complete blood count (CBC), differential count (DC), and blood culture tests were enrolled in the present study. The ML model was developed using CBC, DC, and CPD to predict bacteremia from positive blood cultures obtained within 4 h before or after the acquisition of CBC/DC blood samples. RESULTS: This study included 20,636 patients from CMUH, 664 from WMH, and 1622 patients from ANH. Another 3143 patients were included in the prospective validation cohort of CMUH. The CatBoost model achieved an area under the receiver operating characteristic curve of 0.844 in the derivation cross-validation, 0.812 in the prospective validation, 0.844 in the WMH external validation, and 0.847 in the ANH external validation. The most valuable predictors of bacteremia in the CatBoost model were the mean conductivity of lymphocytes, nucleated red blood cell count, mean conductivity of monocytes, and neutrophil-to-lymphocyte ratio. CONCLUSIONS: ML model that incorporated CBC, DC, and CPD showed excellent performance in predicting bacteremia among adult patients with suspected bacterial infections and blood culture sampling in emergency departments.

Synthesis and anticancer activity of a novel series of 9-O-substituted berberine derivatives: a lipophilic substitute role.

To alter its hydrophobicity, a series of compounds bearing 9-O-alkyl- or 9-O-terpenyl- substituted berberine were synthesized and evaluated for anticancer activity against human cancer HepG2 and HT29 cell lines. We found that the lipophilic substitute of 9-O-alkyl- and 9-O-terpenyl berberine derivatives plays a role in inhibiting the human cancer cell growth and its activity could be maximized with the optimized substitute type and chain length. Most strikingly, nonetheless, of the six compounds prepared, sample 8, a farnesyl 9-O-substituted berberine, showed either comparable or better cytotoxic activity against human cancer HepG2 cell line than that of berberine. Compound 8 had also shown a 104-fold antiproliferation activity in compare with berberine against human hepatoma HepG2 cell lines after 48 incubation hours. Further, in Hoechst 33258 and annexin V-FITC/PI staining analyses it induced apoptosis in HepG2 cells at lower concentration than that of berberine for 24h. Take all; farnesyl 9-O-substituted berberine could be a potential candidate for new anticancer drug development.