To investigate the relationship between hypertensive left ventricular hypertrophy (LVH) and levels of endothelin (ET) and nitric oxide (NO), and to provide an experimental basis for prevention and treatment of hypertensive LVH. Fifty eight hypertensive patients and 14 healthy controls were studied. All patients were examined by echocardiography. Left ventricular mass (LVM) and left ventricular mass index (LVMI) were calculated using Devereux RB formula. Hypertensive patients were divided into a LVH (+) group (n=21) and a LVH (-) group (n=37), and the levels of endothelin and nitric oxide in the peripheral venous blood were measured. The mean ET level was significantly higher in the LVH (+) group than in LVH (-) group (p < 0.05), but the NO level was significantly lower in the LVH (+) group. The ET/NO ratio was significantly higher in the LVH (+) group than in LVH (-) group (p< 0.01). For the stepwise multiple regression analysis, the LVMI of hypertensive patients served as a dependent variable, and age, sex, BMI, MAP, ET, NO, and ET/NO served as independent variables. Only MAP, ET, and NO were found to have significant correlation to hypertensive LVH. ET had a significant positive correlation, and NO a significant negative relation to LVMI, but ET/NO showed no correlation to hypertensive LVH. ET and NO are involved in hypertensive LVH; the independent action of ET and NO in the pathogenesis of hypertensive LVH may weaken the relation between ET/NO and hypertensive LVH. (Hypertens Res 2000; 23: 377-380)

This study aims to create a database for quantifying the fraction of metabolism of cytochrome P450 isozymes for cancer drugs approved by the US Food and Drug Administration. A reproducible data collection protocol was developed to extract essential information, including both substrate‐depletion and metabolite‐formation data from publicly available in vitro selective cytochrome P450 enzyme inhibition studies. We estimated the fraction of metabolism from the curated data. To demonstrate the utility of this database, we conducted an in vitro drug interaction prediction for the 42 cancer drugs. In the drug–drug interaction prediction, we identified 31 drug pairs with at least one cancer drug in each pair that had predicted area under concentration ratios > 2. We further found clinical drug interaction pieces of evidence in the literature to support 20 of these 31 drug–drug interaction pairs.

Improving adverse drug event (ADE) prediction is highly critical in pharmacovigilance research. We propose a novel information component guided pharmacological network model (IC-PNM) to predict drug-ADE signals. This new method combines the pharmacological network model and information component, a Bayes statistics method. We use 33,947 drug-ADE pairs from the FDA Adverse Event Reporting System (FAERS) 2010 data as the training data, and the new 21,065 drug-ADE pairs from FAERS 2011-2015 as the validations samples. The IC-PNM data analysis suggests that both large and small sample size drug-ADE pairs are needed in training the predictive model for its prediction performance to reach an area under the receiver operating characteristic curve (AUROC) = 0.82. On the other hand, the IC-PNM prediction performance improved to AUROC = 0.91 if we removed the small sample size drug-ADE pairs from the prediction model during validation.

Computational strategies play a vital role in the prediction of adverse drug events (ADEs) owing to their low cost and increased efficiency. In this study, we used the strengths of the Jaccard and Adamic-Adar indices to build feature fusion-based predictive network models (FFPNMs) with three different machine learning (ML) methods respectively to predict drug-ADE associations. Our FFPNM with the logistic regression (LR) model improved to an area under the receiver operating characteristic curve (AUROC) value of 0.849, while the corresponding AUROC values for the pharmacological network model (PNM) and model based on similarity measures were 0.824 and 0.821, respectively. FFPNM with random forest (RF) is the best model among them with an AUROC value of 0.856, and the performance of FFPNM with SVM is close to that of FFPNM with RF and higher than that of FFPNM with LR. In these models, the bipartite network consisted of 152 drugs and 633 ADEs, which were obtained from the FDA Adverse Event Reporting System (FAERS) 2010 dataset. To better evaluate the performance of FFPNMs, we performed model predictions by different network consisting of 1177 drugs and 97 ADEs which were from the data of the first 120 days of FAERS 2004. FFPNM with RF achieved the best predictive result with AUROC value of 0.913. The results show that FFPNMs with ML methods, specially RF, have a superior prediction performance and robustness using only the topology features of the drug-ADE network. From our findings, the optimal, concise, and efficient models as computational methods for drug-ADE association predictions, were revealed. Source codes of this paper are available on https://github.com/Coderljl/FFPNM. INDEX TERMS Adverse drug event, prediction, complex network, machine learning, local-informationbased similarity measure, feature fusion-based predictive network model.

There is a growing urgent need for point-of-care testing (POCT) devices that integrate sample pretreatment and nucleic acid detection in a rapid, economical, and non-laborintensive way. Here, we have developed an automated, portable nucleic acid detection system employing microfluidic chips integrating rotary valve-assisted sample pretreatment and recombinase polymerase amplification (RPA)-T7-Cas13a into one-step nucleic acid detection. The RPA and clustered regularly interspaced short palindromic repeats (CRISPR)/Cas13a were integrated into a single-chamber reaction. As a validation model, we used this method to detect Group B streptococci (GBS) DNA and achieved a detection sensitivity of 8 copies/reaction, which is 6 times more sensitive than gold-standard polymerase chain reactions (PCRs). Dual specific recognition of RPA with CRISPR/Cas13a makes our method ultraspecific, with correct detection of Group B streptococci from 8 kinds of pathogenic bacteria. For the 16 positive and 24 negative clinical GBS samples, our assay achieved 100% accuracy compared to the PCR technique. The whole procedure can be automatically completed within 30 min, providing a more robust, sensitive, and accurate molecular diagnostic tool for POCT.