Automated Microfluidic Nucleic Acid Detection Platform-Integrated RPA-T7-Cas13a for Pathogen Diagnosis.

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-labor-intensive 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.

Improved Adverse Drug Event Prediction Through Information Component Guided Pharmacological Network Model (IC-PNM).

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 [Formula: see text]. On the other hand, the IC-PNM prediction performance improved to [Formula: see text] if we removed the small sample size drug-ADE pairs from the prediction model during validation.

The Cancer Drug Fraction of Metabolism Database.

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.