Development and Validation of a Deep Learning Model for Predicting Treatment Response in Patients With Newly Diagnosed Epilepsy.

Importance: Selection of antiseizure medications (ASMs) for epilepsy remains largely a trial-and-error approach. Under this approach, many patients have to endure sequential trials of ineffective treatments until the "right drugs" are prescribed.

Objective: To develop and validate a deep learning model using readily available clinical information to predict treatment success with the first ASM for individual patients.

Molecular identification of cultivable bacteria in the gut of adult Bactrocera tau (Walker) and their trapping effect.

Background: It has been shown that some bacteria can attract their hosts. Our research aimed to identify cultivable bacterial isolates in the guts of sexually mature adult female and male Bactrocera tau and to evaluate their utility in trapping of B. tau.