Comparison of In Vitro Bacterial Susceptibility to Common and Novel Equine Wound Care Dressings.

Antimicrobial resistance is becoming a problem of concern in the veterinary field, necessitating the use of effective topical treatments to aid the healing of wounds. Honey has been used for thousands of years for its medicinal properties, but in recent years medical-grade Manuka honey has been used to treat infected wounds. The goal of this study was to determine the relative susceptibility of four common equine wound pathogens to ten different types of antimicrobial agents based on the minimum inhibitory concentration (MIC) and minimum bactericidal concentration (MBC).

Immunogenicity and Vaccine Shedding After 1 or 2 Doses of rVSVΔG-ZEBOV-GP Ebola Vaccine (ERVEBO®): Results From a Phase 2, Randomized, Placebo-controlled Trial in Children and Adults.

Background: The rVSVΔG-ZEBOV-GP vaccine (ERVEBO®) is a single-dose, live-attenuated, recombinant vesicular stomatitis virus vaccine indicated for the prevention of Ebola virus disease (EVD) caused by Zaire ebolavirus in individuals 12 months of age and older.

Methods: The Partnership for Research on Ebola VACcination (PREVAC) is a multicenter, phase 2, randomized, double-blind, placebo-controlled trial of 3 vaccine strategies in healthy children (ages 1-17) and adults, with projected 5 years of follow-up (NCT02876328). Using validated assays (GP-ELISA and PRNT), we measured antibody responses after 1-dose rVSVΔG-ZEBOV-GP, 2-dose rVSVΔG-ZEBOV-GP (given on Day 0 and Day 56), or placebo.

The intersection of video capsule endoscopy and artificial intelligence: addressing unique challenges using machine learning.

Introduction: Technical burdens and time-intensive review processes limit the practical utility of video capsule endoscopy (VCE). Artificial intelligence (AI) is poised to address these limitations, but the intersection of AI and VCE reveals challenges that must first be overcome. We identified five challenges to address.

Few-Shot Learning Enables Population-Scale Analysis of Leaf Traits in .

Plant phenotyping is typically a time-consuming and expensive endeavor, requiring large groups of researchers to meticulously measure biologically relevant plant traits, and is the main bottleneck in understanding plant adaptation and the genetic architecture underlying complex traits at population scale. In this work, we address these challenges by leveraging few-shot learning with convolutional neural networks to segment the leaf body and visible venation of 2,906 leaf images obtained in the field. In contrast to previous methods, our approach (a) does not require experimental or image preprocessing, (b) uses the raw RGB images at full resolution, and (c) requires very few samples for training (e.