# Automatic and accurate identification of aerobic bacteria, anaerobic bacteria, yeasts, and fungi in clinical samples derived from animals and from feed for pets

> **NIH FDA U18** · UNIVERSITY OF CONNECTICUT STORRS · 2021 · $118,940

## Abstract

Project Summary
This project aims to build at the Connecticut Veterinary Medical Diagnostic Laboratory
(CVMDL) the capacity necessary to automatically, rapidly, and accurately identify
aerobic bacteria, anaerobic bacteria, yeasts, and fungi in clinical samples derived from
animals and in commercial feed for pets. Identification is a step needed before
assessing the antimicrobial resistance of bacterial and fungal isolates. The laboratory
needs to replace a now considered obsolete automatic identification system used for ID
aerobic bacteria (Ommnilog system) with a new system that provides broader
capabilities and improved accuracy.
The project's goal aligns with the US FDA Veterinary Laboratory Investigation and
Response Network (Vet-LIRN) efforts towards tracking antimicrobial resistance in
bacteria and fungi isolated from sick animals and feed.
To meet the goal of this project, we are requesting funds to acquire an automated
system like GEN III OmniLog ID Plus System (Biolog Inc., Hayward, CA, USA) and
companion databases including YT (yeast), FF (fungi), AN (anaerobic bacteria) and
GEN III for Gram-negative and positive bacteria. This Biolog advanced phenotypic
technology provides valuable information on the properties of strains, in addition to a
species-level identification. In the system, a proprietary carbon source utilization
technology identifies environmental and pathogenic microorganisms by producing a
characteristic pattern or "metabolic fingerprint" from discrete test reactions performed
within a 96 well microplate.
The purchase of a GEN III OmniLog ID Plus System system will enhance the capacity
at the CVMDL Microbiology Laboratory to be fully engaged with Vet-LIRN goals. The
laboratory recently acquired an advanced automated system to identify bacteria and for
reading antibiotic susceptibility plates (BIOMIC V3, Giles Scientific). The use of both
systems will potentiate CVMDL’s capabilities.

## Key facts

- **NIH application ID:** 10440741
- **Project number:** 1U18FD007497-01
- **Recipient organization:** UNIVERSITY OF CONNECTICUT STORRS
- **Principal Investigator:** Guillermo Roberto Risatti
- **Activity code:** U18 (R01, R21, SBIR, etc.)
- **Funding institute:** FDA
- **Fiscal year:** 2021
- **Award amount:** $118,940
- **Award type:** 1
- **Project period:** 2021-09-06 → 2022-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10440741

## Citation

> US National Institutes of Health, RePORTER application 10440741, Automatic and accurate identification of aerobic bacteria, anaerobic bacteria, yeasts, and fungi in clinical samples derived from animals and from feed for pets (1U18FD007497-01). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10440741. Licensed CC0.

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