# Small Animal Visual System module

> **NIH NIH P30** · BAYLOR COLLEGE OF MEDICINE · 2024 · $153,205

## Abstract

ABSTRACT
Small Animal Visual System Analysis Module
Recent advances in genome editing technology are leading to an explosion of small animal
models of human diseases. These animal models, when characterized efficiently and correctly,
will benefit investigations that elucidate disease mechanisms and lead to exciting discoveries of
therapeutic interventions. The Small Animal Visual System Analysis Module (SAVSAM) of the
Baylor College of Medicine (BCM) Vision Core has been expanded to provide cutting edge
instrumentation and expert services that are too big and specialized for individual labs to carry.
The technologies and services covered in SAVSAM include spectral domain optical coherence
tomography (SDOCT), in vivo and ex vivo electroretinography (ERG), equipment that tests
optokinetic reflex (OKR), patch clamp recording, water-maze behavior testing,
biochromatography, quantitative immunoblotting, and mass spectrometry. The SAVSAM
promotes cross-disciplinary collaborations among BCM vision researchers by allowing both in
vivo and non-invasive services to help gauge structural and functional integrity of visual systems
in their small animal models. The SAVSAM is also committed to training users and enhancing
visual system testing capabilities at BCM.

## Key facts

- **NIH application ID:** 10896357
- **Project number:** 5P30EY002520-45
- **Recipient organization:** BAYLOR COLLEGE OF MEDICINE
- **Principal Investigator:** Ching-Kang Jason Chen
- **Activity code:** P30 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $153,205
- **Award type:** 5
- **Project period:** 1997-07-01 → 2025-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10896357

## Citation

> US National Institutes of Health, RePORTER application 10896357, Small Animal Visual System module (5P30EY002520-45). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10896357. Licensed CC0.

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