# Development of Nanomembrane Electronics and Machine-Learning Algorithms for Quantitative Screening of Dysphagia Therapeutics

> **NIH NIH R21** · GEORGIA INSTITUTE OF TECHNOLOGY · 2021 · $211,947

## Abstract

Project Summary
Dysphagia is an impairment of the swallow reflex's neurological and muscular functions that
causes a debilitating and potentially deadly condition such as choking, malnutrition, dehydration
or pneumonia during swallowing. Dysphagia afflicts almost 15 million Americans, particularly
individuals 50-60 years or older with up to a 20% chance of dysphagia. However, regardless of
the cause of dysphagia, currently there are no available therapeutic treatments. Limitation of
preclinical tools and methods to study dysphagia is one of the biggest reasons for the lack of
therapeutic treatment for dysphagia. Video-fluoroscopic swallowing study (VFSS) has been
used to diagnose dysphagia in a clinical study as well as research with animal models for drug
development. However, the VFSS method in clinical study relies on the active cooperation of a
human subject, such as ingestion of food with barium (oral contrast agent) and movement
immobilization during X-ray imaging. The VFSS tool shows the severe issue in an animal study
due to uncontrollable target, which results in poor image quality and unreliable drug
development. Overall, none of the existing commercial systems can offer a portable, real-time,
continuous monitoring of swallowing with either humans or animals.
Here, this project will develop a novel, nanomembrane electronic system that offers a
continuous, quantitative assessment of swallowing activities in a non-invasive way on the skin of
rat models, which will help to develop potential dysphagia drugs. Specifically, we will develop
soft, ultrathin, lightweight, miniaturized wearable electronics to monitor time-dependent changes
of swallowing muscle functions via wireless, real-time recording of electromyograms on
swallowing muscles of a dysphagia rat model. In this project, our initial study in the evaluation of
dysphagia therapeutics will focus on ALS-related dysphagia since there are well-established
animal models (transgenic superoxide dismutase; SODG93A) with severe dysphagia at a young
age. SODG93A animal models have been widely used to screen potential therapeutic
compounds, including two FDA-approved ALS drugs: edaravone and riluzole. Collectively, if
successful, the newly developed nanomembrane electronics will be a game-changer in the
therapeutic evaluation of candidate drugs for ALS-related dysphagia as well as other diseases-
related dysphagia. The research outcome is expected to provide a new drug for an effective
treatment of dysphagia, which will eventually reduce mortality and improve the quality of life of
dysphagia patients.

## Key facts

- **NIH application ID:** 10373326
- **Project number:** 1R21EB031535-01A1
- **Recipient organization:** GEORGIA INSTITUTE OF TECHNOLOGY
- **Principal Investigator:** Woon-Hong Yeo
- **Activity code:** R21 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $211,947
- **Award type:** 1
- **Project period:** 2021-09-23 → 2024-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10373326

## Citation

> US National Institutes of Health, RePORTER application 10373326, Development of Nanomembrane Electronics and Machine-Learning Algorithms for Quantitative Screening of Dysphagia Therapeutics (1R21EB031535-01A1). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10373326. Licensed CC0.

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