# Improved ventilation of the edematous lung

> **NIH NIH R01** · THE TRUSTEES OF THE STEVENS INSTITUTE OF TECHNOLOGY · 2020 · $444,595

## Abstract

Project Summary
In the acute respiratory distress syndrome (ARDS), inflammatory lung injury increases alveolar-capillary
barrier permeability. Vascular liquid floods the airspace of the lungs and causes pulmonary edema.
The airspace flooding pattern is spatially heterogeneous. Patients with ARDS are treated by
mechanical ventilation, which improves gas exchange but causes additional mechanical injury to the
lungs that escalates over time. In particular, alveolar septa located between aerated and flooded
alveoli are a site of stress concentration and the magnitude of the stress concentration is proportional
to the surface tension at the air-liquid interface in flooded alveoli. Mechanical ventilation cyclically
increases surface tension, thus exacerbates the stress concentrations and causes injury in the form of a
sustained increase in surface tension. The sustained increase in surface tension makes mechanical
ventilation yet more injurious. Thus ventilation injury escalates, with positive feedback, over time.
Working in a high tidal volume lung injury model in rats, we aim to test a surface tension-lowering
compound as a means of reducing the escalation of ventilation injury and to investigate the
mechanism through which the compound acts.

## Key facts

- **NIH application ID:** 9963333
- **Project number:** 5R01HL113577-08
- **Recipient organization:** THE TRUSTEES OF THE STEVENS INSTITUTE OF TECHNOLOGY
- **Principal Investigator:** Carrie E Perlman
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $444,595
- **Award type:** 5
- **Project period:** 2013-02-15 → 2022-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9963333

## Citation

> US National Institutes of Health, RePORTER application 9963333, Improved ventilation of the edematous lung (5R01HL113577-08). Retrieved via AI Analytics 2026-05-21 from https://api.ai-analytics.org/grant/nih/9963333. Licensed CC0.

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