# Neutrophil Decision Making in Confined Environments in Health and Disease

> **NIH NIH R01** · MASSACHUSETTS GENERAL HOSPITAL · 2020 · $361,200

## Abstract

Sepsis diagnosis poses significant clinical and scientific challenges. The incidence of sepsis in the US is twice
the rate of congestive heart failure, six times the rate of colon cancer, and 20 times the incidence of AIDS, and
sepsis is the single most significant expense in Medicare budget. Early and more accurate diagnostic of sepsis
could save lives, reduce costs, and improve treatment. However, the precision of early sepsis diagnosis today
is ~70% (one in every three patients is misdiagnosed). Blood cultures are the gold standard, but their results
are available 3-4 days after clinical decisions have been made. Towards the goal of early and accurate sepsis
diagnosis, we will focus on microfluidic tools that measure neutrophil inflammatory and anti-microbial functions.
We will pursue three enabling technologies to better understand the functionality of neutrophils in the context of
sepsis. We will increase the sensitivity and reduce the duration of a new assay for sepsis based on the
spontaneous neutrophil migration, we will make precision measurements of neutrophil cooperation against live
microbes using new swarming arrays, and we will design devices to trap neutrophil-derived chromatin from
blood (cNETs) and identify bacteria in blood during sepsis.

## Key facts

- **NIH application ID:** 9897528
- **Project number:** 5R01GM092804-11
- **Recipient organization:** MASSACHUSETTS GENERAL HOSPITAL
- **Principal Investigator:** Daniel Irimia
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $361,200
- **Award type:** 5
- **Project period:** 2010-05-01 → 2023-03-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9897528

## Citation

> US National Institutes of Health, RePORTER application 9897528, Neutrophil Decision Making in Confined Environments in Health and Disease (5R01GM092804-11). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/9897528. Licensed CC0.

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