# Leveraging Molecular Technologies to Improve Diagnosis and Management of Pediatric Acute Respiratory Illness in Resource-Constrained Settings

> **NIH NIH K23** · UNIV OF NORTH CAROLINA CHAPEL HILL · 2024 · $203,605

## Abstract

PROJECT SUMMARY/ABSTRACT
Antimicrobial resistance (AMR) is a rapidly growing threat to global health that is primarily driven by the
overuse and misuse of antimicrobials. The applicant’s preliminary work in Uganda confirmed what has been
noted in other resource-limited settings - children who present with febrile illnesses, particularly those with
associated respiratory symptoms, are frequently treated with antibiotic therapy despite most of them likely
having self-limited viral infections that do not require such treatment. This inappropriate dispensation of
antibiotics is in part due to the diagnostic uncertainty inherent in the reliance on clinical symptoms for diagnosis
and management of acute respiratory infections (ARI). Therefore, there is an urgent need for novel diagnostic
tools that can distinguish children with bacterial ARI from those with non-bacterial infections, thus reducing
antibiotic overuse for children presenting with this clinical syndrome. The objective of the scientific aims
proposed in this K23 application is to develop a diagnostic model that accurately predicts bacterial infection in
children with febrile ARI in Uganda. This work will facilitate the long-term goal of the applicant, which is to
become an independent investigator with expertise in diagnostic evaluation and implementation working to
improve quality of care and antimicrobial stewardship in both domestic and global contexts. Specifically, under
the mentorship of experts in the fields of emerging molecular technologies, diagnostic evaluation, and
prediction modeling at the University of North Carolina at Chapel Hill, she will (1) determine the etiology of
febrile ARI in a cohort of previously and prospectively enrolled children, enhancing the diagnostic assessment
with PCR-based and metagenomic next generation sequencing tools, (2) validate a host immune response
gene expression assay, incorporating genes previously identified to be differentially expressed in viral and
bacterial ARI, that minimizes the number of genes included to maximize feasibility in resource-constrained
settings, and (3) develop novel clinical algorithms that combine host-based classifiers with other clinical and
laboratory data to accurately predict bacterial ARI. Through pursuit of the scientific aims, the associated
experiential and hands-on laboratory and biostatistical training, and focused didactic coursework, the applicant
will address the following gaps in her knowledge: (1) molecular technologies and their role in infectious disease
diagnosis and diagnostic development, (2) clinical prediction modeling, and (3) diagnostic tool evaluation and
implementation. By the conclusion of the K23 award period, the applicant will have generated key results to
inform a competitive R01 application. She will also be well-positioned to transition into an independent
physician scientist and leading expert in diagnostic evaluation and antimicrobial stewardship in resource-
constrained settings.

## Key facts

- **NIH application ID:** 10894910
- **Project number:** 5K23AI173658-02
- **Recipient organization:** UNIV OF NORTH CAROLINA CHAPEL HILL
- **Principal Investigator:** Emily Jane Ciccone
- **Activity code:** K23 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $203,605
- **Award type:** 5
- **Project period:** 2023-07-27 → 2028-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10894910

## Citation

> US National Institutes of Health, RePORTER application 10894910, Leveraging Molecular Technologies to Improve Diagnosis and Management of Pediatric Acute Respiratory Illness in Resource-Constrained Settings (5K23AI173658-02). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10894910. Licensed CC0.

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