# Admin-Core-001

> **NIH NIH U54** · STANFORD UNIVERSITY · 2022 · $440,000

## Abstract

OVERALL: SUMMARY
We propose the Stanford U54 SARS-CoV-2 Serological Sciences Center of Excellence (SUSS-COE) as a
member of the SeroNet consortium gathered to address the urgent need for better understanding of human
immune responses to the SARS-CoV-2 coronavirus pandemic that has engulfed the U.S. and the world.
Our Center will be based on four scientific pillars:
  Deep mechanistic analysis of the adaptive immune responses of COVID-19 patients, spanning
 serological, B cell and T cell responses,
  Analysis of immune responses in the blood as well as mucosal sites,
  Comparing immune responses induced by infection to those induced by candidate vaccines, and
  Studying medically underserved, underrepresented and at-risk patient populations
Within these parameters, we will attempt to determine the factors that result in effective and durable immunity
to SARS-CoV-2 infection.
We are dedicated to broad collaboration, rapid sharing of data and technical knowledge, nimbleness in
responding to the rapidly changing pandemic, and rapid translation of research findings to CLIA Lab clinical
testing and development of new therapeutic approaches. We feel these are the best routes forward for
addressing gaps in our understanding of the determinants of protective immunity to SARS-CoV-2, and
providing useful tools for physicians and patients.

## Key facts

- **NIH application ID:** 10709110
- **Project number:** 3U54CA260517-02S1
- **Recipient organization:** STANFORD UNIVERSITY
- **Principal Investigator:** Scott Dexter Boyd
- **Activity code:** U54 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $440,000
- **Award type:** 3
- **Project period:** 2022-09-01 → 2025-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10709110

## Citation

> US National Institutes of Health, RePORTER application 10709110, Admin-Core-001 (3U54CA260517-02S1). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10709110. Licensed CC0.

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