Genomics, Microbiomics, and Bioenergetics-Based Personalized Treatment for Head Trauma Patients at Risk for Sepsis

Abstract

Systemic, non-neurological complications such as sepsis and organ failure are common after severe traumatic brain injury (TBI). Sepsis occurs in 50-75% of patients with severe TBI and can lead to multiple organ system failure, but the search for early-detection biomarkers for sepsis is ongoing. This clinical study examines the correlations between blood cell gene expression and bioenergetics with the clinical observations and long-term outcomes of severe TBI patients admitted to the R. Adams Cowley Shock Trauma Center in order to identify prognostic biomarkers for sepsis and organ failure. Our hypothesis states that gene expression and mitochondrial health of circulating blood cells sense stress in patients and may serve as biomarkers of human pathologies such as sepsis. Gene expression is investigated by whole blood ribonucleic acid (RNA) isolation and analysis via Nanostring nCounter (registered trademark) over the course of 7 days post-injury. Similarly, fresh blood is drawn over the first 7 days for isolation and bioenergetic examination of peripheral blood mononuclear cells (PBMCs). Through the use of machine learning techniques, these data sets were compared to clinical data with the purpose of identifying novel biomarkers as early diagnostic tools and improving outcomes of patients with severe TBI. The results of these studies are detailed in the following technical report.

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Document Details

Document Type
Technical Report
Publication Date
Dec 01, 2023
Accession Number
AD1223356

Entities

People

  • Apurva Borcar
  • Claire Fraser
  • Deborah Stein
  • Gary Fiskum
  • Jennifer Klinedinst
  • Parisa Rangghran
  • Peter Hu
  • Rosemary Kozar
  • Sam Galvagno
  • Shiming Yang

Organizations

  • University of Maryland, Baltimore

Tags

Fields of Study

  • Medicine

Readers

  • Gulf War Illness and Chronic Multisymptom Illness in Veterans.
  • Immunology and Pathology
  • Oncology and Biomarker-Based Cancer Detection.

Technology Areas

  • AI & ML