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BioMEMS: Forging New Collaborations Between Biologists and Engineers
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Collaborative Framework to Advance Symptom Science: An Intramural Perspective.

Leorey N Saligan1

  • 1Psi Beta, Nurse Scientist, National Institute of Nursing Research, Division of Intramural Research, National Institutes of Health, Bethesda, MD, USA.

Journal of Nursing Scholarship : an Official Publication of Sigma Theta Tau International Honor Society of Nursing
|October 31, 2018
PubMed
Summary
This summary is machine-generated.

The National Institutes of Health (NIH) Symptom Science Model (SSM) guides research to uncover the molecular basis of symptoms. This framework enables rapid discovery and validation of symptom biomarkers for improved patient management.

Keywords:
Collaborative frameworksymptom clustersymptom science

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Area of Science:

  • Symptom science
  • Biomarker discovery
  • Molecular underpinnings of symptoms

Background:

  • The National Institutes of Health (NIH) Symptom Science Model (SSM) was developed to advance symptom science.
  • It guides researchers in understanding the molecular basis of symptoms using "omics" approaches.

Purpose of the Study:

  • To describe the collaborative framework used by National Institute of Nursing Research (NINR) investigators.
  • To provide a research exemplar for advancing symptom science.

Main Methods:

  • Literature review to understand symptom science.
  • Cross-sectional, case-controlled, or longitudinal studies to identify biological correlates.
  • Multidisciplinary, collaborative preclinical experiments and clinical trials for validation.

Main Results:

  • Identification of biologic correlates of symptoms using omics and bioinformatic strategies.
  • Validation of correlates for functional relevance through in vitro/ex vivo experiments or clinical trials.
  • NINR investigators identified that specific neural receptor activation can trigger inflammation-related fatigue and chronicity, and targeting these receptors can reduce symptom intensity.

Conclusions:

  • The NIH SSM facilitates rapid data generation and discovery in symptom science.
  • Collaborative efforts with experts and core laboratories enhance identification and validation of symptom correlates.
  • The framework identifies clinically relevant markers for optimizing symptom management.