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Evaluation of Respiratory System Mechanics in Mice using the Forced Oscillation Technique
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Machine learning-based data analytic approaches for evaluating post-natal mouse respiratory physiological evolution.

Wesley Wang1, Diego Alzate-Correa1, Michele Joana Alves1

  • 1Department of Pathology, Division of Neuropathology, The Ohio State University College of Medicine, Columbus, OH, United States.

Respiratory Physiology & Neurobiology
|October 3, 2020
PubMed
Summary
This summary is machine-generated.

Machine learning reveals dynamic shifts in mouse respiratory patterns during postnatal development. This analysis of breathing frequency and tidal volume offers new insights into respiratory physiology and disease phenotyping.

Keywords:
Machine learningRespiratory developmentRespiratory physiology

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Area of Science:

  • Physiology
  • Computational Biology
  • Developmental Biology

Background:

  • Respiratory parameters naturally change during postnatal development, but these shifts are not fully understood.
  • Traditional analysis of respiratory metrics in mice lacks examination of inter-variable relationships and developmental context.

Purpose of the Study:

  • To develop and apply machine learning workflows for a deeper understanding of postnatal respiratory development in mice.
  • To analyze the dynamic interplay of respiratory frequency (f) and tidal volume (TV) with inspiratory and expiratory parameters during maturation.

Main Methods:

  • Utilized plethysmography to collect respiratory data in mice.
  • Developed a machine learning workflow using the R statistical programming language.
  • Examined variations and relationships between respiratory metrics (f, TV) in relation to age and hypercapnic conditions.

Main Results:

  • The machine learning workflow successfully predicted the age of the mice.
  • Demonstrated that variations and relationships among respiratory metrics dynamically change with age.
  • Observed significant shifts in respiratory patterns during hypercapnic breathing.

Conclusions:

  • High-dimensional analysis of non-invasive respiratory metrics provides reliable predictions.
  • Machine learning offers a powerful approach to uncover complex physiological changes during development.
  • These methods hold promise for large-scale phenotyping in developmental studies and disease research.