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Method for Quantitative Study of Airway Functional Microanatomy Using Micro-Optical Coherence Tomography

Overview of attention for article published in PLOS ONE, January 2013
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Title
Method for Quantitative Study of Airway Functional Microanatomy Using Micro-Optical Coherence Tomography
Published in
PLOS ONE, January 2013
DOI 10.1371/journal.pone.0054473
Pubmed ID
Authors

Linbo Liu, Kengyeh K. Chu, Grace H. Houser, Bradford J. Diephuis, Yao Li, Eric J. Wilsterman, Suresh Shastry, Gregory Dierksen, Susan E. Birket, Marina Mazur, Suzanne Byan-Parker, William E. Grizzle, Eric J. Sorscher, Steven M. Rowe, Guillermo J. Tearney

Abstract

We demonstrate the use of a high resolution form of optical coherence tomography, termed micro-OCT (μOCT), for investigating the functional microanatomy of airway epithelia. μOCT captures several key parameters governing the function of the airway surface (airway surface liquid depth, periciliary liquid depth, ciliary function including beat frequency, and mucociliary transport rate) from the same series of images and without exogenous particles or labels, enabling non-invasive study of dynamic phenomena. Additionally, the high resolution of μOCT reveals distinguishable phases of the ciliary stroke pattern and glandular extrusion. Images and functional measurements from primary human bronchial epithelial cell cultures and excised tissue are presented and compared with measurements using existing gold standard methods. Active secretion from mucus glands in tissue, a key parameter of epithelial function, was also observed and quantified.

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The data shown below were compiled from readership statistics for 121 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 3 2%
Singapore 1 <1%
Unknown 117 97%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 39 32%
Researcher 24 20%
Student > Doctoral Student 9 7%
Professor > Associate Professor 9 7%
Student > Postgraduate 6 5%
Other 15 12%
Unknown 19 16%
Readers by discipline Count As %
Engineering 34 28%
Agricultural and Biological Sciences 18 15%
Medicine and Dentistry 17 14%
Physics and Astronomy 13 11%
Biochemistry, Genetics and Molecular Biology 4 3%
Other 13 11%
Unknown 22 18%