Comparison of polynomial fitting versus single time point analysis of ECIS data for barrier assessment.


Journal

Physiological reports
ISSN: 2051-817X
Titre abrégé: Physiol Rep
Pays: United States
ID NLM: 101607800

Informations de publication

Date de publication:
10 2021
Historique:
received: 30 06 2021
accepted: 05 07 2021
entrez: 4 10 2021
pubmed: 5 10 2021
medline: 11 3 2022
Statut: ppublish

Résumé

Electrical cell-substrate impedance sensing (ECIS) is an in vitro methodology for measuring the barrier integrity of a variety of cell types, including pulmonary endothelial cells. These experiments are frequently used for in vitro assessment of lung injury. The data derived from ECIS experiments consists of repeated measures of resistance across an endothelial monolayer. As such, these data reflect the dynamic changes in electrical resistance that occur over time. Currently methodologies for assessing ECIS data rely on single point assessments of barrier function, such as the maximal drop in trans-endothelial electrical resistance (TER

Identifiants

pubmed: 34605187
doi: 10.14814/phy2.14983
pmc: PMC8488550
doi:

Types de publication

Journal Article Research Support, N.I.H., Extramural

Langues

eng

Sous-ensembles de citation

IM

Pagination

e14983

Subventions

Organisme : NHLBI NIH HHS
ID : K08 HL132055
Pays : United States
Organisme : NHLBI NIH HHS
ID : R01 HL133413
Pays : United States

Informations de copyright

© 2021 The Authors. Physiological Reports published by Wiley Periodicals LLC on behalf of The Physiological Society and the American Physiological Society.

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Auteurs

Karthik Suresh (K)

Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA.

Laura Servinsky (L)

Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA.

Laura Johnston (L)

Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA.

Naresh M Punjabi (NM)

Department of Medicine, University of Miami, Coral Gables, FL, USA.

Steven M Dudek (SM)

Department of Medicine, University of Illinois, Chicago, IL, USA.

Mahendra Damarla (M)

Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA.

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Classifications MeSH