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dc.contributor.authorWilson, Abbey E.
dc.contributor.authorSergiel, Agnieszka
dc.contributor.authorSelva, Nuria
dc.contributor.authorSwenson, Jon
dc.contributor.authorZedrosser, Andreas
dc.contributor.authorStenhouse, Gordon
dc.contributor.authorJanz, David M.
dc.date.accessioned2022-03-24T13:09:14Z
dc.date.available2022-03-24T13:09:14Z
dc.date.created2021-04-07T12:55:07Z
dc.date.issued2021
dc.identifier.citationMethodsX. 2021, 8 .
dc.identifier.issn2215-0161
dc.identifier.urihttps://hdl.handle.net/11250/2987403
dc.description.abstractEnzyme immunoassays (EIAs) are a common tool for measuring steroid hormones in wildlife due to their low cost, commercial availability, and rapid results. Testing technologies improve continuously, sometimes requiring changes in protocols or crucial assay components. Antibody replacement between EIA kits can cause differences in EIA sensitivity, which can hinder monitoring hormone concentration over time. The antibody in a common cortisol EIA kit used for long-term monitoring of stress in wildlife was replaced in 2014, causing differences in cross reactivity and standard curve concentrations. Therefore, the objective of this study was to develop a method to standardize results following changes in EIA sensitivity. We validated this method using cortisol concentrations measured in the hair of brown bears (Ursus arctos). • We used a simple linear regression to model the relationship between cortisol concentrations using kit 1 and kit 2. • We found a linear relationship between the two kits (R2 = 0.85) and used the regression equation (kit2 = (0.98 × kit1) + 1.65) to predict cortisol concentrations in re-measured samples. • Mean predicted percent error was 16% and 72% of samples had a predicted percent error <20%, suggesting that this method is well-suited for correcting changes in EIA sensitivity.
dc.language.isoeng
dc.titleCorrecting for enzyme immunoassay changes in long term monitoring studies
dc.typePeer reviewed
dc.typeJournal article
dc.description.versionpublishedVersion
dc.source.pagenumber11
dc.source.volume8
dc.source.journalMethodsX
dc.identifier.doi10.1016/j.mex.2021.101212
dc.identifier.cristin1902709
cristin.ispublishedtrue
cristin.fulltextoriginal
cristin.qualitycode1


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