4.1 Article

Generation of red drum (Sciaenops ocellatus) hematology Reference Intervals with a focus on identified outliers

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VETERINARY CLINICAL PATHOLOGY
卷 47, 期 1, 页码 22-28

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WILEY
DOI: 10.1111/vcp.12569

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Anemia; crude oil; Deepwater Horizon oil spill; fish; MC252

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BackgroundThe Deepwater Horizon (DWH) oil spill in 2010 released millions of barrels of crude oil into the northern Gulf of Mexico, exposing numerous species of animals to the toxic components of oil. A comprehensive assessment of morbidity and mortality caused by DWH oil exposure was undertaken by the DWH Natural Resource Damage Trustees to characterize ecosystem damages. ObjectivesThis study aimed to characterize normal hematologic RIs in red drum fish with blood cell descriptions, and to demonstrate the importance of identifying and removing outliers when generating RI. MethodsTwo years after the oil spill, 57 adult, red drum fish of mixed sexes were caught along the eastern Louisiana coastline. Eight different sites were chosen to catch the fish; 6 sites were contaminated with oil, and 2 sites were not contaminated at the time of the oil spill. Hematologic RIs were generated from heparinized whole blood samples of healthy red drum as determined by gross examination and histopathologic examination. Two methods were used to detect hematologic effects likely caused by oil contamination. ResultsRed drum PCVs (RI 42-62%) were higher than previously reported in cold water and bottom-dwelling fish species, while absolute WBC counts (RI 2.9-8.7 x10(9)/L) were comparable to WBC counts previously reported in other fish species with heterophil and lymphocyte absolute concentrations frequently being equivalent. Anemic animals (PCV<42%) were only identified in oil-contaminated sites. ConclusionRIs in many wild fish species are lacking, and therefore, this study provides valuable baseline data on healthy red drum fish. The outliers assessed using ASVCP RI guidelines can provide valuable clinical information regarding individuals in population health assessments, which may be more sensitive for the detection of abnormalities than for population statistics comparing the mean. The importance of removing outliers and rerunning RI statistics is highlighted by this field example.

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