Literature DB >> 30324720

Bonamia in Ostrea angasi: Diagnostic performance, field prevalence and intensity.

Jessica J Buss1,2, Kathryn H Wiltshire2, Thomas A A Prowse3, James O Harris1, Marty R Deveney2.   

Abstract

Bonamia spp. parasites threaten flat oyster (Ostrea spp.) farming worldwide. Understanding test performance is important for designing surveillance and interpreting diagnostic results. Following a pilot survey which found low Bonamia sp. intensity in farmed Ostrea angasi, we tested further oysters (n = 100-150) from each of three farms for Bonamia sp. using heart smear, histology and qPCR. We used a Bayesian Latent Class Model to assess diagnostic sensitivity (DSe) and specificity (DSp) of these tests individually or in combination, and to assess prevalence. Histology was the best individual test (DSe 0.76, DSp 0.93) compared to quantitative polymerase chain reaction (qPCR) (DSe 0.69, DSp 0.93) and heart smear (DSe 0.61, DSp 0.60). Histology combined with qPCR and defining a positive from either test as an infected case maximized test performance (DSe 0.91, DSp 0.88). Prevalence was higher at two farms in a high-density oyster growing region than at a farm cultivating oysters at lower density. Parasite intensities were lower than in New Zealand and European studies, and this is probably contributed to differences in the performance of test when compared to other studies. Understanding diagnostic test performance in different populations can support the development of improved Bonamia surveillance programs.
© 2018 John Wiley & Sons Ltd.

Entities:  

Keywords:  zzm321990Ostrea angasizzm321990; Bonamia sp.; sensitivity; specificity

Mesh:

Year:  2018        PMID: 30324720     DOI: 10.1111/jfd.12906

Source DB:  PubMed          Journal:  J Fish Dis        ISSN: 0140-7775            Impact factor:   2.767


  1 in total

1.  Field Evaluation of Diagnostic Test Sensitivity and Specificity for Salmonid Alphavirus (SAV) Infection and Pancreas Disease (PD) in Farmed Atlantic salmon (Salmo salar L.) in Norway Using Bayesian Latent Class Analysis.

Authors:  Mona Dverdal Jansen; Mario Guarracino; Marianne Carson; Ingebjørg Modahl; Torunn Taksdal; Hilde Sindre; Edgar Brun; Saraya Tavornpanich
Journal:  Front Vet Sci       Date:  2019-11-28
  1 in total

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