Evaluation of Non-Invasive Sampling Methods for Detection of Hepatitis E Virus Infected Pigs in Pens.
HEV
animal experiments
boot sock
oral fluid
pig farms
pooled faecal droppings
refinement
sensitivity
specificity
three Rs
Journal
Microorganisms
ISSN: 2076-2607
Titre abrégé: Microorganisms
Pays: Switzerland
ID NLM: 101625893
Informations de publication
Date de publication:
16 Feb 2023
16 Feb 2023
Historique:
received:
30
01
2023
revised:
13
02
2023
accepted:
14
02
2023
entrez:
25
2
2023
pubmed:
26
2
2023
medline:
26
2
2023
Statut:
epublish
Résumé
Pigs are a reservoir of hepatitis E virus (HEV), which causes hepatitis in humans. To study the epidemiology of HEV in pig farms, sampling methods are currently used that cause discomfort to pigs, such as rectal sampling. In line with the 3Rs principle, we aimed to evaluate non-invasive methods to detect pens with HEV-shedding pigs. Twenty-eight pens of one farm were sampled cross-sectionally. Individual rectal swabs (IRS) were collected to determine prevalence within pens. Four pen-level samples were compared: a pool of IRS per pen (P), boot socks (BS), oral fluid (OF) and pooled faecal droppings (FD). Each sample was tested by RT-PCR and the sensitivity and specificity of each method was determined by Bayesian latent class analysis. According to IRS, 19/28 pens were HEV positive. BS had a sensitivity of 95% and detected HEV in pens with 10% of pigs shedding; however, specificity was below 30%. FD were comparably accurate to P, with a sensitivity and specificity of 94% and 86%, respectively. BS sampling is thus advised to detect early shedding of HEV or pen contamination, and FD to determine the duration of shedding. This study demonstrates that non-invasive sampling can replace rectal swabs in research on HEV in pigs.
Identifiants
pubmed: 36838465
pii: microorganisms11020500
doi: 10.3390/microorganisms11020500
pmc: PMC9962119
pii:
doi:
Types de publication
Journal Article
Langues
eng
Subventions
Organisme : Topsector AgriFood
ID : TKI AF 18119
Organisme : European Union's Horizon 2020 Research and Innovation programme
ID : 773830
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