Assessment of 2 Hair Removal Methods in New Zealand White Rabbits (


Journal

Journal of the American Association for Laboratory Animal Science : JAALAS
ISSN: 2769-6677
Titre abrégé: J Am Assoc Lab Anim Sci
Pays: United States
ID NLM: 101269489

Informations de publication

Date de publication:
01 05 2022
Historique:
pubmed: 2 3 2022
medline: 26 5 2022
entrez: 1 3 2022
Statut: ppublish

Résumé

Rabbits are frequently used as surgical models in research. However, studies assessing the effects of various hair removal methods on wound healing and surgical site infection (SSI) in rabbits are sparse. Here we evaluated the effects of 2 hair removal methods-clipping with electric clippers and using a commercial depilatory agent-on wound healing and SSI as assessed via wound scoring and histology. Incisions were assigned ASEPSIS scores on days 3 and 7. To assess whether the hair removal methods influenced aseptic preparation, swabs for bacterial culture were obtained just after hair removal on day 0, after aseptic skin preparation on day 0, and on day 1. For histopathologic assessment, full-thickness punch biopsies were obtained on days 0, 1, 3, 7, and 21. Histopathology revealed significant differences between the 2 methods, with the depilatory method having consistently higher scores (that is, more abnormalities). We conclude that for a surgical preparation regimen, clipping is safer, more efficacious, and less traumatic to tissues in rabbits.

Identifiants

pubmed: 35227364
doi: 10.30802/AALAS-JAALAS-21-000110
pmc: PMC9137288
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

296-303

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Auteurs

Jennifer E LeFors (JE)

Departments of Laboratory Animal Resources, Bethesda, MD;, Email: jennifer.lefors@usuhs.edu.

Laura M Anderson (LM)

Departments of Laboratory Animal Resources, Silver Spring, MD.

Margaret A Hanson (MA)

Pathology, Walter Reed Army Institute of Research, Silver Spring, MD.

Sorana Raiciulescu (S)

Preventive Medicine and Biostatistics, Uniformed Services University, Bethesda, MD.

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Classifications MeSH