Applied Mandibular Osteometry in Young Lambs: Morphometric and Clinical Insights.
Published In: Anatomia, Histologia, Embryologia: Journal of Veterinary Medicine Series C, 2025, v. 54, n. 1. P. 1 1 of 3
Database: Academic Search Ultimate 2 of 3
Authored By: de Lana, Julyana Fernandes; Lopes, Carolina Figueiredo; Pereira, Vitor Pires; Chiarello, Guilherme Pereira; Depedrini, Jurema Salerno; Pérez, William; de Morais‐Pinto, Luciano 3 of 3
Abstract
Osteometric studies of the mandible are useful for identifying polymorphisms that are affected by general factors of anatomical variation, such as breed and gender, but age‐related changes have not yet been reported in sheep. Our results showed that the morphometric parameters of the mandible were significantly affected by the age of the lambs. However, at 155 days of age, the mandible already presents all the morphological characteristics observed in adult animals. Furthermore, this study revealed, by analysing Person's correlation coefficient, that the position of the mental and mandibular foramen is already established at 155 days of age and will not be affected by the proportional growth of the mandible. These data have direct implications for veterinary practice, as they can increase the precision and effectiveness of clinical and anaesthetic procedures on the mandible. This contributes to maintaining the health and well‐being of animals, improving productive performance in farming systems. [ABSTRACT FROM AUTHOR]
Additional Information
- Source:Anatomia, Histologia, Embryologia: Journal of Veterinary Medicine Series C. 2025/01, Vol. 54, Issue 1, p1
- Document Type:Article
- Subject Area:Zoology
- Publication Date:2025
- ISSN:0340-2096
- DOI:10.1111/ahe.70018
- Accession Number:183821106
- Copyright Statement:Copyright of Anatomia, Histologia, Embryologia: Journal of Veterinary Medicine Series C is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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