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Sexual dimorphism in the first rib of Homo sapiens.

Mikel ArlegiAndrea García-SagastibelzaChristine Veschambre-CoutureAsier Gómez-Olivencia
Published in: Journal of anatomy (2021)
This work aimed to study sexual dimorphism in the first rib of modern humans, with a special focus on whether differences in shape are due to divergent allometric growth in males and females. Also, we compare the accuracy of sex classification using different approaches based on two methodologies, traditional morphometry based on linear measurements and geometric morphometric analysis based on 2D landmark coordinates. The sample studied here comprised 121 right and left first ribs from 65 female and male adult recent Euro-American Homo sapiens individuals. For traditional morphometrics, 12 metric variables were collected from each rib using a digital caliper, and for geometric morphometrics, six landmarks and 31 semilandmarks were captured from photographs using digital software. Both geometric morphometric and metric data were analyzed to calculate the index of sexual dimorphism, variation related to lateral asymmetry, variation in size and shape, and allometric trends between males and females. Finally, a linear discriminant analysis (LDA) was performed comparing both methodologies to test the best approach for sex classification. Results indicated that there are significant sex differences in the size and shape of the first ribs of recent Euro-American Homo sapiens. Regression analysis revealed different allometric patterns for males and females, and this could partially explain shape differences between sexes. Additionally, traditional morphometrics showed that all characteristics analyzed are significantly dimorphic, with the midshaft minimum craniocaudal diameter, the sternal end minimum diameter, and the neck minimum craniocaudal diameter displaying the most dimorphic scores. Similarly, geometric morphometrics results indicated that males have more curved and interno-exteriorly wider first ribs. Finally, analysis of sex classification using LDA yielded slightly better accuracy for traditional morphometry (83.8%) than the geometric morphometrics approach (81.3%) based on form Procrustes coordinates. This study demonstrates the usefulness of applying two different morphometric approaches to obtain more comprehensive results.
Keyphrases
  • machine learning
  • mental health
  • young adults
  • minimally invasive
  • big data