JOURNAL ARTICLE

MATERIAL STRENGTH AND MECHANICAL FAILURE MODE OF PORCINE SPINAL DURA MATER UNDER A PRESSURIZED LOADING CONDITION.

  • Published In: Journal of Mechanics in Medicine & Biology, 2025, v. 25, n. 3. P. 1 1 of 3

  • Database: Academic Search Ultimate 2 of 3

  • Authored By: TAMURA, ATSUTAKA; SAKAUE, CHIKANO 3 of 3

Abstract

Mechanical damage to the meninges, which protect the spinal cord from blunt external forces, can cause idiopathic cerebrospinal fluid (CSF) leakage. This is probably because even a small meningeal failure leads to the leakage of CSF out of the subarachnoid space. However, the dura mater, the outermost layer of the meninges, is especially resilient and structurally tough. Moreover, CSF leakage can be caused by daily activities, including coughing, sneezing, and falling. Because of these contradicting facts, the essential mechanism of CSF leakage is difficult to understand. Recently, extensive efforts have been made to elucidate the mechanism of traumatic and impact-related injuries through computational simulations. It is crucial to comprehend the actual failure mode of biological materials under in vivo-like injurious loading conditions to enhance the accuracy of injury prediction. Therefore, in this study, we focused on the relationship between the intrinsic shape of wrinkles formed on the dural surface and the mechanical failure mode of the spinal dura mater. We found that a generated crack runs along the microscopic wrinkles in the longitudinal direction even when the spinal dura mater is statically pressurized. [ABSTRACT FROM AUTHOR]

Additional Information

  • Source:Journal of Mechanics in Medicine & Biology. 2025/04, Vol. 25, Issue 3, p1
  • Document Type:Article
  • Subject Area:Health and Medicine
  • Publication Date:2025
  • ISSN:0219-5194
  • DOI:10.1142/S0219519424500398
  • Accession Number:184634217
  • Copyright Statement:Copyright of Journal of Mechanics in Medicine & Biology is the property of World Scientific Publishing Company 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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