JOURNAL ARTICLE

KOMAROVA'S BONE REMODELING TYPE MODEL REVISITED WITHIN THE CONTEXT OF A NEW PARAMETER AFFECTING BOTH PRODUCTION AND REMOVAL ACTIVITIES OF OSTEOBLASTS AND OSTEOCLASTS.

  • Published In: Journal of Mechanics in Medicine & Biology, 2023, v. 23, n. 10. P. 1 1 of 3

  • Database: Academic Search Ultimate 2 of 3

  • Authored By: RAMTANI, SALAH; TOUDJI, LIZA; BOUKHAROUBA, TAOUFIK; BOUCETTA, ABDELKADER; GARZÓN-ALVARADO, DIEGO A. 3 of 3

Abstract

Taking advantage of the well-known Komarova's type model, it is proposed here to analyze again the bone dynamics process within the context of a new parameter introduced in order to act only on the production/removal activities of osteoblasts and osteoclasts (BMU) while saving the net effectiveness of osteoclast-or-osteoblast-derived autocrine or paracrine factors. The effects of this new parameter upon simulation of the bone remodeling cycle as well as stable, intrinsically regulated oscillatory changes in bone cell numbers and bone mass are analyzed and from which unstable oscillations, similar to pathologically accelerated bone remodeling of Paget's disease appear. One can say that the introduced d parameter, with 0 ≤ d < 1 , can be viewed as a new parameter driven by Pathological conditions. On the other hand, the parameter d can probably be linked to the complex mechanisms that regulate communication between osteoblasts and osteoclasts, which are known as critical to bone cell biology. [ABSTRACT FROM AUTHOR]

Additional Information

  • Source:Journal of Mechanics in Medicine & Biology. 2023/12, Vol. 23, Issue 10, p1
  • Document Type:Article
  • Subject Area:Health and Medicine
  • Publication Date:2023
  • ISSN:0219-5194
  • DOI:10.1142/S0219519423500410
  • Accession Number:174823528
  • 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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