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MECHANICAL SPECIFICATIONS OF THE LUNG AND BREAST CANCEROUS CELLS USING ATOMIC FORCE MICROSCOPE.

  • Published In: Journal of Mechanics in Medicine & Biology, 2024, v. 24, n. 5. P. 1 1 of 3

  • Database: Academic Search Ultimate 2 of 3

  • Authored By: REZAEI, IRAJ; SADEGHI, ALI 3 of 3

Abstract

Cell deformation and changes in mechanical properties, such as adhesion, elasticity modulus, and height, are some of the most significant signs of cancer. This paper investigates the mechanical parameters of normal and cancerous cells in the breast and lungs. The lung's normal cell studied is MRC-5, and the cancerous ones include A-549, COR-L105, and CALU-6. MCF-10A is the normal breast cell studied, and MDA-MB-468, MDA-MB-231, and ZR-75-1 are cancerous. The mechanical specifications and cellular topography were obtained using nanoindentation by the atomic force microscope (AFM). The elasticity modulus and adhesion amounts for at least 96 indents were computed by mathematical averaging for each cell line in two different modes. The results indicate that cancer decreases the cells' elasticity modulus by one-tenth in the lung and lower than one-third in breast cells. Cancer cells are up to several hundred times more adhesive than normal cells. In addition, a cancerous cell's height is greater than normal cells. This study's elastic modules are compared to the other references. Overall, the results are compatible. [ABSTRACT FROM AUTHOR]

Additional Information

  • Source:Journal of Mechanics in Medicine & Biology. 2024/06, Vol. 24, Issue 5, p1
  • Document Type:Article
  • Subject Area:Physics
  • Publication Date:2024
  • ISSN:0219-5194
  • DOI:10.1142/S0219519423500793
  • Accession Number:178482474
  • 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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