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Related Concept Videos

Shearing Strain01:20

Shearing Strain

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The shearing strain represents a cubic element's angular change when subjected to shearing stress. This type of stress can transform a cube into an oblique parallelepiped without influencing normal strains. The cubic element experiences a significant transformation when exposed solely to shearing stress. Its shape alters from a perfect cube into a rhomboid, clearly demonstrating the effect of shearing strain. The degree of this strain is considered positive if it reduces the angle between the...
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Elastic Strain Energy for Shearing Stresses01:20

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As discussed in previous lessons, strain energy in a material is the energy stored when it is elastically deformed, a concept crucial in materials science and mechanical engineering. This energy results from the internal work done against the cohesive forces within the material. When a material undergoes shearing stress and corresponding shearing strain, the strain energy density, which is the energy stored per unit volume, is calculated. Within the elastic limit, where the stress is...
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Proteoglycans01:05

Proteoglycans

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Glycans, a class of complex heterogeneous molecules, can be covalently attached to proteins to form glycosylated proteins that regulate various physiological and pathological processes. Glycosylated proteins or glycoproteins comprise N-linked and O-linked oligosaccharides. O-glycosylation is the most common type of protein glycosylation. Here, glycans attach to the oxygen atom of the hydroxyl groups of Serine or Threonine residues. O-linked glycosylation occurs later in protein processing,...
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Proteoglycans are extensively glycosylated proteins, commonly found in the extracellular matrix, interwoven with collagen fibers. Hyaline cartilage, the most common type of cartilage in the body, consists of short and dispersed collagen fibers associated with large amounts of proteoglycans. These proteoglycans have long negative charges that attract cations, which in turn attract water molecules. This influx of ions and water molecules swells up the proteoglycan like a water-soaked gel that can...
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The different configurations of source-load connections include wye (star) and delta connections. The relationship between line and phase voltages and currents varies depending on the configuration. When the source is supplying power, it is transmitted through the wires to the load, and during this transmission, some power is absorbed by the wires, leading to line loss.
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Maximum shear strain-based algorithm can predict proteoglycan loss in damaged articular cartilage.

Atte S A Eskelinen1, Mika E Mononen2, Mikko S Venäläinen3

  • 1Department of Applied Physics, University of Eastern Finland, Yliopistonranta 1, POB 1627, 70211, Kuopio, Finland. attees@uef.fi.

Biomechanics and Modeling in Mechanobiology
|January 12, 2019
PubMed
Summary

Predicting post-traumatic osteoarthritis (PTOA) progression is crucial. A new algorithm using maximum shear strain effectively predicts proteoglycan loss in injured cartilage, aiding early identification of high-risk defects.

Keywords:
CartilageFinite element analysisModelingOsteoarthritisProteoglycanStrain

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Area of Science:

  • Biomechanical Engineering
  • Orthopedics
  • Biomaterials Science

Background:

  • Post-traumatic osteoarthritis (PTOA) compromises articular cartilage integrity due to injurious loading, leading to defects.
  • Proteoglycan depletion is an early indicator around cartilage defects in PTOA.
  • Current limitations exist in fully restoring injured cartilage and predicting PTOA progression.

Purpose of the Study:

  • To develop a predictive algorithm for proteoglycan loss in injured cartilage.
  • To investigate biomechanical stimuli, specifically local strains and stresses, driving fixed charge density (FCD) loss.
  • To assess the algorithm's potential for predicting PTOA progression.

Main Methods:

  • Developed a computational algorithm to predict FCD concentration decrease in injured cartilage.
  • Evaluated multiple mechanisms based on local strains or stresses for FCD loss.
  • Utilized a degeneration threshold linked to chondrocyte apoptosis and matrix damage.

Main Results:

  • The algorithm driven by maximum shear strain demonstrated significant FCD loss adjacent to the lesion, aligning with literature.
  • Strain-based FCD degeneration showed qualitatively similar loss patterns regardless of coordinate system or threshold selection.
  • Greatest FCD losses consistently occurred in tissue bordering the defect.

Conclusions:

  • The proposed strain-based FCD degeneration algorithm shows promise for predicting PTOA progression.
  • Biomechanical stimuli, particularly maximum shear strain, are key drivers of FCD loss.
  • This predictive capability can help identify high-risk cartilage defects for early intervention.