Musculoskeletal Disease Modeling
Musculoskeletal Disease Modeling - Related Videos
10:53
A Novel Application of Musculoskeletal Ultrasound Imaging
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Cited by 14 •
2013
We describe a new ultrasound-based vector tissue Doppler imaging technique to measure muscle contraction velocity, strain and strain rate with sub-millisecond temporal resolution during dynamic activities. This approach provides complementary measurements of dynamic muscle function and could lead to a better understanding of mechanisms underlying musculoskeletal disorders.
09:18
A 3D Bone Culture Platform Using Human Osteocytes and Decellularized Extracellular Matrix for Modeling Musculoskeletal Diseases
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2026
This protocol describes the fabrication of 3D bone constructs using human osteocytes embedded in methacrylated gelatin (GelMA) hydrogels supplemented with decellularized extracellular matrix. The method is low-cost, scalable, and compatible with basic laboratory equipment, providing a translational platform for studying musculoskeletal disease processes.
07:49
Isolating Stem Cells from Soft Musculoskeletal Tissues
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Cited by 21 •
2010
Isolating adult stem cells from musculoskeletal soft tissues based on the cell's adherence speed to flask.
09:32
Subject-specific Musculoskeletal Model for Studying Bone Strain During Dynamic Motion
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Cited by 3 •
2018
During landing, lower-body bones experience large mechanical loads and are deformed. It is essential to measure bone deformation to better understand the mechanisms of bone stress injuries associated with impacts. A novel approach integrating subject-specific musculoskeletal modeling and finite element analysis is used to measure tibial strain during dynamic movements.
09:23
Parasite Induced Genetically Driven Autoimmune Chagas Heart Disease in the Chicken Model
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Cited by 12 •
2012
The inoculation of Trypanosoma cruzi in fertile eggs prior to incubation renders the parasite kDNA minicircle integration in embryo cells genome. Crossbreeding reveals the vertical transfer of the mutations to progeny. The kDNA integrates into coding regions at several chromosomes and the chickens die with an inflammatory autoimmune heart disease.
FAQs
Each component represents a different influence on tissue behavior. Cells provide the biological response, biomaterials help reproduce aspects of tissue structure, mechanical forces represent loading, and biochemical signals can model inflammation, degeneration, or impaired repair. Combining these elements allows researchers to examine how interacting conditions contribute to disease rather than studying each factor in isolation.
Mechanical forces help reproduce the loading environment experienced by musculoskeletal tissues. Including them can make a model more representative of conditions involving tissue stress, degeneration, or altered repair. Comparing systems with different loading conditions may therefore help researchers determine how physical influences interact with cells and biochemical signals during disease development.
Computational approaches can extend experimental observations by representing how biological, structural, mechanical, and biochemical factors interact over time. This supports prediction of changing disease-related responses under controlled conditions. Used alongside bioengineered systems, computational modeling can help interpret complex interactions and guide questions about therapeutic responses or patient-specific treatment strategies.
Model design should reflect the features of the disorder that researchers need to investigate. A system may emphasize tissue structure, mechanical loading, inflammation, degeneration, or impaired repair, depending on the condition and research question. Matching these features to the disease helps produce information that is relevant to mechanism studies, treatment evaluation, or regenerative research.
Researchers can expose controlled disease-model systems to candidate treatments and examine how the modeled tissue responses change. Because the systems reproduce selected disease-relevant conditions, they can help compare drug responses and reveal biological factors associated with improvement or continued dysfunction. Those observations may support identification of therapeutic targets before developing broader treatment strategies.
These models provide controlled settings for testing how tissue-repair strategies perform under disease-relevant conditions. Their ability to incorporate selected cells, materials, forces, and biochemical signals can help assess regenerative approaches more specifically than a generalized system. Computational extensions may also support patient-specific treatment planning by representing how relevant factors interact over time.