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Record W7106016213 · doi:10.7939/83072

Mechanical Suppression of Microgravity-Induced Chondrocyte Hypertrophy in Engineered Human Cartilage

2025· dissertation· en· W7106016213 on OpenAlexaboutno aff

Bibliographic record

VenueUniversity of Alberta Library · 2025
Typedissertation
Languageen
FieldMedicine
TopicSpaceflight effects on biology
Canadian institutionsnot available
Fundersnot available
KeywordsEndochondral ossificationChondrogenesisCartilageMesenchymal stem cellChondrocyteSpaceflightOssificationTissue engineeringMuscle hypertrophy

Abstract

fetched live from OpenAlex

Articular cartilage (AC) of the knee exists under a low oxygen and mechanically active environment. Mechanical unloading conditions of spaceflight microgravity pose a risk for astronauts to develop knee osteoarthritis-like pathology, which culminates in AC atrophy and breakdown, joint space narrowing, subchondral bone thickening, and the formation of bone spurs. Mesenchymal stem cells (MSC) are a promising cell source for AC replacement given their potential to form AC precursor cells – chondrocytes, but current protocols can lead to undesirable hypertrophic differentiation that progresses to bone through the process of endochondral ossification when implanted in vivo. We have previously found that combining low oxygen and mechanical loading (mechano-hypoxia conditioning) during cartilage development from human bone marrow (hBM) derived-MSC under gravity loading conditions can reduce OA-like molecular characteristics. This research explored the treatment of mechano-hypoxia conditioning during chondrogenic stimulation of MSC under simulated microgravity (SMG) as a methodology to produce stable chondrocytes without the propensity to form bone. This was explored through the following objectives: 1. Determine the effect of mechano-hypoxia conditioning on chondrogenically stimulated MSC under SMG. 2. Determine if mechano-hypoxia-conditioned tissue-engineered cartilage from MSC resists bone formation after implantation in vivo. To achieve these objectives, hBM MSC sourced from four male (ages 25-51) and four female (ages 19-41) donors were isolated, expanded, and chondrogenically induced to form engineered cartilage models. The engineered cartilage models were subjected to static (gravity) conditions, SMG for 3 weeks, SMG for 6 weeks, and SMG for 3 weeks + dynamic compression (DC) respectively. SMG was applied to the constructs by culturing in a rotatory wall vessel bioreactor for the specified time. At the experimental endpoint, tissue constructs were randomly distributed for downstream analyses including gene expression, mechanical tests, biochemical assays for extracellular matrix, histology, and subcutaneous implantation in mice following University of Alberta animal use protocols. After 5 weeks, the implanted tissue was retrieved and imaged via micro-CT, micro-X-ray fluorescent imaging for biomineralization and scanning electron microscope (SEM) imaging for the structure and composition of calcification. Data from this study shows that Mechano-hypoxia conditioning significantly reduced the heightened expression of collagen X induced by SMG, validating this conditioning method as a countermeasure to microgravity-induced chondrocyte hypertrophy. Additionally, Matrix gla protein (MGP), a potent inhibitor of bone formation, was significantly upregulated by mechano-hypoxia conditioning. This increase was found to be correlated with a significant resistance to bone formation in vivo, measured through micro-CT analysis. This data suggests that mechano-hypoxia conditioning has the potential to counteract osteoarthritic characteristics induced in cartilage from SMG and lends insight into the mechanisms involved in the process of mechanosignalling for osteoarthritis and new areas of drug discovery research. The outcome of this experiment will advance our understanding of the molecular processes of cartilage breakdown in microgravity and avenues for prevention.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.019
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.007
GPT teacher head0.221
Teacher spread0.213 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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