Non-hypertrophic chondrogenesis of mesenchymal stem cells through mechano-hypoxia programing
Bibliographic record
Abstract
Cartilage tissue engineering aims to generate functional replacements to treat cartilage defects from damage and osteoarthritis. Human bone marrow-derived mesenchymal stem cells (hBM-MSC) are a promising cell source for making cartilage, but current differentiation protocols require the supplementation of growth factors like TGF-β1 or −β3. This can lead to undesirable hypertrophic differentiation of hBM-MSC that progress to bone. We have found previously that exposing engineered human meniscus tissues to physiologically relevant conditions of the knee (mechanical loading and hypoxia; hence, mechano-hypoxia conditioning) increased the gene expression of hyaline cartilage markers, SOX9 and COL2A1, inhibited hypertrophic marker COL10A1, and promoted bulk mechanical property development. Adding further to this protocol, we hypothesize that combined mechano-hypoxia conditioning with TGF-β3 growth factor withdrawal will promote stable, non-hypertrophic chondrogenesis of hBM-MSC embedded in an HA-hydrogel. We found that the combined treatment upregulated many cartilage matrix- and development-related markers while suppressing many hypertrophic- and bone development-related markers. Tissue level assessments with biochemical assays, immunofluorescence, and histochemical staining confirmed the gene expression data. Further, mechanical property development in the dynamic compression treatment shows promise toward generating functional engineered cartilage through more optimized and longer culture conditions. In summary, this study introduced a novel protocol to differentiate hBM-MSC into stable, cartilage-forming cells.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".