An assessment of the response of human MSCs to hydrostatic pressure in environments supportive of differential chondrogenesis
Bibliographic record
Abstract
Mechanical stimulation can modulate the chondrogenic differentiation of stem/progenitor cells and potentially benefit tissue engineering (TE) of functional articular cartilage (AC). Mechanical cues like hydrostatic pressure (HP) are often applied to cell-laden scaffolds, with little optimization of other key parameters (e.g. cell density, biomaterial properties) known to effect lineage commitment. In this study, we first sought to establish cell seeding densities and fibrin concentrations supportive of robust chondrogenesis of human mesenchymal stem cells (hMSCs). High cell densities (15*106 cells/ml) were more supportive of sGAG deposition on a per cell basis, while collagen deposition was higher at lower seeding densities (5*106 cells/ml). Employment of lower fibrin (2.5 %) concentration hydrogels supported more robust chondrogenesis of hMSCs, with higher collagen type II and lower collagen type X deposition compared to 5 % hydrogels. The application of HP to hMSCs maintained in identified chondro-inductive culture conditions had little effect on overall levels of cartilage-specific matrix production. However, if hMSCs were first temporally primed with TGF-β3 before its withdrawal, they responded to HP by increased sGAG production. The response to HP in higher cell density cultures was also associated with a metabolic shift towards glycolysis, which has been linked with a mature chondrocyte-like phenotype. These results suggest that mechanical stimulation may not be necessary to engineer functional AC grafts using hMSCs if other culture conditions have been optimised. However, such bioreactor systems can potentially be employed to better understand how engineered tissues respond to mechanical loading in vivo once removed from in vitro culture environments.
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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".