Interplay of matrix stiffness and stress relaxation in directing osteogenic differentiation of mesenchymal stem cells
Bibliographic record
Abstract
-acrylic acid) hydrogels on the osteogenic differentiation of human mesenchymal stem cells (hMSCs). Varying the amount of the crosslinker and the ratio between the monomers enabled the obtainment of hydrogels with controlled mechanical properties, as characterized using unconfined compression and atomic force microscopy (AFM). Subsequently, the surface of the hydrogels was functionalized with a mimetic peptide of the BMP-2 protein, in order to favor the osteogenic differentiation of hMSCs. Finally, hMSCs were cultured on the hydrogels with different stiffness and stress relaxation: 15 kPa - 15%, 60 kPa - 15%, 140 kPa - 15%, 100 kPa - 30%, and 140 kPa - 70%. The cells on hydrogels with stiffnesses from 60 kPa to 140 kPa presented a star-like shape, typical of osteocytes, which has only been reported by our group for two-dimensional substrates. Then, the extent of hMSC differentiation was evaluated by using immunofluorescence and by quantifying the expression of both osteoblast markers (Runx-2 and osteopontin) and osteocyte markers (E11, DMP1, and sclerostin). It was found that a stiffness of 60 kPa led to a higher expression of osteocyte markers as compared to stiffnesses of 15 and 140 kPa. Finally, the strongest expression of osteoblast and osteocyte differentiation markers was observed for the hydrogel with a high relaxation of 70% and a stiffness of 140 kPa.
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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".