Soft Hydrogel Environments that Facilitate Cell Spreading and Aggregation Preferentially Support Chondrogenesis of Adult Stem Cells
Bibliographic record
Abstract
Mesenchymal stem/stromal cells (MSCs) represent a promising cell type for treating damaged synovial joints. The therapeutic potential of MSCs will be facilitated by the engineering of biomaterial environments capable of directing their fate. Here the interplay between matrix elasticity and cell morphology in regulating the chondrogenic differentiation of MSCs when seeded onto or encapsulated within hydrogels made of interpenetrating networks (IPN) of alginate and collagen type I is explored. This IPN system enables the independent control of substrate stiffness (in 2D and in 3D) and cell morphology (3D only). The expression of chondrogenic markers SOX9, ACAN, and COL2 increases when MSCs are cultured onto the soft substrate, which correlates with increased SMAD2/3 nuclear localization, enhanced MSCs condensation, and the formation of larger cellular aggregates. The encapsulation of spread MSCs within a soft IPN increases the expression of cartilage-specific genes, which is linked to cellular condensation and nuclear SMAD2/3 localization. Surprisingly, cells forced to adopt a more rounded morphology within the same soft IPNs expressed higher levels of the osteogenic markers RUNX2 and COL1. The insight provided by this study suggests that a mechanobiology informed approach to biomaterial development will be integral to the development of successful cartilage tissue engineering strategies.
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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".