A dynamic matrix potentiates mesenchymal stromal cell paracrine function <i>via</i> an effective mechanical dose
Bibliographic record
Abstract
The paracrine function of mesenchymal stromal cells (MSCs) contributes a lot to tissue development, and it is regulated by various physical factors. Moreover, the extracellular matrix (ECM) of MSCs is dynamic, and its remodeling is always occurring. In particular, stiffness changes are prevalent. Accordingly, ECM stiffness changes may affect the paracrine function of MSCs, which has not been investigated much. In this study, for the first time, alginate hydrogels with different stiffening times were used to assess the influence of dynamic ECM stiffness changes on the paracrine function of MSCs. It was found that a stiffer matrix (14.72 ± 1.44 kPa) under static conditions without any additional treatment could significantly potentiate the paracrine function of MSCs compared to a soft matrix (2.44 ± 0.23 kPa). Furthermore, this promotion was regulated by the activation of Yes-associated protein (YAP), which was caused by the polymerization of F-actin. Intriguingly, in a dynamic system, the MSC-encapsulating matrix that stiffened on the third day had stronger YAP activation than the Static-Stiff matrix. However, this activation was weakened when MSCs were cultured in a matrix that stiffened on the fifth day. The results show that an increase in ECM mechanical dosing levels would promote the paracrine function of MSCs. Moreover, an effective mechanical dose that can influence the paracrine function of MSCs indeed exists.
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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) | 0.001 | 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) | 0.002 | 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".