The role of miR‐301a and the influence of extracellular environment surface tensions on the dichotomous phenotype shown in primary human bone marrow‐derived mesenchymal stem cells (868.4)
Bibliographic record
Abstract
It has been established that the microenvironment of mesenchymal stem cells (MSCs) is influential in determining differentiation. Expression of non‐coding RNAs, such as microRNAs, has been found to be altered in response to various stimuli and may thus contribute to stem cell differentiation. Previous research in our lab has shown that miR‐301a mediates a dichotomous phenotype in primary human MSCs. As MSCs have been found to directly contribute to cardiac remodeling post‐MI, we sought to understand how migration from bone marrow to cardiac tissue could influence MSC differentiation. The objective of this study is to analyze how varying matrix stiffness would influence miR‐301a expression and MSC differentiation. We analyzed mRNA levels of c‐kit, Dicer, MYH9 and 10 and found that these have increased expression on fibronectin‐coated 2kPa plates simulating native bone marrow stiffness, however this is blunted on 15kPa plates, simulating left ventricle myocardium stiffness. When over‐expressing miR‐301a, we found that there is a significant decrease in c‐kit, Dicer, MYH9 and 10. Interestingly, after quantifying miR‐301a expression on these coated plates we found that there is a substantial increase on 15kPa fibronectin‐coated plates. These observations indicate that matrix stiffness may be influencing MSC differentiation via miR‐301a.
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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".