Abstract 12: The Effect of Extracellular Matrix Stiffness on Human Bone Marrow-Derived Mesenchymal Stem Cell Differentiation
Bibliographic record
Abstract
Differentiation of human mesenchymal stem cells (hMSCs) has been shown to be influenced by the surrounding microenvironment. It is important to understand the physiological implications of the hMSC microenvironment regarding differentiation within the body, especially in patients with cardiovascular disease. We are interested in understanding the influence of differing extracellular matrix (ECM) stiffness found in the body that the MSC encounters during its journey from the bone marrow to the infarct scar in patients recovering from a myocardial infarction. As hMSCs must respond rapidly to their environment, we also investigated the influence of microRNA at different surface tensions. HMSCs were isolated from the bone marrow of patients undergoing open heart surgery and cultured in standard DMEM/F12 with 20% FBS. We plated these cells on fibronectin-coated plates with surface tensions of 2kPa, simulating bone marrow; 15kPa, simulating left ventricle; and 100kPa, simulating a fibrotic environment. Protein and mRNA were collected for further analysis. Our data have revealed that softer surface tensions, representing a bone marrow-undifferentiating environment, cause a decrease in the protein expression of EDA-fibronectin and alpha-smooth muscle actin. There were also increases in mRNA of myosin heavy chain-9 and 10, and collagen-1. Softer surface tensions also show a slight increase in miR-301a, although at 15kPa, miR-301a expression is increased even further. Our lab has previously shown that miR-301a is involved in maintaining a proliferative phenotype of hMSCs. Interestingly, Dicer1, responsible for processing microRNAs, is upregulated at softer surface tensions and attenuated at 15kPa. Dicer1 mRNA expression is attenuated at 15kPa. These results indicate that ECM stiffness influences hMSC differentiation and the increase in Dicer1 found with softer matrices could represent an umbrella miRNA inhibition effect to effectively suppress hMSC differentiation.
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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) | 0.007 | 0.001 |
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".