Abstract 224: Primary Human Cells Isolated From Atria and Bone Marrow Exhibit Comparable Anti-fibrotic Cell Phenotypes Upon Transfection With MicroRNA-301a
Bibliographic record
Abstract
There are many cell types that can contribute to cardiac fibrosis including atrial fibroblasts (AFs) and bone marrow-derived progenitor cells (MPCs). We have previously shown that MPCs display a myofibroblast phenotype in vitro which is linked to altered microRNA(miR)-301a expression, a miR affiliated with maintaining proliferation in numerous cell types. We have also shown that miR-301a influences a dichotomous phenotype in primary human MPCs isolated from patients undergoing open heart surgery. As both MPCs and AFs display a dichotomous phenotype where each cell type displays a phenotype that pathologically contributes to fibrosis, we transfected both MPCs and AFs with miR-301a. AFs were also isolated from patients undergoing open heart surgery. We observed decreases in levels of both mRNA and protein of collagen I, non-muscle myosin IIA, and EDA-fibronectin. These proteins are expressed in myofibroblasts, the cell type predominantly responsible for causing cardiac fibrosis. In addition, transfection of miR301a caused both cell types to increase proliferation, which was analyzed using MTT proliferation assays. These results indicate that miR-301a could be influencing a non-fibrotic phenotype, which could prove useful in cell therapy trials where progenitor cells are injected into scar tissue in order to help heal patients who have suffered from a myocardial infarction. Over-expressing miR-301a in cells used could prevent them from differentiating into pro-fibrotic phenotypes and encourage their proliferation, thereby potentiating their efficacy.
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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) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.004 |
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".