Improved Autograft Survival of Mesenchymal Stromal Cells by Plasminogen Activator Inhibitor 1 Inhibition
Bibliographic record
Abstract
Mesenchymal stromal cells (MSCs) display robust reparative properties through their ability to limit apoptosis, enhance angiogenesis, and direct positive tissue remodeling. However, low in vivo survival of transplanted cells limits their overall effectiveness and significantly affects their clinical usage. Consequently, identifying strategies to improve cell survival in vivo are a priority. One explanation for their low survival is that MSCs are often transplanted into ischemic tissue, such as infarcted myocardium, where there is poor blood supply and low oxygen tension. Therefore, we examined how MSCs respond to a hypoxic, nutrient-poor stress environment to identify trophic factors that could be manipulated in advance of MSC transplantation. Combining microarray and proteomic screens we identified plasminogen activator inhibitor 1 (PAI-1) as one factor consistently upregulated in our in vitro ischemia-mimicking conditions. Subsequent genetic and chemical manipulation studies define PAI-1 as a negative regulator of MSC survival in vivo. Mechanistically, MSC-derived PAI-1 does not alter MSC survival through a plasmin-dependent mechanism but rather directly impacts on the adhesiveness of MSCs to their surrounding matrices. Thus we can conclude that post-transplantation, PAI-1 negatively impacts MSC survival by promoting anoikis via matrix detachment.
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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".