Abstract 16757: Immunomodulatory Hydrogel Promotes Survival of Mesenchymal Stem Cells in a Rat Model of Myocardial Infarction
Bibliographic record
Abstract
Introduction: Stem cell (SC) therapy is a potential method of repairing the heart after injury. However, SCs have been shown to have poor survival after transplantation, at least in part, due to a hostile pro-inflammatory ischemic myocardium. Thus, there is significant interest in the identification of strategies that can increase the retention/survival of SCs after transplantation. Here we seek to investigate whether the use of an immunomodulatory hydrogel, containing anti-inflammatory sinapic acid (SA), promotes the survival of SCs after transplantation. Methods: Myocardial infarction was induced in rats followed by the delayed delivery of A) mesenchymal stem cells (MSCs), B) MSCs in a hydrogel without SA or C) MSCs in a hydrogel containing SA (10μM). Delivery of MSCs (7.5 x 10 5 in 30μL) occurred 7 days after MI, representing the subacute stage and to minimize acute inflammation. Survival of MSCs, stably expressing firefly luciferase under the constitutive CMV promoter, was followed longitudinally using bioluminescence imaging. Results: Panels A-C provide representative images of the viability of MSCs in different groups, with the quantification shown in panel D. There were no differences in survival when MSCs were delivered alone (A) or in a hydrogel without SA (B). However, the survival of MSCs was significantly increased when they were transplanted in a hydrogel with the anti-inflammatory SA (C). Conclusions: Here we provide evidence of the potential for an immunomodulatory hydrogel to enhance the viability of MSCs after infarction, which may lead to improvement of cardiac function. Furthermore, use of this hydrogel in combination with stem cells could be a potential therapeutic strategy for ameliorating cardiac repair after significant damage.
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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 | 0.001 | 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) | 0.004 | 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".