Rosuvastatin-loaded injectable immunomodulatory hydrogel mitigates local immune response against transplanted stem cells and promotes heart repair in vivo
Bibliographic record
Abstract
In clinical trials allogeneic mesenchymal stem cells (MSCs) from young and healthy donors have shown promise to repair the heart following a heart attack. However, immune rejection of transplanted MSCs has prevented the clinical translation of stem cells-based therapies for cardiac patients. Therefore, strategies to improve survival of implanted stem cells in the heart would be of immense therapeutic value. This study presents the development of a novel immunomodulatory chitosan-rosuvastatin (CR) hydrogel loaded with MSCs for cardiac repair. The hydrogel showed excellent 3 dimensional (3D) structure and porosity, and was found to support the growth of MSCs. In an in vivo rat model of myocardial infarction (MI), the immunomodulatory CR hydrogel provided physical bulk, improved the retention of MSCs and cardiac function at 4 weeks after MI. The RNA sequencing data demonstrate that rosuvastatin improved the “stemness” of MSCs and reduced the activation of T-cells, downregulated T H1 polarization in response to inflammatory stress in the infarcted heart. Therefore, the current study presents a new paradigm in improving clinical effectiveness of stem cell therapy for cardiac repair by modulating local immune response in the heart against transplanted stem cells using a novel immunomodulatory CR hydrogel.
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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".