Abstract 5372: In Vivo Myocardial Distribution of Adult Stem Cells Immediately Following Intracoronary Delivery in a Swine Model of Myocardial Infarction
Bibliographic record
Abstract
Background: Cardiac cell-based therapy (CCT) has emerged as a promising therapeutic strategy. There are few data comparing the fate of different stem cell (SC) populations delivered by the intracoronary injection (ICI). Aim: Document the in vivo myocardial distribution of SC delivered by ICI following recent myocardial infarction. Methods: In a myocardial infarction swine model, near-infrared (NIR) fluorescence was used to provide high sensitivity in vivo tracking of SC in the immediate phase (< 60 minutes) after ICI of 2x107 cells. After labeling with the NIR fluorophore, IR-786, the NIR signal intensities and myocardial distribution patterns of mesenchymal (MSC), bone marrow-(BMMNC) and peripheral blood-mononuclear (PBMNC) cells were compared using an open-chested fluorescence imaging system. Results: The SC populations of interest were successfully loaded with IR-786. While all SC populations readily distributed along the vascular territory of the infarct-related artery immediately after injection, there was a mean SI intensity drop of 29.8% and 14.1% for PBMNC and BMNC (p<0.05), respectively, which correlated with poor cell retention. No significant SI change was found in MSC-injected swine, which was associated with less cell loss after delivery. There was also evidence of MSC-related vessel plugging in some swine (Figure ). Conclusion: Our in vivo findings suggest that immediately after injection (during the first 60min ICI), there is already evidence of poor SC retention and distribution vary depending on cell population, potentially impacting clinical efficacy and safety of CCT.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".