Human Mesenchymal Stromal Cells Improve Cardiac Perfusion in an Ovine Immunocompetent Animal Model
Bibliographic record
Abstract
BACKGROUND: Mesenchymal stromal cells (MSCs) hold considerable promise in the treatment of ischemic heart disease. Most preclinical studies of MSCs for acute myocardial infarction (AMI) have been performed either in syngeneic animal models or with human cells in xenogeneic immunodeficient animals. A preferable pre-clinical model, however, would involve human MSCs in an immunocompetent animal. METHODS: AMI was generated in adult sheep by inducing ischemia reperfusion of the second diagonal branch. Sheep (n = 10) were randomized to receive an intravenous injection of human MSCs (1 × 10(6) cells/kg) or phosphate buffered saline. Cardiac function and remodeling were evaluated with echocardiography. Perfusion scintigraphy was used to identify sustained myocardial ischemia. Interaction between human MSCs and ovine lymphocytes was assessed by a mixed lymphocyte response (MLR). RESULTS: Sheep receiving human MSCs showed significant improvement in myocardial perfusion at 1 month compared with baseline measurements. There was no change in ventricular dimensions in either group after 1 month of AMI. No adverse events or symptoms were observed in the sheep receiving human MSCs. The MLR was negative. CONCLUSION: The immunocompetent ovine AMI model demonstrates the clinical safety and efficacy of human MSCs. The human cells do not appear to be immunogenic, further suggesting that immunocompetent sheep may serve as a suitable pre-clinical large animal model for testing human MSCs.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.003 | 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".