Adipose-derived stem cells are an effective cell candidate for treatment of heart failure: an MR imaging study of rat hearts
Bibliographic record
Abstract
This study assessed the potential therapeutic efficacy of adipose-derived stem cells (ASCs) on infarcted hearts. Myocardial infarction was induced in rat hearts by occlusion of the left anterior descending artery (LAD). One week after LAD occlusion, the rats were divided into three groups and subjected to transplantation of ASCs or transplantation of cell culture medium (CCM) or remained untreated. During a 1-mo recovery period, magnetic resonance imaging showed that the ASC-treated hearts had a significantly greater left ventricular (LV) ejection fraction and LV wall thickening than did the CCM-treated and untreated hearts. The capillary density in infarct border zone was significantly higher in the ASC-treated hearts than in the CCM-treated and untreated hearts. However, only 0.5% of the ASCs recovered from the ASC-treated hearts were stained positive for cardiac-specific fibril proteins. It was also found that ASCs under a normal culture condition secreted three cardiac protective growth factors: vascular endothelial growth factor, hepatocyte growth factor, and insulin-like growth factor-1. Results of this study suggest that ASCs were able to improve cardiac function of infarcted rat hearts. Paracrine effect may be the mechanism underlying the improved cardiac function and increased capillary density.
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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 | 0.000 | 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) | 0.001 | 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".