The Effect of Age on the Efficacy of Human Mesenchymal Stem Cell Transplantation after a Myocardial Infarction
Bibliographic record
Abstract
OBJECTIVE: Clinical trials of cardiac cell therapy have indicated limited benefits in aging patients, even though preclinical studies using young animals consistently reported significant improvements. Animal studies have demonstrated reduced efficacy of donor cells isolated from older individuals. Here, we evaluated the effects of donor age on the function of human mesenchymal stem cells (hMSCs) in the context of cell therapy for ischemic cardiomyopathy. METHODS: In vitro, we compared the growth and clonogenic potential of hMSCs isolated from young or old patients (1-5 vs. 50-70 years old). In vivo, we injected young or old hMSCs (2.0 x 10(6)) (or medium) into the infarcted myocardia of immunosuppressed rats immediately after coronary artery ligation (myocardial infarction [MI]). We assessed cardiac function (echocardiography) at 1, 2, and 4 weeks after MI, and myocardial matrix metalloproteinase-2 (MMP-2), MMP-9, and tissue inhibitor of matrix metalloproteinase-3 (TIMP-3) levels at 1 week. RESULTS: In vitro, growth and colony-forming unit fibroblast (CFU-F) formation were markedly diminished in old hMSCs (p < 0.001 and p < 0.05, respectively, vs. young). In vivo, compared with old hMSCs or medium, young hMSCs best preserved ejection fraction, fractional shortening (p < 0.05), and left ventricular end-diastolic and end-systolic volumes (p < 0.01). Recipients of young hMSCs also exhibited increases in vascular density and TIMP-3 protein levels and activity (p < 0.05), and decreases in MMP protein levels and activity (p < 0.05). CONCLUSIONS: The regenerative capacity of hMSCs was significantly influenced by age. Transplanting young hMSCs improved functional outcomes after an MI by preventing matrix degradation and promoting angiogenesis. The clinical implication is that aged patients require an optimized source of stem cells for treatment.
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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) | 0.000 | 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".