Human CD34 <sup>+</sup> Cells in Experimental Myocardial Infarction
Bibliographic record
Abstract
RATIONALE: Human CD34(+) cells have been used in clinical trials for treatment of myocardial infarction (MI). However, it is unknown how long the CD34(+) cells persist in hearts, whether the improvement in cardiac function is sustained, or what are the underlying mechanisms. OBJECTIVE: We sought to track the fate of injected human CD34(+) cells in the hearts of severe combined immune deficiency (SCID) mice after experimental MI and to determine the mechanisms of action. METHODS AND RESULTS: We used multimodality molecular imaging to track the fate of injected human CD34(+) cells in the hearts of SCID mice after experimental MI, and used selective antibody blocking to determine the mechanisms of action. Bioluminescence imaging showed that injected CD34(+) cells survived in the hearts for longer than 12 months. The PET signal from the injected cells was detected in the wall of the left ventricle. Cardiac MRI showed that left ventricular ejection fraction was significantly improved in the treated mice compared to the control mice for up to 52 weeks (P<0.05). Furthermore, treatment with anti-alpha4beta1 showed that generation of human-derived cardiomyocytes was inhibited, whereas anti-vascular endothelial growth factor (VEGF) treatment blocked the production of human-derived endothelial cells. However, the improvement in cardiac function was abolished only in the anti-VEGF, but not anti-alpha4beta1, treated group. CONCLUSIONS: Angiogenesis and/or paracrine effect, but not myogenesis, is responsible for functional improvement following CD34(+) cells therapy.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".