Positron emission tomography quantification of stem cells in cardiovascular disease
Bibliographic record
Abstract
Stem cell therapy is emerging as a possible method for treating many diseases and disorders, such as cardiovascular disease. In particular, stem cells may be able to revive the dead tissue caused by acute myocardial infarction (heart attack). Adipose-derived stem cells were labelled with 18F-fluorodeoxyglucose (FDG) and superparamagnetic iron oxide (SPIO) particles, for imaging with positron emission tomography (PET) and magnetic resonance imaging (MRI), respectively, and injected into several rats following induced myocardial infarction. Stem cell retention in the heart was investigated following three injection sites; two within the heart (intramyocardial and left intraventricular), and one easily accessible vein (tail vein). The PET and MR images were registered and the initial distributions analyzed using region of interest (ROI) analysis, to determine which injection method would result in the highest stem cell retention in the infarcted heart. The ROI results determined that the intramyocardial injection had the highest % injected dose (%ID) in the heart with 14 +/- 4%, followed by left intraventricular and tail vein with %IDs of 3.6 +/- 0.8% and 1.2 +/- 0.6%, respectively. The results indicate that stem cell delivery via intramyocardial injection should be utilized for optimal retention in the heart.
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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 | 0.000 |
| 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".