Tracking Stem Cell Therapy in the Myocardium: Applications of Positron Emission Tomography
Bibliographic record
Abstract
The introduction of stem cells and/or progenitor cells into damaged myocardium has promising therapeutic potential in ischemic heart diseases and dilated cardiomyopathy. However, understanding the biologic mechanisms and the outcomes of transplanted cells during cardiac regenerative therapy remains mostly limited to histological assessment. Positron emission tomography (PET) is a sensitive molecular imaging modality that can non-invasively assess stem cell retention, survival, and function after transplantation. Two radiolabel approaches have been explored to implement PET: 1) direct cell labeling with a radionuclide; and 2) reporter gene-based cell labeling. Direct cell labeling has previously been used for early tracking of transplanted stem cells into the myocardium in several therapeutic clinical trials. Stem cells can also be labeled after transfection with a reporter gene, which can subsequently be visualized by using a PET reporter probe that binds to the reporter gene, therefore allowing serial in vivo evaluation of cell viability and proliferation in long-term follow-up studies. Recently, some studies successfully used this method to visualize implanted stem cells by PET imaging in animals. With the projected rapid growth of cell therapy for heart disease, PET is expected to play a major role in monitoring relevant changes that occur at every stage in cardiac regenerative therapy. These two cell tracking approaches used for PET imaging are reviewed here and compared against other imaging modalities.
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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) | 0.002 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".