Stem cell implantation in ischemic mouse heart: a high‐resolution magnetic resonance imaging investigation
Bibliographic record
Abstract
Advances in the biology of stem cells have evoked great interest in cell replacement therapies for the regeneration of heart tissue after myocardial infarction. However, results from human trials are controversial, since the destination of the injected cells, their engraftment and their long-term fate have remained unclear. Here we investigate whether transplanted cells can be identified in the intact and lesioned murine myocardium employing high-resolution MRI. Cardiac progenitor cells, expressing the enhanced green fluorescent protein (EGFP), were labeled with ultra-small paramagnetic iron-oxide (USPIO) nanoparticles and transplanted into the intact or injured myocardium of mice. Their precise location was determined with high-resolution MRI and compared with histological tissue sections, stained with Prussian blue for iron content. These experiments showed that iron nanoparticle-loaded cells could be identified at high resolution in the mouse heart. However, ischemic myocardium (after cryoinjury or left coronary artery ligation) was characterized by a signal attenuation similar to that induced by USPIO-labeled cells in T2*-weighted MR images, making detection of labeled stem cells in this area by T2*-sensitive contrast rather difficult. In animals with myocardial injury only, the signal attenuated areas were of the same size in proton density- and T2*-weighted MR images. In injured animals also receiving labeled cells the lesioned area appeared larger in T2*--than in proton density-weighted MR images. This sequence-dependent lesion size change is due to the increased signal loss caused by the iron oxide nanoparticles, most sensitively detectable in the T2*-sensitive images. Thus, using the novel combination of these two parameter weightings, USPIO-labeled cells can be detected at high resolution in ischemic myocardium.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".