Nestin‐expressing neural stem cells identified in the scar following myocardial infarction
Bibliographic record
Abstract
Nerve fiber innervation of the scar following myocardial damage may have occurred either via the growth of pre-existing fibers and/or the mobilization of neural stem cells. The present study examined whether neural stem cells were recruited to the infarct region of the rat heart following coronary artery ligation. The neural stem cell marker nestin was detected in the infarct region of 1-week post-myocardial infarct (MI) male rats and cultured scar-derived neural-like cells. By contrast, nestin staining was undetected in either scar myofibroblasts or cardiac myocytes residing in the non-infarcted left ventricle. Reactive astrocytes were isolated from the infarct region and characterized by the co-expression of nestin, glial fibrillary acidic protein, and vimentin. Specific staining of oligodendrocytes and neurons was also detected in the infarct region and cultured scar-derived neural-like cells. Furthermore, neurofilament-M positive fibers were identified in the scar and tyrosine hydroxylase immunoreactivity was observed in peripherin-positive neurons. Neurite formation was induced in PC12 cells treated with the conditioned-media of primary passage scar-derived cells, highlighting the synthesis and secretion of neurotrophic factors. Nerve growth factor (NGF) and brain-derived neurotrophic factor were detected in myofibroblasts and neural cells, and both cell types expressed the NGF receptors trkA and p75. These data highlight the novel observation that neural stem cells were recruited to the infarct region of the damaged rat heart and may contribute in part to nerve fiber growth and subsequent innervation of the scar.
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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".