Bibliographic record
Abstract
Scar formation following an ischemic insult to the heart is referred to as reparative fibrosis and represents an essential physiological response to heal the damaged myocardium. The biological events of reparative fibrosis include inflammation, the deposition of collagen by myofibroblasts, sympathetic innervation, and angiogenesis. Several studies have further reported that scar formation was associated with the recruitment of neural crest-derived cardiac resident nestin(+) cells that display characteristics consistent with a neural progenitor/stem cell phenotype. During the reparative fibrotic response, these nestin(+) cells participate in neural remodeling and represent a novel cellular substrate of angiogenesis. In addition, a subpopulation of nestin(+) cells identified in the normal heart expressed cardiac progenitor transcriptional factors and may directly contribute to myocardial regeneration following ischemic damage. Nestin protein was also detected in endothelial cells of newly formed blood vessels in the scar and may represent a marker of revascularization. Lastly, nestin was induced in a subpopulation of smooth muscle α-actin(+) scar-derived myofibroblasts, and the expression of the intermediate filament protein may provide a proliferative advantage. Collectively, these data demonstrate that diverse populations of nestin(+) cells participate in cardiac wound healing.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".