Exact relevance of bone marrow cells in the healing process after myocardial infarction: analysis with a murine model of bone marrow cell transplantation.
Bibliographic record
Abstract
BACKGROUND: Cellular cardiomyoplasty has created new possibilities in cardiac regeneration. Several cell types can be used in the procedure, such as skeletal myoblasts and bone marrow cells. Recent publications have suggested that bone marrow cells may be excellent candidates due to their pluripotency, but their actual role in cardiac regeneration is unknown. OBJECTIVE: To evaluate the exact physiological role of bone marrow cells in the healing process after myocardial infarction. METHODS: A mouse bone marrow cell transplantation model was used in which transplanted cells were easily detectable by immunohistochemistry. Chimeric mice were subjected to myocardial infarction by ligation of the left descending coronary artery. After one month, the mice were sacrificed and the scars were analyzed. RESULTS: Transplanted bone marrow cells were detected in the scars and these cells seemed able to transdifferentiate into endothelial cells, but no transdifferentiation into cardiomyocytes occurred. This mechanism of regeneration was dismissed because only 2% of the vessels in the scars were positive for transplanted cells. CONCLUSIONS: Bone marrow cells might be involved in myocardial healing, but this physiological mechanism is insufficient to allow correct regeneration.
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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) | 0.002 | 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".