Cytokine-Induced Mobilization of Circulating Endothelial Progenitor Cells Enhances Repair of Injured Arteries
Bibliographic record
Abstract
BACKGROUND: The existence of circulating endothelial progenitor cells (CEPCs) has previously been documented. These cells can be mobilized by cytokines and are recruited to sites of injury, where they may participate in tissue repair. In the present study, we examined the hypothesis that mobilization of CEPCs by exogenous granulocyte-colony stimulating factor (G-CSF) enhances repair of injured arteries by facilitating reendothelialization and inhibiting neointima development. METHODS AND RESULTS: Male rats were injected daily with 50 microg/kg recombinant human G-CSF or 0.9% NaCl SC for 8 days. On the fifth day of treatment, 1 mL of blood was collected for fluorescence-activated cell sorting analysis of mononuclear cells, and the animals underwent balloon angioplasty of the common carotid artery. The animals were killed at 2 or 4 weeks after injury, and the carotid arteries were harvested and processed for immunohistochemistry, scanning electron microscopy (SEM), and morphometric analysis of endothelialization and neointimal formation. G-CSF increased the number of circulating mononuclear cells that express endothelial cell lineage markers several-fold. SEM and immunohistochemical staining with the endothelial marker, platelet and endothelial cell adhesion molecule-1, showed rapid and nearly complete (>90%) reendothelialization of the denuded vessels in the G-CSF-treated animals compared with <20% in the control animals. Reendothelialization was paralleled by a decrease in inflammation in the vessel wall. Neointima thickness was reduced by approximately 60% in the G-CSF-treated animals compared with control animals at 2 and 4 weeks after injury. CONCLUSIONS: We postulate that cytokine-induced mobilization of CEPCs may be a suitable therapeutic strategy for prevention of restenosis after revascularization procedures.
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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".