Transplantation of Monocytes: A Novel Strategy for <i>In Vivo</i> Augmentation of Collateral Vessel Growth
Bibliographic record
Abstract
Therapeutic augmentation of collateral vessel growth (arteriogenesis) is of particular clinical interest. Because monocytes localize to areas of collateral growth and create a highly arteriogenic environment through secretion of multiple growth factors, we tested the hypothesis that monocyte "homing" can therapeutically be exploited. We have used a rabbit model of arteriogenesis to investigate the therapeutic potential of transplanted rabbit monocytes that were either ex vivo stimulated or adenovirally transduced to express a transgene encoding an arteriogenic growth factor. The monocytes were intravenously injected 24 hr or 7 days after ligation of the animal's right femoral artery. Seven days after transplantation collateral flow was determined with a doppler flow probe and collateral vessels were quantified angiographically. Whereas transplantation of allogeneic cells (same species) resulted in a strong promotion of arteriogenesis, most likely through induction of local inflammation and recruitment of recipient monocytes, transplantation of autologous cells (same animal) was not able to significantly augment collateralization. However, when autologous monocytes were used as vehicles to deliver granulocyte macrophage-colony stimulating factor as therapeutic transgene, collateralization was strongly augmented. Their localization to the site of collateral development posttransplantation was demonstrated by ex vivo transduction with beta-galactosidase. Because isolation of monocytes is clinically widely available their ex vivo engineering and transplantation represents an intriguing new strategy for therapeutic arteriogenesis.
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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 | 0.001 |
| 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".