VCAM-1 expression on dystrophic muscle vessels has a critical role in the recruitment of human blood-derived CD133+ stem cells after intra-arterial transplantation
Bibliographic record
Abstract
Recently our group demonstrated the myogenic capacity of human CD133(+) cells isolated from peripheral blood when delivered in vivo through the arterial circulation into the muscle of dystrophic scid/mdx mice. CD133(+) stem cells express the adhesion molecules CD44, LFA-1, PSGL-1, alpha4-integrins, L-selectin, and chemokine receptor CCR7. Moreover these cells adhere in vitro to VCAM-1 spontaneously and after stimulation with CCL19. Importantly, after muscle exercise, we found that the expression of VCAM-1 is strongly up-regulated in dystrophic muscle vessels, whereas the number of rolling and firmly adhered CD133(+) stem cells significantly increased. Moreover, human dystrophin expression was significantly increased when muscle exercise was performed 24 hours before the intra-arterial injection of human CD133(+) cells. Finally, treatment of exercised dystrophic mice with anti-VCAM-1 antibodies led to a dramatic blockade of CD133(+) stem cell migration into the dystrophic muscle. Our results show for the first time that the expression of VCAM-1 on dystrophic muscle vessels induced by exercise controls muscle homing of human CD133(+) stem cells, opening new perspectives for a potential therapy of muscular dystrophy based on the intra-arterial delivery of CD133(+) stem cells.
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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".