CD31 <sup>+</sup> Cells Represent Highly Angiogenic and Vasculogenic Cells in Bone Marrow
Bibliographic record
Abstract
RATIONALE: Bone marrow (BM) cells play an important role in physiological and therapeutic neovascularization. However, it remains unclear whether any specific uncultured BM cell populations have higher angiogenic and vasculogenic activities. Moreover, there has been controversy regarding the vasculogenic ability of BM cells. OBJECTIVE: Preliminary flow cytometric analysis showed that CD31, traditionally a marker for endothelial cells, is expressed in certain nonendothelial BM mononuclear cells in both human and mouse. Based on the conserved CD31 expression in the axis of hematopoietic stem/progenitor cells (HSC/HPCs) to endothelial cells, we further sought to determine the comprehensive vasculogenic and angiogenic characteristics of human and mouse BM-derived CD31(+) cells. METHODS AND RESULTS: Flow cytometric analysis demonstrated that all CD31(+) cells derived from BM were CD45(+) and expressed markers for both HSC/HPCs and endothelial cells. Comprehensive gene expression analyses revealed that BM-CD31(+) cells expressed higher levels of angiogenic genes than CD31(-) cells. Endothelial progenitor cells, as well as HSC/HPCs, were almost exclusively confined to the CD31(+) cell fraction, and culture of CD31(+) cells under defined conditions gave rise to endothelial cells. Finally, injection of CD31(+) cells into ischemic hindlimb repaired ischemia, increased expression of angiogenic and chemoattractive factors, and, in part, directly contributed to vasculogenesis, as demonstrated by both 3D confocal microscopy and flow cytometry. CONCLUSIONS: These data indicate that BM-CD31(+) cells represent highly angiogenic and vasculogenic cells and can be a novel and highly promising source of cells for cell therapy to treat ischemic cardiovascular diseases.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".