Isolation and Characterization of Human Bone-Derived Endothelial Cells
Bibliographic record
Abstract
Historically, the etiology of local bone pathologies, such as avascular necrosis, has been related to intravascular occlusion. Recent reports have highlighted the occlusion of arteries, venules, and/or capillaries in bone tissue. Endothelium of bone presumably participates locally in the formation of the microvascular thrombosis. It is also known that endothelial cells (ECs) play a central role in angiogenesis, a process seen in osteosarcoma, amongst other bone diseases. Given the well-recognized heterogeneity of ECs throughout the body, investigations of local bone disease related to endothelium processes may be more appropriately targeted on bone ECs rather than other primary ECs or an immortalized EC line. In the current study, mechanical and enzymatic methods are described to isolate ECs from cancellous human bone tissue followed by immunomagnetic bead separation to purify the cell populations. The human bone-derived endothelial cells (hBDECs) were characterized based on endothelial cell antigen expression and functional assays. This study is the first report of isolation and expansion of ECs from human bone tissue. Isolation of hBDECs in human vascular bone diseases may facilitate the study of the molecular and/or genetic abnormalities in the vasculature system that contributes to the initiation and/or progression of the disease.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".