Osteocyte apoptosis is mechanically regulated and induces angiogenesis in vitro
Bibliographic record
Abstract
Osteocyte apoptosis, associated with reduced interstitial fluid flow, precedes osteoclast precursor recruitment and may aid in the delivery of osteoclast precursors to the remodeling site by promoting angiogenesis. To test the association between fluid flow and osteocyte apoptosis, osteocyte-like MLO-Y4 cells were subjected to either oscillatory fluid flow (10 dynes/cm², 1 Hz) or no flow conditions with or without TNF-α treatment to induce osteocyte apoptosis chemically. Flow protected osteocytes from apoptosis regardless of whether they were treated with TNF-α (p < 0.001) or not (p < 0.05). TNF-α-induced apoptotic and nonapoptotic osteocyte conditioned media were used to study the effect of osteocyte apoptosis on angiogenesis. Apoptotic osteocyte conditioned media caused more endothelial cell proliferation (p < 0.05) and migration (p < 0.05), and tubule networks with longer (p < 0.01) and more (p < 0.001) branches than nonapoptotic osteocyte conditioned media. Apoptotic osteocyte conditioned media contained more vascular endothelial growth factor (VEGF) than nonapoptotic osteocyte conditioned media (p < 0.05). VEGF concentrations found in apoptotic osteocyte conditioned media formed endothelial tubule networks with longer (p < 0.05) and more (p < 0.02) branches than VEGF concentrations in nonapoptotic osteocyte conditioned media. Blocking VEGF in apoptotic osteocyte conditioned media abolished tubule formation effects (p < 0.001). Our results suggest that osteocyte apoptosis is flow-regulated and promotes angiogenesis in a VEGF-mediated manner.
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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".