Bibliographic record
Abstract
Endothelial Progenitor Cells (EPCs) are thought to participate in the healing of injured vascular endothelium by incorporating into the defect sites to mediate endothelial recovery. Recently, osteopontin (OPN) was shown to be fundamental in accelerating estrogen-dependent healing of injured blood vessels. Here, we are investigating the effect OPN has on EPC behavior. Late outgrowth human EPCs (LEPCs) were derived from circulating monocytes isolated by leukophoresis, and grown in culture until passage six. L-EPCs were then assayed for adhesion, spreading, chemotaxis, and haptotaxis, as well as resistance to detachment by flow electric cellsubstrate impedance sensing (ECIS). The results of standard and ECIS methods showed both dose and time dependent responses in cell adhesion and spreading. In addition, OPN promoted haptotactic migration of EPCs in Boyden chamber assays. LEPCs seeded onto 10μM OPN substrates and exposed to laminar flow had grater survival and higher resistance to detachment than OPN/static and flow only conditions. CD44 and !1 integrins were only responsible for approximately 50% of LEPCs adhesion to OPN compared to the unblocked condition. Western blots showed that Rho GTPases were activated in L-EPCs seeded on OPN. However, this activation could not be completely blocked by either CD44 or !1 integrin antagonists. These data confirm the direct effects of OPN on EPCs adhesion, and suggest that OPN works by mediating cell adhesion during vascular injury.
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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".