Surface topography modulates initial platelet adhesion to titanium substrata
Bibliographic record
Abstract
The clinical success of implanted biomaterials such as dental implants is largely determined by the molecular signaling that occurs at the tissue-implant interface. The modification of surface topography is a widely-employed strategy for optimizing tissue integration with dental implants. However, little is known regarding the direct, cellular-level effects of substratum topography on platelet signaling and adhesion, despite these cells being the first to encounter the implant surface during surgical placement. Here we compared platelet adhesion and secretion on four (4) different titanium surfaces, notably, the modifications applied to commercially available dental implants: smooth (S) titanium; acid-etched (AE), sandblasted (SB) and a combined acid-etching/sandblasting procedure (SLA). Platelets were isolated from human blood, washed, and seeded on to the 4 test surfaces; platelet adhesion was quantified by microscopy. In addition, the secretion of critical molecules stored in platelet granules (platelet factor 4, PF4; soluble P-selectin, sCD62P; transforming growth factor-beta1, TGF-β1; platelet-derived growth factor-AB, PDGF-AB) was measured by enzyme-linked immunosorbent assay (ELISA) analysis of the supernatants. There was greater platelet adhesion to the rougher AE and SB surfaces, however, the concentration of the secreted growth factors was comparable on all surfaces. We conclude that while surface topography can be engineered to modulate initial platelet adhesion, granule secretion is likely regulated as a separate and independent process.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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) | 0.000 | 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 teacher head, 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".