A Hydrophilic Dental Implant Surface Exhibit Thrombogenic Properties In Vitro
Bibliographic record
Abstract
BACKGROUND: Surface modifications of dental implants have gained attention during several years and the thrombotic response from blood components with these materials has become more important during recent years. PURPOSE: The aims of this study were to evaluate the thrombogenic response of whole blood, in contact with clinically used dental surfaces, Sandblasted Large grit Acid etched titanium (SLA) and Sandblasted Large grit Acid etched, and chemically modified titanium with hydrophilic properties (SLActive). METHODS: An in vitro slide chamber model, furnished with heparin, was used in which whole blood came in contact with slides of the test surfaces. After incubation (60-minute rotation at 22 rpm in a 37°C water bath), blood was mixed with ethylenediaminetetraacetic acid (EDTA) or citrate, further centrifuged at +4°C. Finally, plasma was collected pending analysis. RESULTS: Whole blood in contact with surfaces resulted in significantly higher binding of platelets to the hydrophilic surface, accompanied by a significant increase of contact activation of the coagulation cascade. In addition, the platelet activation showed a similar pattern with a significant elevated release of β-TG from platelet granule. CONCLUSIONS: The conclusion that can be drawn from the results in our study is that the hydrophilic modification seems to augment the thrombogenic properties of titanium with implications for healing into bone of, that is titanium dental implants.
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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) | 0.002 | 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".