The effect of shear on in vitro platelet and leukocyte material-induced activation
Bibliographic record
Abstract
The failure to understand the mechanisms of biomaterial-associated thrombosis prevents us from improving the blood compatibility of stents and mechanical heart valves. Blood-material interactions trigger a complex series of events and anticoagulant and anti-platelet therapies are needed to reduce the risks of thrombotic complications with most cardiovascular materials. While material interaction with platelets has been widely studied, little is currently known on material-induced leukocyte activation in the presence of shear. In vitro experiments were performed to assess the effect of flow on blood cell activation induced by medical grade metals, ST316L and TiAl6V4. Blood was circulated in flow chambers preloaded with or without metal wires at shear rates of 100, 500, and 1500 s⁻¹. Platelet and leukocyte activation, leukocyte-platelet aggregation, and tissue factor expression on monocytes were measured by flow cytometry. Metal surfaces were characterized by scanning electron microscopy. Under physiological shear rates, no significant platelet microparticle formation was observed. However, significant CD11b up-regulation, leukocyte-platelet aggregates, and tissue factor expression were observed at 100 s⁻¹. As shear rate increased to 1500 s⁻¹, leukocyte activation reduced to control values. TiAl6V4-induced leukocyte activation was generally lower than that of ST316L. Adhesion significantly decreased with increasing shear rate to 1500 s⁻¹. In blood, increase within physiological shear rates led to a significant reduction in in vitro material-induced leukocyte activation, suggesting that difference between material biocompatibility may be better identified at low shear rates or under pathological shear conditions.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 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".