Determination of surface-induced platelet activation by applying time-dependency dissipation factor versus frequency using quartz crystal microbalance with dissipation
Bibliographic record
Abstract
Platelet adhesion and activation rates are frequently used to assess the thrombogenicity of biomaterials, which is a crucial step for the development of blood-contacting devices. Until now, electron and confocal microscopes have been used to investigate platelet activation but they failed to characterize this activation quantitatively and in real time. In order to overcome these limitations, quartz crystal microbalance with dissipation (QCM-D) was employed and an explicit time scale introduced in the dissipation versus frequency plots (Df-t) provided us with quantitative data at different stages of platelet activation. The QCM-D chips were coated with thrombogenic and non-thrombogenic model proteins to develop the methodology, further extended to investigate polymer thrombogenicity. Electron microscopy and immunofluorescence labelling were used to validate the QCM-D data and confirmed the relevance of Df-t plots to discriminate the activation rate among protein-modified surfaces. The responses showed the predominant role of surface hydrophobicity and roughness towards platelet activation and thereby towards polymer thrombogenicity. Modelling experimental data obtained with QCM-D with a Matlab code allowed us to define the rate at which mass change occurs (A/B), to obtain an A/B value for each polymer and correlate this value with polymer thrombogenicity.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".