Does surface chemistry affect thrombogenicity of surface modified polymers?
Bibliographic record
Abstract
With some exceptions, surface chemistry had little effect on platelet and leukocyte activation, and cell deposition, by scanning electron microscopy after blood exposure and clotting times among a group of 12 unmodified and plasma modified tubings. All materials activated platelets and leukocytes to detectable levels, although some materials increased the value of one activation parameter but not another. Unmodified materials [polyethylene (PE), Pellethane (PEU), latex, nylon, and Silastic] and modified materials (H(2)O plasma treated PE and PEU, CF(4) plasma treated PE, fluorinated PEU, NH(4) plasma treated PEU, polyethylene imine treated PEU, and heparin treated PEU) were characterised by XPS and contact angle. The objective of this project was to define a series of assays for the evaluation of hemocompatibility of cardiovascular devices with a view to clarify the specific requirements of ISO-10993-4, and to define an appropriate screening program for new blood contacting biomaterials. PE, PE--CF(4), PE--H(2)0, PEU--F, latex, and PEU-heparin were the exceptions to the general observations, although each behaved differently. PE proved to be least reactive, whereas PE-CF(4) was most reactive by several assays. Platelet microparticle formation (determined by flow cytometry), PTT, postblood exposure SEM, total SC5b-9, C3a, and platelet and leukocyte loss (cell counts) were able to distinguish differences among these materials, and often, but not always, showed expected correlations.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".