Does surface chemistry affect thrombogenicity of surface modified polymers?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.010 | 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 it