Antifouling Polymer Brushes Displaying Antithrombogenic Surface Properties
Bibliographic record
Abstract
The contact of blood with artificial materials generally leads to immediate protein adsorption (fouling), which mediates subsequent biological processes such as platelet adhesion and activation leading to thrombosis. Recent progress in the preparation of surfaces able to prevent protein fouling offers a potential avenue to mitigate this undesirable effect. In the present contribution, we have prepared several types of state-of-the-art antifouling polymer brushes on polycarbonate plastic substrate, and investigated their ability to prevent platelet adhesion and thrombus formation under dynamic flow conditions using human blood. Moreover, we compared the ability of such brushes--grafted on quartz via an adlayer analogous to that used on polycarbonate--to prevent protein adsorption from human blood plasma, assessed for the first time by means of an ultrahigh frequency acoustic wave sensor. Results show that the prevention of such a phenomenon constitutes one promising route toward enhanced resistance to thrombus formation, and suggest that antifouling polymer brushes could be of service in biomedical applications requiring extensive blood-material surface contact.
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.
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) | 0.001 | 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".