Fibronectin adsorption on surface-modified polyetherurethanes and their differentiated effect on specific blood elements related to inflammatory and clotting processes
Bibliographic record
Abstract
After the introduction of a medical device into the body, adhesive proteins such as fibronectin (Fn) will adsorb to the surface of the biomaterial. Monocytes (MCs) will interact with these adsorbed proteins, and adopt either a proinflammatory and/or prowound healing phenotype, thereby influencing many blood interaction events including thrombogenesis. In this work, Fn adsorption as well as subsequent MC response and thrombus formation were investigated on two surfaces-modified polyetherurethanes (PEUs) using different surface modifiers: an anionic/dihydroxyl oligomeric (ADO) additive, known to enable cell adhesion, and a fluorinated polypropylene oxide oligomer (PPO), known to reduce platelet adhesion. Results indicated that at 24 h of MC culture, PEU-ADO and PEU-PPO promoted an anti-inflammatory character relative to the base PEU. Longer clotting times, based on a free hemoglobin assay, were also found on the two surface-modified PEUs relative to the native one, suggesting their potential for the reduction of thrombus formation. In presence of a Fn monolayer, the surface-modified PEUs conserved a lower thrombogenic character than the base PEU, and was however significantly decreased when compared to prior protein adsorption. Furthermore, Fn coatings increased the MC production levels of tumor necrosis factor-α and interleukin-10 at 24 h, while not affecting the anti-inflammatory effect of the modifications relative to the base PEU. This finding was most prominent on PEU-PPO, suggesting that the interaction of the adsorbed Fn with blood cells was different for the two additives. Hence, the results highlighted differentiating effects of Fn adsorption on specific blood activating processes related to inflammatory and thrombotic responses.
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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 | 0.000 | 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) | 0.000 | 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".