In vitro thrombogenicity investigation of new water-dispersible polyurethane anionomers bearing carboxylate groups
Bibliographic record
Abstract
New segmented polyurethane (PU) anionomers based on hydroxytelechelic polybutadiene were synthesized via an aqueous dispersion process. Incorporation of carboxylic groups was achieved using thioacids of different length. Surface properties were investigated by mean of water absorption analysis and static contact-angle measurements using water, diiodomethane, formamide and ethylene glycol. Blood compatibility of the PUs was evaluated by in vitro adhesion assays using 111In-radiolabeled platelet-rich plasma and [125I]fibrinogen. Morphology of the adhered platelets was examined by scanning electron microscopy (SEM). Results were compared to two biomedical-grade PUs, namely Pellethane and Tecoflex. Insertion of carboxylic groups increased surface hydrophilicity and limited water uptake ( < 8% for an ion content of 5% by weight). Surface energy of all synthesized PUs was between 40 and 45 mJ/m2. Platelet adhesion and fibrinogen adsorption on the PU anionomer surfaces were affected as a function to the increase of graft length; thiopropionic was the most haemocompatible, followed by thiosuccinic and then thioglycolic acid. SEM analyses of all ionic PU samples exhibited low platelet adhesion to surfaces with no morphological modification. In conclusion, increased hydrophily, dynamic mobility and charge repulsion are synergistic key factors for enhanced haemocompatibility.
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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".