Surface characterization, hemo- and cytocompatibility of segmented poly(dimethylsiloxane)-based polyurethanes
Bibliographic record
Abstract
Segmented polyurethanes based on poly(dimethylsiloxane), currently used for biomedical applications, have sub-optimal biocompatibility which reduces their efficacy. Improving the endothelial cell attachment and blood-contacting properties of PDMS-based copolymers would substantially improve their clinical applications. We have studied the surface properties and in vitro biocompatibility of two series of segmented poly(urethane-dimethylsiloxane)s (SPU-PDMS) based on hydroxypropyl- and hydroxyethoxypropyl- terminated PDMS with potential applications in blood-contacting medical devices. SPU-PDMS copolymers were characterized by contact angle measurements, surface free energy determination (calculated using the van Oss-Chaudhury-Good and Owens-Wendt methods), and atomic force microscopy. The biocompatibility of copolymers was evaluated using an endothelial EA.hy926 cell line by direct contact assay, before and after pre-treatment of copolymers with multicomponent protein mixture, as well as by a competitive blood-protein adsorption assay. The obtained results suggested good blood compatibility of synthesized copolymers. All copolymers exhibited good resistance to fibrinogen adsorption and all favored albumin adsorption. Copolymers based on hydroxyethoxypropyl-PDMS had lower hydrophobicity, higher surface free energy, and better microphase separation in comparison with hydroxypropyl-PDMS-based copolymers, which promoted better endothelial cell attachment and growth on the surface of these polymers as compared to hydroxypropyl-PDMS-based copolymers. The results showed that SPU-PDMS copolymers display good surface properties, depending on the type of soft PDMS segments, which can be tailored for biomedical application requirements such as biomedical devices for short- and long-term uses.
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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) | 0.001 | 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".