Synthesis of Glycocalyx-Mimetic Surfaces and Their Specific and Nonspecific Interactions with Proteins and Blood
Bibliographic record
Abstract
Glycocalyx mimicking glycopolymer brushes presenting mannose, galactose and glucose residues in the pyranose form, were synthesized on planar substrates (Si wafer, gold chip) and monodispersed polystyrene (PS) particles to generate bioactive surfaces. We investigated the specific protein binding interactions of the surfaces with carbohydrate binding proteins as well as their non-specific protein interactions in blood plasma. Surface Plasmon Resonance (SPR) analysis showed that the glycopolymer brush presenting mannose residues showed specific multivalent interaction with lectin (Concanavalin A (Con A)). The grafting density has little influence on the binding mode, which indicated steric interference arising from the inter chain interaction has little influence on the binding. The glycopolymer brushes performed better against non-specific single protein adsorption than conventional polymer containing hydroxyl groups. There was some influence on the type of carbohydrates residues present as well the type of anticoagulant used for blood collection. SPR analysis showed that the total protein adsorption from plasma was greatly reduced, as low as 24.3 ng/cm 2 from undiluted plasma on the glucose carrying brush. All the glycopolymer brushes showed similar levels of platelet activation, however, the platelet adhesion on the surface was dependent on the type of carbohydrate residues present. Blood coagulation on the surfaces was not greatly influenced by the carbohydrate structure suggesting that surfaces may be non-thrombogenic. Our data demonstrate that the structure and presentation of carbohydrate residues are important factors in the design of carbohydrate arrays and synthetic blood contacting surfaces based on glycopolymers as these parameters are influencing the specific and non-specific interaction with proteins.
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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".