Inhibitory Effect of Hydrophilic Polymer Brushes on Surface‐Induced Platelet Activation and Adhesion
Bibliographic record
Abstract
Poly(N,N-dimethylacrylamide) (PDMA) brushes are successfully grown from unplasticized poly(vinyl chloride) (uPVC) by well-controlled surface-initiated atom transfer radical polymerization (SI-ATRP). Molecular weights of the grafted PDMA brushes vary from ≈ 35,000 to 2,170000 Da, while the graft density ranges from 0.08 to 1.13 chains · nm(-2). The polydispersity of the grafted PDMA brushes is controlled within 1.20 to 1.80. Platelet activation (expression of CD62) and adhesion studies reveal that the graft densities of the PDMA brushes play an important role in controlling interfacial properties. PDMA brushes with graft densities between 0.35 and 0.50 chains · nm(-2) induce a significantly reduced platelet activation compared to unmodified uPVC. Moreover, the surface adhesion of platelets on uPVC is significantly reduced by the densely grafted PDMA brushes. PDMA brushes that have high molecular weights lead to a relatively lower platelet activation compared to low-molecular-weight brushes. However, the graft density of the brush is more important than molecular weight in controlling platelet interactions with PVC. PDMA brushes do not produce any significant platelet consumption in platelet rich plasma. Up to a seven-fold decrease in the number of platelets adhered on high graft density brushes is observed compared to the bare PVC surface. Unlike the bare PVC, platelets do not form pseudopodes or change morphology on PDMA brush-coated surfaces.
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".