Anti-Thrombogenicity Study of a Covalently-Attached Monolayer on Stent-Grade Stainless Steel
Bibliographic record
Abstract
Implantable devices fabricated from austenitic type 316L stainless steel have been employed significantly in medicine, principally because the material displays excellent mechanical characteristics and corrosion resistance. It is well known, however, that interaction of exposure of such a material to blood can initiate platelet adhesion and blood coagulation, leading to a harmful medical condition. In order to prevent undesirable surface platelet adhesion on biomaterials employed in procedures such as renal dialysis, we developed an ultrathin anti-thrombogenic covalently attached monolayer based on monoethylene glycol silane chemistry. This functions by forming an interstitial hydration layer which displays restricted mobility in the prevention of surface fouling. In the present work, the promising anti-thrombogenic properties of this film are examined with respect to platelet aggregation on 316L austenitic stainless steel exposed to whole human blood. Prior to exposure with blood, all major surface modification steps were examined by X-ray photoelectron spectroscopic analysis and surface free-angle measurement by contact angle goniometry. End-stage anti-thrombogenicity detection after 20 min of blood exposure at 100 s−1, 300 s−1, 600 s−1, 750 s−1, and 900 s−1 shear rates revealed that a significant reduction (>90%) of platelet adhesion and aggregation was achieved for surface-modified steel, compared with untreated material. This result is confirmed by experiments conducted in real time for 60-minute exposure to blood at 100 s−1, 600 s−1, and 900 s−1 shear rates.
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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".