Antibody-Based Capture and Behaviour of Endothelial Cell Lines on Pre-Surface Modified Medical Grade Steel
Bibliographic record
Abstract
Coronary artery disease is one of the major causes of morbidity and mortality worldwide. Coronary stents, tube-shaped medical implants that are placed in narrowed coronary arteries, have been used successfully in the management of this condition. However, re-narrowing (i.e. restenosis) of the artery can occur which is instigated by an immune response towards the implanted ‘foreign’ material. A new approach to prevent restenosis and reduce the stent-induced immune response has been proposed previously, which involves re-endothelialization of the implanted stent. In the present study a proof-of-concept experiment involving surface-modified medical grade steel was employed in order to examine the best surface chemistry for in vitro cell capture. Steel coupons were coated with a silanized adlayer, followed by attachment of whole antibodies, followed by culturing of human umbilical vein endothelial cells (HUVECs) or human aortic endothelial cells (HAECs or HAoECs). With regard to the adlayer-antibody configuration, HUVECs adhered and grew with normal morphology, and a significantly increased number of HUVECs proliferated on the coupons compared to the ‘bare’ surface. Similar effects were observed with HAECS grown on adlayer-antibody modified substrates, with a significantly higher number of cells proliferating. These results demonstrate a successful strategy for re-endothelialization of the steel surface that may prevent immune response with respect to the behaviour of steel-based stents in vivo.
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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".