The Adhesive Properties of Endothelial Cells on Endovascular Stent Coated by Substrates of Poly-<scp>L</scp>-Lysine and Fibronectin
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Optimizing endothelial cell growth and adhesion on the surface of metallic stents implanted in the vascular system is a fundamental issue in understanding and improving their long-term biocompatibility. The ability of the endothelial cell to attach and adhere to the luminal stent surface as well as the capacity to withstand the significant shear stress associated with blood flow are important determinants. The adhesive characteristics of human umbilical vein endothelial cellsectin (HUVEC) on stent surfaces coated with either Poly-L-Lysine (PLL) or fibron (FN) were compared with uncoated controls. Increasing concentrations of PLL and FN were measured using a micropipette aspiration system. The adhesivenamic properties of HUVECs under static flow conditions were compared to a dy environment on endovascular stents using a parallel-plate-flow chamber. A scanning electron microscope picture was used to measure the number and the adhesive cell ratio as well as the percentage of surface coverage of stent by endothelial cells. The adhesive forces of HUVECs on foreign surfaces coated with PLL and FN were higher compared to uncoated surfaces, and were dependent on incr ing concentrations. These coatings resulted in significant increase of the adhesive force of HUVECs. The influence of substrates on the adhesion of the endothelial cell monolayer under static or dynamic flow conditions was highly significant compared with controls (p<0.01). No significant differences were observed between PLL and FN substrates. Both PLL and FN coated surfaces can significantly increase the adhesion and growth of HUVECs on metallic stent surfaces.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| 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 it