Enhanced endothelial cell functions on rosette nanotube-coated titanium vascular stents
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
One of the main problems with current vascular stents is a lack of endothelial cell interactions, which if sufficient, would create a uniform healthy endothelium masking the underlying foreign metal from inflammatory cell interference. Moreover, if endothelial cells from the arterial wall do not adhere to the stent, the stent can become loose and dislodge. Therefore, the objective of this in vitro study was to design a novel biomimetic nanostructured coating (that does not contain drugs) on conventional vascular stent materials (specifically, titanium) for improving vascular stent applications. Rosette nanotubes (RNTs) are a new class of biomimetic nanotubes that self-assemble from DNA base analogs and have been shown in previous studies to sufficiently coat titanium and enhance osteoblast cell functions. RNTs have many desirable properties for use as vascular stent coatings including spontaneous self-assembly in body fluids, tailorable surface chemistry for specific implant applications, and nanoscale dimensions similar to those of the natural vascular extracellular matrix. Importantly, the results of this study provided the first evidence that RNTs functionalized with lysine (RNT-K), even at low concentrations, significantly increase endothelial cell density over uncoated titanium. Specifically, 0.01 mg/mL RNT-K coated titanium increased endothelial cell density by 37% and 52% compared to uncoated titanium after 4 h and three days, respectively. The excellent cytocompatibility properties of RNTs (as demonstrated here for the first time for endothelial cells) suggest the need for the further exploration of these novel nanostructured materials for vascular stent applications.
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.
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 | 0.000 |
| 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 it