Nanotechnology in development of next generation of stent and related medical devices: Current and future aspects
Bibliographic record
Abstract
Coronary stents have saved millions of lives in the last three decades by treating atherosclerosis especially, by preventing plaque protrusion and subsequent aneurysms. They attenuate the vascular SMC proliferation and promote reconstruction of the endothelial bed to ensure superior revascularization. With the evolution of modern stent types, nanotechnology has become an integral part of stent technology. Nanocoating and nanosurface fabrication on metallic and polymeric stents have improved their drug loading capacity as well as other mechanical, physico-chemical, and biological properties. Nanofeatures can mimic the natural nanofeatures of vascular tissue and control drug-delivery. This review will highlight the role of nanotechnology in addressing the challenges of coronary stents and the recent advancements in the field of related medical devices. Different generations of stents carrying nanoparticle-based formulations like liposomes, lipid-polymer hybrid NPs, polymeric micelles, and dendrimers are discussed highlighting their roles in local drug delivery and anti-restenotic properties. Drug nanoparticles like Paclitaxel embedded in metal stents are discussed as a feature of first-generation drug-eluting stents. Customized precision stents ensure safe delivery of nanoparticle-mediated genes or concerted transfer of gene, drug, and/or bioactive molecules like antibodies, gene mimics via nanofabricated stents. Nanotechnology can aid such therapies for drug delivery successfully due to its easy scale-up possibilities. However, limitations of this technology such as their potential cytotoxic effects associated with nanoparticle delivery that can trigger hypersensitivity reactions have also been discussed in this review. This article is categorized under: Implantable Materials and Surgical Technologies > Nanotechnology in Tissue Repair and Replacement Therapeutic Approaches and Drug Discovery > Nanomedicine for Cardiovascular Disease Therapeutic Approaches and Drug Discovery > Emerging Technologies.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".