Nanomedicine in cardiovascular therapy: recent advancements
Bibliographic record
Abstract
Cardiovascular disease (CVD) is comprised of a group of disorders affecting the heart and blood vessels of the human body and is one of the leading causes of death worldwide. Current therapy for CVD is limited to the treatment of already established disease, and it includes pharmacological and/or surgical procedures, such as percutaneous coronary intervention with stenting and coronary artery bypass grafting. However, lots of complications have been raised with these modalities of treatment, including systemic toxicity with medication, stent thrombosis with percutaneous coronary intervention and nonsurgical candidate patients for coronary artery bypass grafting. Nanomedicine has emerged as a potential strategy in dealing with these obstacles. Applications of nanotechnology in medicine are already underway and offer tremendous promise. This review explores the recent developments of nanotechnology in the field of CVD and gives an insight into its potential for diagnostics and therapeutics applications. The authors also explore the characteristics of the widely used biocompatible nanomaterials for this purpose and evaluate their opportunities and challenges for developing novel nanobiotechnological tools with high efficacy for biomedical applications, such as radiological imaging, vascular implants, gene therapy, myocardial infarction and targeted delivery systems.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".