Metal-based nanoparticles: Promising tools for the management of cardiovascular diseases
Bibliographic record
Abstract
Cardiovascular disease (CVD) is the leading cause of death worldwide. A search for more effective treatments of CVD is increasingly needed. Major advances in nanotechnology opened new avenues in CVD therapeutics. Owing to their special properties, iron oxide, gold and silver nanoparticles (NPs) could exert various effects in the management and treatment of CVD. The role of iron oxide NPs in the detection and identification of atherosclerotic plaques is receiving increased attention. Moreover, these NPs enhance targeted stem cell delivery, thereby potentiating the regenerative capacity at the injured sites. In addition to their antioxidative and antihypertrophic capacities, gold NPs have also been shown to be useful in the identification of plaques and recognition of inflammatory markers. Contrary to first reports suggestive of their cardio-vasculoprotective role, silver NPs now appear to exert negative effects on the cardiovascular system. Indeed, these NPs appear to negatively modulate inflammation and cholesterol uptake, both of which exacerbate atherosclerosis. Moreover, silver NPs may precipitate bradycardia, conduction block and sudden cardiac death. In this review, we dissect the cellular responses and toxicity profiles of these NPs from various perspectives including cellular and molecular ones.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".