Metal-based nanoparticles: Promising tools for the management of cardiovascular diseases
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
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.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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