Silicon dioxide nanoparticles increase macrophage atherogenicity: Stimulation of cellular cytotoxicity, oxidative stress, and triglycerides accumulation
Bibliographic record
Abstract
Nanoparticle research has focused on their toxicity in general, while increasing evidence points to additional specific adverse effects on atherosclerosis development. Arterial macrophage cholesterol and triglyceride (TG) accumulation and foam cell formation are the hallmark of early atherogenesis, leading to cardiovascular events. To investigate the in vitro atherogenic effects of silicon dioxide (SiO2 ), J774.1 cultured macrophages (murine cell line) were incubated with SiO2 nanoparticle (SP, d = 12 nm, 0-20 µg/mL), followed by cellular cytotoxicity, oxidative stress, TG and cholesterol metabolism analyses. A significant dose-dependent increase in oxidative stress (up to 164%), in cytotoxicity (up to 390% measured by lactate dehydrogenase (LDH) release), and in TG content (up to 63%) was observed in SiO2 exposed macrophages compared with control cells. A smaller increase in macrophage cholesterol mass (up to 22%) was noted. TG accumulation in macrophages was not due to a decrease in TG cell secretion or to an increased TG biosynthesis rate, but was the result of attenuated TG hydrolysis secondary to decreased lipase activity and both adipose triglyceride lipase (ATGL) and hormone-sensitive lipase (HSL) protein expression (by 42 and 25%, respectively). Overall, SPs showed pro-atherogenic effects on macrophages as observed by cytotoxicity, increased oxidative stress and TG accumulation. © 2014 Wiley Periodicals, Inc. Environ Toxicol 31: 713-723, 2016.
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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) | 0.000 | 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) | 0.001 | 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 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".