Selenium Nanoparticles Decorated With Stevioside Potentially Attenuate Fructose Palmitate Induced Lipid Accumulation in HepG2 Cells
Bibliographic record
Abstract
The excessive accumulation of lipid droplets within hepatocytes stands as a hallmark characteristic of metabolic‐associated fatty liver disease (MAFLD). Selenium (Se) nanoparticles (NPs) have garnered considerable attention for their notable bioavailability, minimal toxicity, and exceptional antioxidant properties. However, a critical limitation lies in the propensity of SeNPs to aggregate into the biologically inactive elemental Se, thereby constraining their utility. Here, we utilized Stevioside ( SV ), a natural sweetener, to modify SeNPs and obtained the SV‐SeNPs with a size of about 187 ± 7 nm. We aimed to investigate the effect of SV‐SeNPs on high fructose–palmitate (HFP) induced lipid accumulation in HepG2 cells. Noteworthy is the absence of overt cytotoxicity attributed to SV‐SeNPs on normal HepG2 cells. Of significance, our findings delineate the profound inhibitory effects of SV‐SeNPs on the expression of key genes implicated in de novo lipogenesis, such as fatty‐acid synthase (FASN), acetyl‐CoA‐carboxylase 1 (ACC1), and stearoyl‐CoA desaturase–1 (SCD1) within HFP‐induced HepG2 cells. Furthermore, our investigation reveals that SV‐SeNPs mediate a significant reduction in lipid accumulation by activating the PI3K/AKT/Nrf2 signaling cascades. Additionally, the antioxidative properties of SV‐SeNPs are underscored by their ability to counteract oxidative stress via the upregulation of two pivotal antioxidant enzymes, superoxide dismutase (SOD) and glutathione peroxidase (GSHPx). In conclusion, our study unveils the potential beneficial effects of SV‐SeNPs on the prevention and treatment of MAFLD by effectively suppressing lipid accumulation and ameliorating oxidative stress.
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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".