Lipid Droplets: Their Role in Nanoparticle-Induced Oxidative Stress
Bibliographic record
Abstract
Lipid droplets are cytoplasmic organelles found in almost all cells under physiological or pathological conditions. Certain nanoparticles can induce lipid droplet formation under oxidative stress conditions. Small metallic nanoparticles such as cadmium telluride (CdTe) nanoparticles, particularly those with incompletely protected surfaces, induce oxidative stress and may inflict damages to several intracellular organelles. The objective of this study was to assess formation of lipid droplets in cells treated with CdTe nanoparticles and relate their status to cell function (mitochondrial activity and cell viability). Multicolor labeling of cellular organelles (lipid droplets and lysosomes) showed that lipid droplets formed in pheochromocytoma (PC12) cells following nanoparticle or oleic acid treatment. Some lipid droplets were found closely apposed to lysosomes suggesting possible communication between these organelles during severe oxidative stress. Combination of microscopy of living cells with cell viability assays showed that oleic acid-induced lipid droplets not only serve as intracellular lipid storage sites but also play a protective role in starving stressed cells. Results from these studies suggest that oleic acid-induced LD in PC12 cells are dynamic and adaptive organelles, which provide energy to starving cells and facilitate their rescue under starvation and exposure to metallic nanoparticles.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".