Nanoparticles can induce changes in the intracellular metabolism of lipids without compromising cellular viability
Bibliographic record
Abstract
There is growing concern about the safety of engineered nanoparticles, which are produced for various industrial applications. Quantum dots are colloidal semiconductor nanoparticles that have unique luminescence characteristics and the potential to become attractive tools for medical imaging. However, some of these particles can cause oxidative stress and induce cell death. The objective of this study was to explore quantum dot-induced metabolic changes, which could occur without any apparent cellular damage. We provide evidence that both uncoated and ZnS-coated quantum dots can induce the accumulation of lipids (increase in cytoplasmic lipid droplet formation) in two cell culture models: glial cells in primary mouse hypothalamic cultures and rat pheochromocytoma PC12 cells. Glial cells treated with CdTe quantum dots accumulated newly synthesized lipids in a phosphoinositide 3-kinase-dependent manner, which was consistent with the growth factor-dependent accumulation of lipids in PC12 cells treated with CdTe and CdSe/ZnS quantum dots. In PC12 cells, quantum dots, as well as the hypoxia mimetic CoCl(2), induced the up-regulation of hypoxia-inducible transcription factor-1alpha and the down-regulation of the beta-oxidation of fatty acids, both of which could contribute to the accumulation of lipids. On the basis of our results, we propose a model illustrating how nanoparticles, such as quantum dots, could trigger the formation of intracellular lipid droplets, and we suggest that metabolic measurements, such as the determination of fat oxidation in tissues, which are known sites of nanoparticle accumulation, could provide useful measures of nanoparticle safety. Such assays would expand the current platform of tests for the determination of the biocompatibility of nanomaterials.
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.
How this classification was reachedexpand
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) | 0.001 | 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 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".