Quantum dot-induced cell death involves Fas upregulation and lipid peroxidation in human neuroblastoma cells
Bibliographic record
Abstract
BACKGROUND: Neuroblastoma, a frequently occurring solid tumour in children, remains a therapeutic challenge as existing imaging tools are inadequate for proper and accurate diagnosis, resulting in treatment failures. Nanoparticles have recently been introduced to the field of cancer research and promise remarkable improvements in diagnostics, targeting and drug delivery. Among these nanoparticles, quantum dots (QDs) are highly appealing due to their manipulatable surfaces, yielding multifunctional QDs applicable in different biological models. The biocompatibility of these QDs, however, remains questionable. RESULTS: We show here that QD surface modifications with N-acetylcysteine (NAC) alter QD physical and biological properties. In human neuroblastoma (SH-SY5Y) cells, NAC modified QDs were internalized to a lesser extent and were less cytotoxic than unmodified QDs. Cytotoxicity was correlated with Fas upregulation on the surface of treated cells. Alongside the increased expression of Fas, QD treated cells had increased membrane lipid peroxidation, as measured by the fluorescent BODIPY-C11 dye. Moreover, peroxidized lipids were detected at the mitochondrial level, contributing to the impairment of mitochondrial functions as shown by the MTT reduction assay and imaged with confocal microscopy using the fluorescent JC-1 dye. CONCLUSION: QD core and surface compositions, as well as QD stability, all influence nanoparticle internalization and the consequent cytotoxicity. Cadmium telluride QD-induced toxicity involves the upregulation of the Fas receptor and lipid peroxidation, leading to impaired neuroblastoma cell functions. Further improvements of nanoparticles and our understanding of the underlying mechanisms of QD-toxicity are critical for the development of new nanotherapeutics or diagnostics in nano-oncology.
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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".