Probing and Preventing Quantum Dot-Induced Cytotoxicity with Multimodal α-Lipoic Acid in Multiple Dimensions of The Peripheral Nervous System
Bibliographic record
Abstract
AIM: Toxicity of nanoparticles developed for biomedical applications is extensively debated as no uniform guidelines are available for studying nanomaterial safety, resulting in conflicting data obtained from different cell types. This study demonstrates the varied toxicity of a selected type of nanoparticle, cadmium telluride quantum dots (QDs), in three increasingly complex cell models of the peripheral nervous system. MATERIALS & METHODS: QD-induced cytotoxicity was assessed via cell viability assays and biomarkers of subcellular damage in PC12 cells and mixed primary dispersed dorsal root ganglia (DRG) cultures. Morphological analysis of neurite outgrowth was used to determine the viability of axotomized DRG explant cultures. RESULTS & DISCUSSION: Cadmium telluride QDs and their core metals exert different degrees of toxicity in the three cell models, the primary dispersed DRGs being the most susceptible. alpha-lipoic acid is an effective, multimodal, cytoprotective agent that can act as an antioxidant, metal chelator and QD-surface modifier in these cell systems. CONCLUSION: Complex multicellular model systems, along with homogenous cell models, should be utilized in standard screening and monitoring procedures for evaluating nanomaterial safety.
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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) | 0.000 | 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 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".