Understanding and improving assays for cytotoxicity of nanoparticles: what really matters?
Bibliographic record
Abstract
Despite years of excellent individual studies, the impact of nanoparticle (NP) cytotoxicity studies remains limited by inconsistent data collection and analysis. It is often unclear how exposure conditions can be used to determine cytotoxicity quantitatively. Discrepancies due to using different measurement conditions, readouts and controls to characterize NP interactions with cells lead to further challenges. To examine which parameters are critical in NP cytotoxicity studies, we have chosen to examine two NP types (liposomes and quantum dots) at different concentrations incubated with two primary vascular endothelial cells, HUVEC and HMVEC-C for a standard time of 24 h. We paid close attention to the effects of positive controls and cell association on interpretation of cytotoxicity data. Various cellular responses (ATP content, oxidative stress, mitochondrial toxicity, and phospholipidosis) were measured in parallel. Interestingly, cell association data varied significantly with the different image analyses. However, cytotoxicity responses could all be correlated with exposure concentration. Cell type did have an effect on cytotoxicity reports. Most significantly, NP cytotoxicity results varied with the inclusion or exclusion of positive controls. In the absence of positive controls, one tends to emphasize small changes in cell responses to NPs.
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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 | 0.036 | 0.042 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".