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.
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 | 0.000 | 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 | 0.001 |
| 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 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".