Toxicological assessment of industrial solvents using human cell bioassays: assessment of short-term cytotoxicity and long-term genotoxicity potential
Bibliographic record
Abstract
There is an increasing demand for simple toxicological screening methods to assess the human health risk associated with exposure to environmental toxicants. Such screening tools should allow for risk evaluation in terms of both short-term/acute toxicity and longer-term genetic damage, which may lead to mutagenicity and carcinogenicity. We employed a battery of human cell bioassays using the human hepatoma cell-line, HepG2, to assess the cytotoxic and genotoxic potential of environmental pollutants. Here, we demonstrate direct application of these human cell bioassays to the toxicological assessment of a number of industrial solvents that are in common use worldwide. HepG2 cells were exposed to various solvents at concentrations ranging from 25 to 500 ppm. The cells were then analysed using specific protocols for four different adverse effects: cell death/acute cytotoxicity using a neutral red uptake assay, altered enzyme function (often an indicator of cell stress) using the ethoxyresorufin O-deethylase (EROD) bioassay, DNA single strand breaks (SSB), and DNA repair induction, which evaluates mutagenic activity. Using the positive controls, linear dose-response curves were achieved for all four bioassays. The high sensitivity of the tests allowed for environmentally meaningful assessments, and precision studies showed excellent reproducibility for all four bioassays. Comparing the results of the four bioassays on each of 16 industrial solvents allowed for ranking of the anticipated relative human toxicity of these solvents, which were comparable with data from standard toxicity tests and human occupational data. Overall, the study clearly supports the application of the HepG2 cell bioassay system for rapid toxicological screening of many candidate toxicants for both short- and long-term toxicity potential.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".