Development of stable HSPA1A promoter-driven luciferase reporter HepG2 cells for assessing the toxicity of organic pollutants present in air
Bibliographic record
Abstract
HSPA1A (HSP70-1) is a highly inducible heat shock gene up-regulated in response to environmental stresses and pollutants. The aim of our study was to evaluate the sensitivity of the stable metabolically competent HepG2 cells containing a human HSPA1A promoter-driven luciferase reporter (HepG2-luciferase cells) for assessing the toxicity of organic pollutants present in air. The HepG2-luciferase cells were validated by heat shock treatment and testing three organic compounds (pyrene, benzo[a]pyrene, and formaldehyde) that are ubiquitous in the air. The maximal level of HSPA1A (HSP70-1) and relative luciferase activity induced by heat shock were over three and nine times the control level, respectively. Pyrene, benzo[a]pyrene, and formaldehyde all induced significantly elevated levels of relative luciferase activity in a dose-dependent manner. Extractable organic matter (EOM) from urban traffic and coke oven emissions in ambient air were tested on the HepG2-luciferase cells. The traffic EOM induced significant increase in relative luciferase activity at concentrations of picogram per liter. The coke oven EOM produced a strong dose-dependent induction of relative luciferase activity up to six times the control value. Significant increases in relative luciferase activity were observed at concentrations that were as low, or lower than the concentrations that the tested organic pollutants decreased cell viability, and increased malondialdehyde concentration, Olive tail moment, and micronuclei frequency. Therefore, we conclude that the HepG2-luciferase cells are a valuable tool for rapid screening of the overall toxicity of organic pollutants present in air.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".