Investigating Benzene and Metabolite Mediated Alterations in Nrf2 Signaling in Murine Fetal Liver Cells
Bibliographic record
Abstract
Leukemia is a cancer of the blood, where the body produces an overabundance of immature white blood cells. It is the most common childhood cancer and possibly originates from chemical exposure during fetal development. Understanding the molecular mechanism and causes of childhood leukemia will help the development of therapeutic and preventative strategies to reduce and treat occurrences of this cancer. Benzene is a non-polar aromatic ring that has been confirmed to cause leukemia in adults. Benzene metabolites can generate free radicals that could potentially be involved in the development of childhood leukemia through in utero exposures. It is found in tobacco smoke, gasoline, industrial solvents and many other substances. Pregnant mothers can be chronically exposed to these substances through daily activities. In order to help determine the mechanism of benzene toxicity, a study was performed to determine the presence of free radicals in the livers of fetal mice at gestation day 14. Fetal liver cell cultures were exposed to varying concentrations of benzene and additional cell cultures were exposed to various concentrations of a benzene metabolite mixture, composed of hydroquinone and benzoquinone. RNA was extracted from these cells and converted into complementary DNA. cDNA was then used in quantitative polymerase chain reactions to analyze a set of genes that are regulated by an antioxidant pathway. This pathway can be activated in response to oxidative stress, and a change in gene expression would indicate the presence of benzene or benzene metabolite toxicity in the liver at gestation day 14.
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 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) | 0.002 | 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".