Bibliographic record
Abstract
A major goal in human health risk assessment is the identification and management of chemicals that may cause cancer in human populations.The current goldstandard for assessing chemical carcinogenicity is the two-year rodent cancer bioassay, which is animal, time, and resource intensive.The use of toxicogenomics for chemical risk assessment was first proposed over 15 years ago because of its potential to produce toxicologically relevant data more quickly, using fewer animals, and at a lower cost than the two-year cancer bioassay.While the two-year cancer bioassay produces a detailed inventory of chemical-dependent lesions, toxicogenomics analyzes chemical-dependent changes to global gene expression.Moreover, toxicogenomics provides comprehensive mechanistic data that are not obtained using standard tests.In this thesis quantitative, predictive, and mechanistic approaches were applied to a toxicogenomic case study of the rodent hepatocarcinogen furan.Female B3C6F1 mice were exposed for three weeks to non-carcinogenic or carcinogenic doses of furan.The dose response of a variety of transcriptional endpoints produced benchmark doses (BMDs) similar to the furandependent cancer BMDs.Bioinformatic analysis of disease datasets showed strong similarity between global gene expression changes induced by furan and those associated with the appropriate hepatic pathologies.The molecular pathways that were enriched in the liver following furan exposure facilitated the development of a molecular mode of action (MoA) for furan-induced liver cancer.Finally, transcriptional changes in formalinfixed and paraffin embedded (FFPE) samples were compared to high quality frozen samples in order to evaluate whether archival samples are a viable option for toxicogenomic studies.The advantage of using FFPE tissues is that they are very well iii characterized (phenotypically); the disadvantage is that formalin degrades biomacromolecules, including RNA.FFPE samples as old as three decades were shown to be feasible for toxicogenomics studies using a ribo-depletion RNA-seq protocol.Taken together, this case study demonstrates the utility of toxicogenomics data in human health risk assessment and the potential of archival FFPE tissue samples, and identifies viable strategies toward the reduction of animal usage in chemical testing.7.5 Concluding Remarks ....
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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 | 0.000 | 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) | 0.001 | 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".