An Aryl Hydrocarbon Receptor Odyssey to the Shores of Toxicology: The Deichmann Lecture, International Congress of Toxicology-XI
Bibliographic record
Abstract
The science of toxicology is devoted, in large part, to understanding mechanisms of toxicity so that we can more accurately assess the risk posed by exposure to xenobiotic agents and, perhaps, intervene in the toxicologic process to mitigate harm. Dioxin-like chemicals continue to be of great concern as environmental toxicants. About 30 years ago the aryl hydrocarbon receptor (AHR) was discovered as a specific binding site for 2,3,7,8-tetrachlorodibenzo-p-dioxin. This giant step led to our current view that essentially all toxic effects of dioxins are AHR-mediated. The AHR serves as the archetype for understanding toxicity mediated by other soluble receptors. The fact that toxicity is receptor-mediated has important implications, especially for dose-response relationships. In laboratory animals genetic differences in AHR gene structure lead to profound differences in responsiveness to dioxin-like chemicals. Humans, however, exhibit relatively few AHR polymorphisms and these seem to exert only modest effects on downstream events. Dioxin toxicity is fundamentally due to AHR-mediated dysregulation of gene expression. Our current challenging goal is to determine which dysregulated genes underlie specific forms of dioxin toxicity. Mapping AHR-mediated gene expression in a variety of biological systems may help explain why dramatic differences in susceptibility to dioxin toxicity exist among laboratory species and why humans appear to be relatively resistant to adverse effects of dioxins.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.010 |
| Insufficient payload (model declined to judge) | 0.012 | 0.008 |
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".