Aryl Hydrocarbon Receptor Targeted by Xenobiotic Compounds and Dietary Phytochemicals
Bibliographic record
Abstract
The aryl hydrocarbon receptor (AhR) is a ligand-activated transcription factor that mediates the toxic effects of halogenated aromatic hydrocarbons (HAHs) such as polychlorinated dibenzo-p-dioxins (PCDDs), polychlorinated dibenzofurans (PCDFs) and select polychlorinated biphenyls (PCBs). Detectable levels of these contaminants are present in all humans. The most toxic compound in the class is 2,3,7,8-tetrachlordibenzo-p-dioxin (TCDD or dioxin). Laboratory animals exposed to environment levels of TCDD exhibit a wide spectrum of toxic responses, including increases in a number of different cancers. It is estimated that 90% of human exposure to these compounds is through dietary intake of products from animal origin and fish. AhR also exhibits profound ligand binding promiscuity, binding a number of compounds including phytochemicals such as polyphenols and flavonoids, many of which act as AhR antagonists. The presence of AhR-binding phytochemicals in the diet may in some cases antagonize the toxic effects of AhR-activating food contaminants. In this chapter we discuss the signalling pathways, the molecular mechanisms and potential health effects of activation of AhR by the dioxin-like food contaminants as well as the potential beneficial effects of AhR-modulating phytochemicals.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 0.012 |
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".