Bibliographic record
Abstract
Abstract A dioxin‐like compound (DLC) is a halogenated aromatic compound that has toxicological properties similar to those of the reference toxicant 2,3,7,8‐tetrachlorodibenzo‐p‐dioxin (TCDD). The DLCs include other halogenated dibenzo‐p‐dioxins and dibenzofurans (especially those that are chlorinated in the 2‐,3‐,7‐, and 8‐positions), and coplanar polychlorinated biphenyls (PCBs). They associate with the aryl hydrocarbon receptor (AhR) protein, and share some biological end‐points with TCDD, notably the induction of Phase 1 monooxygenase enzymes. Because DLCs normally occur as complex mixtures in environmental and biological samples, it is common to refer to the TCDD equivalent concentration [toxic equivalence (TEQ)], which is obtained by summing for each congener its actual amount or concentration by an empirical toxic equivalency factor (TEF). Conventional analysis of DLCs by gas chromatography/mass spectrometry (GC/MS) is cumbersome and expensive, and much attention has been given to developing bioassays that will yield a measure of the TEQ in a single determination. Many such bioassays are mechanism‐based, meaning that the assay end‐point is one of the steps along the pathway of the mechanism of action. This article begins with an overview of bioassay methods in general, pointing out similarities and differences between chemical assays and bioassays, before describing particular assays that have been developed for DLCs. Among mechanism‐based assays, AhR binding assays are well established, and are useful because they include the dioxin‐like activity of both productive and unproductive compounds. In assays based on subsequent stages of the mechanism of action, unproductive substances can antagonize the responses of productive compounds, a phenomenon that is explicable in terms of target molecule antagonism, in which the AhR protein is the target molecule. Immunoassays for DLCs have recently received much attention; although their spectrum of cross‐reactivity does not always correlate well with TEFs, there has been important recent progress in terms of the sensitivity and detection limit (DL) of these assays. Finally, early life stage bioassays are becoming increasingly important as research has revealed the toxicological effects of DLCs during development.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.173 | 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; both teacher heads agree on what is shown here.
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".