MétaCan
Menu
Back to cohort
Record W1605218365 · doi:10.1002/9780470027318.a0811

Dioxin‐Like Compounds, Screening Assays

2000· other· en· W1605218365 on OpenAlexaff
Nigel J. Bunce, John R. Petrulis

Bibliographic record

VenueEncyclopedia of Analytical Chemistry · 2000
Typeother
Languageen
FieldEnvironmental Science
TopicToxic Organic Pollutants Impact
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsBioassayAryl hydrocarbon receptorChemistryToxicantMechanism of actionEnvironmental chemistryPolychlorinated dibenzodioxinsPolychlorinated dibenzofuransCongenerBiological activityChromatographyBiochemistryToxicityOrganic chemistryBiologyIn vitro

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.173
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.1730.001

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.

Opus teacher head0.007
GPT teacher head0.228
Teacher spread0.221 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreOther

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".

Quick stats

Citations0
Published2000
Admission routes1
Has abstractyes

Explore more

Same venueEncyclopedia of Analytical ChemistrySame topicToxic Organic Pollutants ImpactFrench-language works237,207