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Record W2222120260 · doi:10.1159/000424206

Immunotoxicity of Polychlorinated Biphenyls: Present Status and Future Considerations

2015· review· en· W2222120260 on OpenAlexaff
Helen Tryphonas

Bibliographic record

Venuenot available
Typereview
Languageen
FieldImmunology and Microbiology
TopicImmunotoxicology and immune responses
Canadian institutionsHealth Canada
Fundersnot available
KeywordsImmunotoxicologyChlorine atomPolychlorinated biphenylBiphenylChemistryEnvironmental chemistryMechanism (biology)Immune systemBiologyImmunologyOrganic chemistry

Abstract

fetched live from OpenAlex

Polychlorinated biphenyls (PCBs) are widely spread environmental contaminants consisting of chemical mixtures containing many of the 209 possible congeners. The potential immunomodulatory properties of PCBs have been the subject of extensive experimental investigations. The available evidence indicates that the immune system is a target for PCBs and is perhaps one of the most sensitive indicators for adverse PCB-induced health effects. Recent advances regarding the mechanism of PCB-induced immunotoxicity point to their dependency on the presence of the aromatic hydrocarbon receptor and their ability to bind to this receptor as the venue for their toxicological activity. Their binding affinity depends on the degree of chlorination of the biphenyl structure and the position of the chlorine atoms. Such advances have contributed significantly to the determination of the relative immunotoxic potential of PCB mixtures and to the calculation of TEFs for several of the PCB congeners. Such information is critical for evaluating the potential risk PCBs pose to human health. To fully exploit the potential contribution immunotoxicology can make to risk assessment, it is important that the data base on mechanism(s) of PCB-induced immunotoxicity and the potential agonistic/antagonistic properties of PCBs be expanded considerably.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0000.001
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.001

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 head0.047
GPT teacher head0.327
Teacher spread0.280 · 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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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

Citations65
Published2015
Admission routes1
Has abstractyes

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