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The Duality of Arsenic Metabolism: Impact on Human Health

2022· review· en· W4295754725 on OpenAlexafffund
Mahmoud A. El‐Ghiaty, Ayman O.S. El‐Kadi

Bibliographic record

VenueThe Annual Review of Pharmacology and Toxicology · 2022
Typereview
Languageen
FieldEnvironmental Science
TopicArsenic contamination and mitigation
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of CanadaAlberta Innovates
KeywordsArsenicDetoxification (alternative medicine)MetabolismMethylationArsenic poisoningMechanism (biology)ToxicityChemistryHuman healthArsenic toxicityBiotransformationEnvironmental chemistryToxicologyBiologyBiochemistryEnvironmental healthMedicineEnzymeOrganic chemistryPathologyPhilosophyGene

Abstract

fetched live from OpenAlex

Arsenic is a naturally occurring hazardous element that is environmentally ubiquitous in various chemical forms. Upon exposure, the human body initiates an elimination pathway of progressive methylation into relatively less bioreactive and more easily excretable pentavalent methylated forms. Given its association with decreasing the internal burden of arsenic with ensuing attenuation of its related toxicities, biomethylation has been applauded for decades as a pure route of arsenic detoxification. However, the emergence of detectable trivalent species with profound toxicity has opened a long-standing debate regarding whether arsenic methylation is a detoxifying or bioactivating mechanism. In this review, we approach the topic of arsenic metabolism from both perspectives to create a complete picture of its potential role in the mitigation or aggravation of various arsenic-related pathologies.

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)0.000
metaresearch head score (Gemma)0.000
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 score0.004
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.002

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 head0.052
GPT teacher head0.445
Teacher spread0.392 · 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

Citations78
Published2022
Admission routes2
Has abstractyes

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