MétaCan
Menu
Back to cohort
Record W4386529157 · doi:10.1080/03602532.2023.2254525

Differential modulation of cytochrome P450 enzymes by arsenicals in non-human experimental models

2023· review· en· W4386529157 on OpenAlexafffund
Mahmoud A. El‐Ghiaty, Sara R. El-Mahrouk, Mohammed A. Alqahtani, Ayman O.S. El‐Kadi

Bibliographic record

VenueDrug Metabolism Reviews · 2023
Typereview
Languageen
FieldPharmacology, Toxicology and Pharmaceutics
TopicPharmacogenetics and Drug Metabolism
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsArsenicCytochrome P450XenobioticEnzymeBiochemistryBiologyChemistry

Abstract

fetched live from OpenAlex

Arsenic is a hazardous heavy metalloid that imposes threats to human health globally. It is widely spread throughout the environment in various forms. Arsenic-based compounds are either inorganic compounds (iAs) or organoarsenicals (oAs), where the latter are biotically generated from the former. Exposure to arsenic-based compounds results in varying biochemical derangements in living systems, leading eventually to toxic consequences. One important target for arsenic in biosystems is the network of metabolic enzymes, especially the superfamily of cytochrome P450 enzymes (CYPs) because of their prominent role in both endobiotic and xenobiotic metabolism. Therefore, the alteration of the CYPs by different arsenicals has been actively studied in the last few decades. We have previously summarized the findings of former studies investigating arsenic associated modulation of different CYPs in human experimental models. In this review, we focus on non-human models to get a complete picture about possible CYPs alterations in response to arsenic exposure.

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)0.002
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 categoriesMeta-epidemiology (narrow)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.964
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0070.002
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0010.000

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 head0.256
GPT teacher head0.518
Teacher spread0.261 · 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
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

Citations8
Published2023
Admission routes2
Has abstractyes

Explore more

Same venueDrug Metabolism ReviewsSame topicPharmacogenetics and Drug MetabolismFrench-language works237,207