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Record W4293027686 · doi:10.3390/antiox11091635

Structural Elucidation of Novel Stable and Reactive Metabolites of Green Tea Catechins and Alkyl Gallates by LC-MS/MS

2022· article· en· W4293027686 on OpenAlexafffund
Ons Ousji, Lekha Sleno

Bibliographic record

VenueAntioxidants · 2022
Typearticle
Languageen
FieldMedicine
TopicTea Polyphenols and Effects
Canadian institutionsUniversité du Québec à Montréal
FundersNatural Sciences and Engineering Research Council of CanadaCentre d’Excellence en Recherche sur les Maladies Orphelines – Fondation Courtois
KeywordsChemistryGallic acidBiotransformationCatechinLiquid chromatography–mass spectrometryAntioxidantTandem mass spectrometryPolyphenolMetabolic pathwayGlutathioneChromatographyBiochemistryOrganic chemistryMetabolismMass spectrometryEnzyme

Abstract

fetched live from OpenAlex

Synthetic gallic acid derivatives are employed as additives in food, personal care products, and pharmaceutical formulations. Despite their widespread use, little is known about their human exposure, health effects, and metabolism. Green tea catechins are natural antioxidants, known for their health-promoting properties, and are also employed as food additives or in personal care products. The objective of this study was to establish metabolic pathways involved in the biotransformation of green tea catechins and synthetic gallate esters. Liquid chromatography coupled with high-resolution tandem mass spectrometry (LC-HRMS/MS) was used to elucidate oxidative and methylated metabolites, in addition to glutathione conjugates, formed in vitro using human liver microsomal incubations. The developed method was applied to 14 different parent compounds with a wide range of polarities, for the structural elucidation of many known and novel metabolites. These results serve to inform about the wide variety of possible metabolites formed upon exposure to these compounds.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.467
Threshold uncertainty score0.612

Codex and Gemma teacher scores by category

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

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 head0.013
GPT teacher head0.253
Teacher spread0.239 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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

Citations19
Published2022
Admission routes2
Has abstractyes

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