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Record W3087172481 · doi:10.1093/ajcn/nqaa204

Recommendations for standardizing nomenclature for dietary (poly)phenol catabolites

2020· article· en· W3087172481 on OpenAlexaff
Colin D. Kay, Michael N. Clifford, Pedro Mena, Gordon J. McDougall, Cristina Andrés‐Lacueva, Aedín Cassidy, Daniele Del Rio, Nikolai Kuhnert, Claudine Manach, Gema Pereira‐Caro, Ana Rodriguez‐Mateos, Augustin Scalbert, Francisco A. Tómas‐Barberán, Gary Williamson, David S. Wishart, Alan Crozier

Bibliographic record

VenueAmerican Journal of Clinical Nutrition · 2020
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMetabolomics and Mass Spectrometry Studies
Canadian institutionsUniversity of Alberta
FundersAgència de Gestió d'Ajuts Universitaris i de RecercaInstituto de Salud Carlos IIIMinistero delle Politiche Agricole Alimentari e ForestaliU.S. Department of AgricultureAgence Nationale de la RechercheInstitució Catalana de Recerca i Estudis AvançatsEuropean Regional Development FundHorizon 2020 Framework ProgrammeNational Institute of Food and AgricultureCentro de Investigación Biomédica en Red Fragilidad y Envejecimiento SaludableWorld Health Organization
KeywordsTerminologyConfusionNomenclatureChemical nomenclatureMetabolomeHealth claims on food labelsHuman healthChemistryBiochemistryMetaboliteBiologyMedicineFood sciencePsychologyEnvironmental healthOrganic chemistry

Abstract

fetched live from OpenAlex

There is a lack of focus on the protective health effects of phytochemicals in dietary guidelines. Although a number of chemical libraries and databases contain dietary phytochemicals belonging to the plant metabolome, they are not entirely relevant to human health because many constituents are extensively metabolized within the body following ingestion. This is especially apparent for the highly abundant dietary (poly)phenols, for which the situation is compounded by confusion regarding their bioavailability and metabolism, partially because of the variety of nomenclatures and trivial names used to describe compounds arising from microbial catabolism in the gastrointestinal tract. This confusion, which is perpetuated in online chemical/metabolite databases, will hinder future discovery of bioactivities and affect the establishment of future dietary guidelines if steps are not taken to overcome these issues. In order to resolve this situation, a nomenclature system for phenolic catabolites and their human phase II metabolites is proposed in this article and the basis of its format outlined. Previous names used in the literature are cited along with the recommended nomenclature, International Union of Pure and Applied Chemistry terminology, and, where appropriate, Chemical Abstracts Service numbers, InChIKey, and accurate mass.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.717
Threshold uncertainty score0.366

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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 head0.077
GPT teacher head0.396
Teacher spread0.320 · 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 designNot applicable
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

Citations111
Published2020
Admission routes1
Has abstractyes

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