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Diferuloylquinide is converted to diferuloylquinic acid and other phenolic compounds during digestion and/or metabolism in rats.

2008· article· en· W2259358963 on OpenAlexaff
Adriana Farah, Jane Shearer, Tomas de Paulis

Bibliographic record

VenueThe FASEB Journal · 2008
Typearticle
Languageen
FieldMedicine
TopicCoffee research and impacts
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsQuinic acidChlorogenic acidChemistryPhenolic acidMetaboliteMetabolismIn vivoFerulic acidFood scienceRoastingBiochemistryChromatographyBiologyAntioxidant

Abstract

fetched live from OpenAlex

Chlorogenic acids are esters of hydroxycinnamic acids with quinic acid. Coffee is one of the main food sources of chlorogenic acids and during the roasting of the beans, part of these compounds is transformed into lactones or quinides, through the loss of a water molecule and formation of an intramolecular ester bond. Diferuloylquinide (DIFEQ), a representative compound of this class, has previously exhibited hypoglicemic activity in rats, among other biological activities. In the present preliminary study, DIFEQ was diluted in saline and administered to 3 male Sprague Dawley rats through a gastric catheter. Blood draws were taken 20, 40 and 60 min. after DIFEQ administration and plasma samples were analyzed by HPLC‐UV. The main DIFEQ metabolite observed in plasma in all time points was its corresponding chlorogenic acid, diferuloylquinic acid. A series of other metabolites including ferulic acid, isoferulic acid and 3‐feruloylquic acid were also identified, in addition to small amounts of DIFEQ. Whether DIFEQ breakdown occurs prior, during or after absorption is still under investigation. These results suggest that the major compounds responsible for the hypoglycemic and other biological activities of quinides observed in vivo are their corresponding chlorogenic acid compounds and metabolites. Financial support: Institute for Coffee Studies‐VU (USA), CBP&Dcafé (Brazil).

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 score0.762
Threshold uncertainty score0.430

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 integrity0.0000.001
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 head0.048
GPT teacher head0.309
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; 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

Citations3
Published2008
Admission routes1
Has abstractyes

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