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Record W4405949529 · doi:10.26599/jfb.2024.95028398

Antioxidant and anti-inflammatory activities, bioaccessibility, transmembrane transport of major phenolics from selected floral honeys using Caco-2 BBe1 cell model

2024· article· en· W4405949529 on OpenAlexaff
Yan Zhu, Ronghua Liu, Lili Mats, Honghui Zhu, Tauseef Khan, John L. Sievenpiper, Dan Ramdath, Rong Tsao

Bibliographic record

VenueJournal of Food Bioactives · 2024
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicBee Products Chemical Analysis
Canadian institutionsSt. Michael's HospitalAgriculture and Agri-Food Canada
FundersNational Honey Board
KeywordsAntioxidantTraditional medicineCaco-2ChemistryFood scienceCellBiochemistryMedicine

Abstract

fetched live from OpenAlex

In the present study, we assessed the antioxidant activity of the phenolic extracts and major phenolic compounds of alfalfa, buckwheat, clover and orange honeys collected in North America using chemical-based and cell-based antioxidant assays (CAA). Cell culture models using Caco-2 BBe1 were established to evaluate the effect of honey phenolics on antioxidant enzyme activities and secretion of interleukin 8 (IL-8). Moreover, bioaccessibility, transmembrane transport and cellular uptake of honey phenolics were also studied. Based on the same quantity of the honey studied, phenolic extract of buckwheat honey showed the highest FRAP, DPPH, ORAC and CAA values, and strongest activity in restoring activities of antioxidant enzymes (GR, SOD and CAT) and in reducing TNF-α-induced IL-8 secretion. Our results showed that compared to the major phenolic component of honey of plant origin, minor phenolics or combination of different phenolic compounds, particularly those derived from propolis, and their phase Ⅱ metabolites may play contribute more to the overall antioxidant and anti-inflammatory effects. Future research will focus on bioavailability of honey phenolics and their metabolites, and the molecular mechanism of the antioxidant, anti-inflammatory activities.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.016
Threshold uncertainty score0.423

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.019
GPT teacher head0.222
Teacher spread0.203 · 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

Citations0
Published2024
Admission routes1
Has abstractyes

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