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Record W2947160103 · doi:10.25177/jfst.4.5.ra.487

Phenolics from canola crude extracts protect cells from oxidative stress

2019· article· en· W2947160103 on OpenAlexaff
Peter Eck, Yougui Chen, Usha Thiyam‐Holländer, N.A.M. Eskin

Bibliographic record

VenueSDRP Journal of Food Science & Technology · 2019
Typearticle
Languageen
FieldMedicine
TopicPhytochemicals and Antioxidant Activities
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsCanolaOxidative stressFood scienceChemistryTraditional medicineBiochemistryMedicine

Abstract

fetched live from OpenAlex

Background: By-products of canola oil production are currently discarded or low value commodities.This study investigates if these by-products are a significant source of extractable dietary phenols which could exhibit cellular antioxidants activity.Methods: Endogenous phenolic compounds obtained from canola oil deodistillates and canola meal using different extraction techniques were identified and examined for their in vitro antioxidant activities in "test tube" and cellular assays.Results: Sinapine was the predominant phenolic in the canola meal crude extract, while canolol was the only significant phenol in the accelerated solvent extract of canola meal.The deodistillate did not have canolol or sinapine present, but contained high molecular weight phenols of unknown identity.The "test tube" antioxidant assays indicated that canola meal crude extract and sinapic acid both exhibited stronger antioxidant potentials compared to the other extracts.A dose dependent cyto-protection effect was observed under oxidative challenge by H 2 O 2 , when cells were incubated with the canola meal accelerated solvent extract, deodistillate extract and sinapic acid.Conclusions: This study demonstrates that canola by-products can be the sources of health promoting phenols, for possible formulation into value added nutraceuticals.

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 score0.007
Threshold uncertainty score0.569

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.014
GPT teacher head0.259
Teacher spread0.246 · 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
Published2019
Admission routes1
Has abstractyes

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