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Record W3011591276 · doi:10.1016/j.jff.2020.103906

Fruits and leaves from wild blueberry plants contain diverse polyphenols and decrease neuroinflammatory responses in microglia

2020· article· en· W3011591276 on OpenAlexafffundabout
Michelle Debnath-Canning, Scott Unruh, Poorva Vyas, Noriko Daneshtalab, Abir U. Igamberdiev, John T. Weber

Bibliographic record

VenueJournal of Functional Foods · 2020
Typearticle
Languageen
FieldNeuroscience
TopicNeuroinflammation and Neurodegeneration Mechanisms
Canadian institutionsMemorial University of Newfoundland
FundersNatural Sciences and Engineering Research Council of CanadaCanada Foundation for Innovation
KeywordsMicrogliaPolyphenolBiologyBotanyNeuroinflammationInflammationImmunologyBiochemistryAntioxidant

Abstract

fetched live from OpenAlex

For the treatment of neurological disorders, polyphenols in Vaccinium berry species may be an effective addition to standard medicinal products. Polyphenols may reduce oxidative stress and inflammation, processes believed to contribute to disorders such as Parkinson’s disease. We performed an analysis of polyphenol content, biochemical attributes and neurobiological activity of extracts from wild blueberries native to Newfoundland and Labrador. Fruits and leaves of samples contained several polyphenolic compounds, such as anthocyanins, and demonstrated high antioxidant capacity. Cell cultures of microglia, the innate immune cells of the brain, were exposed to glutamate or α-synuclein in order to induce inflammatory responses, which decreased the amount of cells after 24 h. Overall, treatment of cells with fruit or leaf extracts inhibited cell death and decreased morphological criteria associated with inflammation. These results suggest that dietary intake of blueberry fruits and leaves or supplements may be protective against neurodegenerative disorders that include a neuroinflammatory component.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

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)0.0020.000

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 head0.064
GPT teacher head0.253
Teacher spread0.190 · 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 source (direct Gemma or distilled Codex), 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

Citations62
Published2020
Admission routes3
Has abstractyes

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