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Record W2000358520 · doi:10.1139/o05-063

Blueberry flavonoids inhibit matrix metalloproteinase activity in DU145 human prostate cancer cells

2005· article· en· W2000358520 on OpenAlexaffvenue
Michael D Matchett, Shawna L. MacKinnon, Marva I. Sweeney, Katherine Gottschall‐Pass, Robert A. R. Hurta

Bibliographic record

VenueBiochemistry and Cell Biology · 2005
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicProtease and Inhibitor Mechanisms
Canadian institutionsUniversity of Prince Edward Island
Fundersnot available
KeywordsDU145Matrix metalloproteinaseProstate cancerCancerMatrix metalloproteinase 9MetalloproteinaseProstateChemistryCancer researchMatrix metalloproteinase inhibitorBiologyTraditional medicineMedicineBiochemistryInternal medicineLNCaP

Abstract

fetched live from OpenAlex

Regulation of the matrix metalloproteinases (MMPs), the major mediators of extracellular matrix (ECM) degradation, is crucial to regulate ECM proteolysis, which is important in metastasis. This study examined the effects of 3 flavonoid-enriched fractions (a crude fraction, an anthocyanin-enriched fraction, and a proanthocyanidin-enriched fraction), which were prepared from lowbush blueberries (Vaccinium angustifolium), on MMP activity in DU145 human prostate cancer cells in vitro. Using gelatin gel electrophoresis, MMP activity was evaluated from cells after 24-hr exposure to blueberry fractions. All fractions elicited an ability to decrease the activity of MMP-2 and MMP-9. Of the fractions tested, the proanthocyanidin-enriched fraction was found to be the most effective at inhibiting MMP activity in these cells. No induction of either necrotic or apoptotic cell death was noted in these cells in response to treatment with the blueberry fractions. These findings indicate that flavonoids from blueberry possess the ability to effectively decrease MMP activity, which may decrease overall ECM degradation. This ability may be important in controlling tumor metastasis formation.

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 categoriesMeta-epidemiology (narrow)
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 score0.017
Threshold uncertainty score1.000

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 integrity0.0000.000
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 head0.006
GPT teacher head0.249
Teacher spread0.243 · 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.

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

Citations82
Published2005
Admission routes2
Has abstractyes

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