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Record W2082221617 · doi:10.1021/jf203304v

Cranberry Proanthocyanidins: Natural Weapons against Periodontal Diseases

2011· review· en· W2082221617 on OpenAlexafffund
Karine Feghali, Mark Feldman, Vu Dang La, Juliana Alves dos Santos, Daniel Grenier

Bibliographic record

VenueJournal of Agricultural and Food Chemistry · 2011
Typereview
Languageen
FieldDentistry
TopicOral microbiology and periodontitis research
Canadian institutionsUniversité Laval
FundersCanadian Institutes of Health Research
KeywordsProanthocyanidinCRANBERRY JUICEGrape seed extractAntimicrobialVacciniumPeriodontitisBiologyChemistryAntioxidantMedicineMicrobiologyBiochemistryPathologyPolyphenolBotanyDentistryInternal medicine

Abstract

fetched live from OpenAlex

Cranberry ( Vaccinium macrocarpon ) is known to have a beneficial effect on several aspects of human health. Proanthocyanidins (PACs), the most abundant flavonoids extracted from red cranberry fruits, have been reported to possess antimicrobial, antiadhesion, antioxidant, and anti-inflammatory properties. Recent in vitro studies have shown that cranberry PACs may be potential therapeutic agents for the prevention and management of periodontitis, an inflammatory disease of bacterial origin affecting tooth-supporting tissues. After presenting an overview of cranberry phytochemicals and their potential for human health benefits, this review will focus on the effects of cranberry PACs on connective tissue breakdown and alveolar bone destruction, as well as their potential for controlling periodontal diseases. Possible mechanisms of action of cranberry PACs include the inhibition of (i) bacterial and host-derived proteolytic enzymes, (ii) host inflammatory response, and (iii) osteoclast differentiation and activity. Given that cranberry PACs have shown interesting properties in in vitro studies, clinical trials are warranted to better evaluate the potential of these molecules for controlling periodontal diseases.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.918
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.002
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 head0.028
GPT teacher head0.284
Teacher spread0.256 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations116
Published2011
Admission routes2
Has abstractyes

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