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Record W2012190419 · doi:10.1111/2049-632x.12168

Promising results of cranberry in the prevention of oral<i>Candida</i>biofilms

2014· article· en· W2012190419 on OpenAlexaff
Marion Girardot, Amandine Guerineau, Leslie Boudesocque‐Delaye, Damien Costa, Laurent Bazinet, Cécile Enguehard‐Gueiffier, Christine Imbert

Bibliographic record

VenuePathogens and Disease · 2014
Typearticle
Languageen
FieldMedicine
TopicAntifungal resistance and susceptibility
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsCandida albicansCandida glabrataCRANBERRY JUICEProanthocyanidinBiofilmFood sciencePhytochemicalChemistryMicrobiologyContext (archaeology)Grape seed extractVaccinium myrtillusCorpus albicansBiologyBotanyPolyphenolBacteriaBiochemistryAntioxidant

Abstract

fetched live from OpenAlex

In the context of dental caries prevention by natural foodstuff sources, antifungal and antibiofilm activities of dry commercial extracts of cranberry fruit (Vaccinium macrocarpon Aiton) and two other red fruits (Vaccinium myrtillus L. and Malpighia punicifolia L.) were assessed on Candida albicans and Candida glabrata yeasts. When added to the culture medium, the cranberry extract displayed a significant anti-adhesion activity against Candida spp. when used at low concentrations. In addition, the pretreatment of surfaces with this extract induced an anti-adhesion activity mainly against C. glabrata yeasts and an antibiofilm activity against C. albicans. This activity was dependent on concentration, species, and strain. A phytochemical investigation bioguided by anti-adhesion tests against the two Candida species was carried out on crude cranberry juice to determine the active fractions. Three subfractions enriched in proanthocyanidins showed an anti-adhesion activity at low concentrations. This study investigated for the first time the interest of crude extracts of cranberry and cranberry juice fractions to prevent biofilms of C. glabrata. It highlighted the potency of consuming this fruit and using it as a source of anti-adhesion agents.

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)0.001
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.151
Threshold uncertainty score0.149

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.019
GPT teacher head0.286
Teacher spread0.267 · 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 designObservational
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

Citations30
Published2014
Admission routes1
Has abstractyes

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