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Record W2031184574 · doi:10.1371/journal.pone.0089059

Subinhibitory Concentrations of Triclosan Promote Streptococcus mutans Biofilm Formation and Adherence to Oral Epithelial Cells

2014· article· en· W2031184574 on OpenAlexafffund
Telma Blanca Lombardo Bedran, Louis Grignon, Denise Madalena Palomari Spolidório, Daniel Grenier

Bibliographic record

VenuePLoS ONE · 2014
Typearticle
Languageen
FieldImmunology and Microbiology
TopicAntimicrobial Peptides and Activities
Canadian institutionsUniversité Laval
FundersCanadian Institutes of Health Research
KeywordsTriclosanBiofilmStreptococcus mutansMicrobiologyCrystal violetAntimicrobialChemistryMinimum inhibitory concentrationBacteriaBiologyMedicine

Abstract

fetched live from OpenAlex

Triclosan is a general membrane-active agent with a broad-spectrum antimicrobial activity that is commonly used in oral care products. In this study, we investigated the effect of sub-minimum inhibitory concentrations (MICs) of triclosan on the capacity of the cariogenic bacterium Streptococcus mutans to form biofilm and adhere to oral epithelial cells. As quantified by crystal violet staining, biofilm formation by two reference strains of S. mutans was dose-dependently promoted, in the range of 2.2- to 6.2-fold, by 1/2 and 1/4 MIC of triclosan. Observations by scanning electron microscopy revealed the presence of a dense biofilm attached to the polystyrene surface. Growth of S. mutans in the presence of triclosan at sub-MICs also increased its capacity to adhere to a monolayer of gingival epithelial cells. The expression of several genes involved in adherence and biofilm formation in S. mutans was investigated by quantitative RT-PCR. It was found that sub-MICs of triclosan significantly increased the expression of comD, gtfC, and luxS, and to a lesser extent of gtfB and atlA genes. These findings stress the importance of maintaining effective bactericidal concentrations of therapeutic triclosan since sub-MICs may promote colonization of the oral cavity by S. mutans.

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 score0.002
Threshold uncertainty score0.364

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 head0.025
GPT teacher head0.218
Teacher spread0.193 · 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

Citations73
Published2014
Admission routes2
Has abstractyes

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