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Record W4413000804 · doi:10.1186/s13104-025-07408-8

Isofloridoside: a novel inhibitor of Streptococcus mutans biofilm formation and glucosyltransferase activity

2025· article· en· W4413000804 on OpenAlexaff
Manami Kimijima, Naoki Narisawa, Yoichiro Hama, Tomoyo Nakagawa-Nakamura, Tatsuro Ito

Bibliographic record

VenueBMC Research Notes · 2025
Typearticle
Languageen
FieldDentistry
TopicOral microbiology and periodontitis research
Canadian institutionsNutrasource
Fundersnot available
KeywordsStreptococcus mutansGlucosyltransferaseBiofilmMicrobiologyChemistryMedicineBiologyBacteriaBiochemistryEnzyme

Abstract

fetched live from OpenAlex

OBJECTIVE: Isofloridoside, a galactose-containing heteroside derived from marine red algae, has potential applications as a sweetener because it can activate the sweetness receptors T1R2/T1R3. The purpose of this study was to investigate the effects of isofloridoside on the growth and sucrose-dependent biofilm formation of the cariogenic bacterium Streptococcus mutans, to evaluate its potential as a caries-preventing agent. RESULTS: The results showed that S. mutans did not grow when isofloridoside was the sole carbon source. Isofloridoside inhibited sucrose-dependent biofilm formation by S. mutans in a concentration-dependent manner, similar to galactose and glucose, but unlike melibiose and galactose-containing disaccharides. Biofilm inhibition induced by isofloridoside was associated with inhibition of glucosyltransferase activity. Isofloridoside exhibited biofilm inhibition comparable to xylitol, an alternative sugar known to inhibit biofilm formation. The differential effects of isofloridoside and melibiose on biofilm formation may result from the structural differences that affect their interactions with S. mutans enzymes. These findings highlight the potential of galactose and its polysaccharides as regulators of S. mutans biofilm formation, and suggest that isofloridoside is a promising alternative sweetener for caries prevention.

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)0.002
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 score0.049
Threshold uncertainty score0.516

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.112
GPT teacher head0.411
Teacher spread0.298 · 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

Citations1
Published2025
Admission routes1
Has abstractyes

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