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Record W4411412999 · doi:10.1021/acsomega.5c02108

Unlocking Histatin Potential against <i>Candida albicans</i> and <i>Streptococcus mutans</i> Biofilms: Targeting the Extracellular Matrix While Preserving Oral Cell Integrity

2025· article· en· W4411412999 on OpenAlexafffund
Luana Mendonça Dias, Lina M. Marin, Ana Cláudia Pavarina, Walter L. Siqueira

Bibliographic record

VenueACS Omega · 2025
Typearticle
Languageen
FieldImmunology and Microbiology
TopicAntimicrobial Peptides and Activities
Canadian institutionsUniversity of Saskatchewan
FundersCanadian Institutes of Health ResearchConselho Nacional de Desenvolvimento Científico e TecnológicoNatural Sciences and Engineering Research Council of CanadaCoordenação de Aperfeiçoamento de Pessoal de Nível SuperiorFundação de Amparo à Pesquisa do Estado de São Paulo
KeywordsStreptococcus mutansBiofilmCandida albicansMicrobiologyExtracellular matrixChemistryBiologyBacteriaBiochemistry

Abstract

fetched live from OpenAlex

, which persist the treatments because of their resilient extracellular matrix (ECM). This study tested four proteins/peptideshistatin 3 (His3), histatin 5 (His5), DR9-RR14, and RR14on these mixed biofilms grown on acrylic resin. Using previously determined biofilm inhibitory concentrations (BIC-2), their effects on biofilm viability, ECM components (proteins, extracellular DNA, and polysaccharides), and biofilm structure were assessed. His3 and His5 were the most effective, reducing biofilm cells by 46 and 41% and significantly decreasing ECM components. DR9-RR14 and RR14 had moderate effects. Imaging confirmed that His3 and His5 disrupted the biofilm structure. Cytotoxicity tests showed that all proteins/peptides were safe for gingival fibroblasts. Among the proteins/peptides evaluated, His3 showed the highest effectiveness, making it a promising candidate for preventing biofilm formation and ECM maturation, suggesting its potential use in treating denture stomatitis.

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 score0.277
Threshold uncertainty score0.989

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 studies0.0010.000
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 head0.008
GPT teacher head0.228
Teacher spread0.219 · 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

Citations2
Published2025
Admission routes2
Has abstractyes

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