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Record W3006641869 · doi:10.3390/jcm9020517

Management of Streptococcus mutans-Candida spp. Oral Biofilms’ Infections: Paving the Way for Effective Clinical Interventions

2020· review· en· W3006641869 on OpenAlexaff
Bahare Salehi, Dorota Kręgiel, Gail B. Mahady, Javad Sharifi‐Rad, Natália Martins, Célia F. Rodrigues

Bibliographic record

VenueJournal of Clinical Medicine · 2020
Typereview
Languageen
FieldDentistry
TopicOral microbiology and periodontitis research
Canadian institutionsUniversity of Winnipeg
FundersFundação para a Ciência e a TecnologiaMinistério da Ciência, Tecnologia e Ensino Superior
KeywordsMedicineBiofilmStreptococcus mutansOral microbiologyCandida albicansMicrobiologyIntensive care medicineDentistryBacteriaBiology

Abstract

fetched live from OpenAlex

Oral diseases are considered the most common noncommunicable diseases and are related to serious local and systemic disorders. Oral pathogens can grow and spread in the oral mucosae and frequently in biomaterials (e.g., dentures or prostheses) under polymicrobial biofilms, leading to several disorders such as dental caries and periodontal disease. Biofilms harbor a complex array of interacting microbes, increasingly unapproachable to antimicrobials and with dynamic processes key to disease pathogenicity, which partially explain the gradual loss of response towards conventional therapeutic regimens. New drugs (synthesized and natural) and other therapies that have revealed promising results for the treatment or control of these mixed biofilms are presented and discussed here. A structured search of bibliographic databases was applied to include recent research. There are several promising new approaches in the treatment of Candida spp.–Streptococcus mutans oral mixed biofilms that could be clinically applied in the near future. These findings confirm the importance of developing effective therapies for oral Candida–bacterial infections.

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)0.011
metaresearch head score (Gemma)0.009
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.960
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0110.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0050.004
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0010.000

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 head0.237
GPT teacher head0.565
Teacher spread0.328 · 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 designOther design
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

Citations53
Published2020
Admission routes1
Has abstractyes

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