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Record W3199823805 · doi:10.18433/jpps32084

Dual-Light Photodynamic Therapy Effectively Eliminates Streptococcus Oralis Biofilms

2021· article· en· W3199823805 on OpenAlexvenueno aff
Jessica Hentilä, Noora Laakamaa, Timo Sorsa, Jukka H. Meurman, Hanna Välimaa, Sakari Nikinmaa, Esko Kankuri, Tuomas Tauriainen, Tommi Pätilä

Bibliographic record

VenueJournal of Pharmacy & Pharmaceutical Sciences · 2021
Typearticle
Languageen
FieldMedicine
TopicPhotodynamic Therapy Research Studies
Canadian institutionsnot available
Fundersnot available
KeywordsStreptococcus oralisStreptococcus gordoniiMucositisPhotodynamic therapyMedicineDisinfectantBiofilmAntimicrobialMicrobiologyStreptococcus mutansStreptococcus sanguinisChemistryRadiation therapyInternal medicineBiologyAntibioticsBacteriaPathologyStreptococcaceae

Abstract

fetched live from OpenAlex

PURPOSE: During cancer treatment, oral mucositis due to radiotherapy or chemotherapy often leads to disruption of the oral mucosa, enabling microbes to invade bloodstream. Viridans streptococcal species are part of the healthy oral microbiota but can be frequently isolated from the blood of neutropenic patients. We have previously shown the antibacterial efficacy of dual-light, the combination of antibacterial blue light (aBL) and indocyanine green photodynamic therapy (aPDT). METHODS: Here, we investigated the dual-light antibacterial action against four-day Streptococcus oralis biofilm. In addition, while keeping the total radiant exposure constant at 100J/cm2, we investigated the effect of changing the different relative light energies of aBL and aPDT to the antibacterial potential. RESULTS: The dual-light had a significant antibacterial effect in all the tested combinations. CONCLUSION: Dual-light can be used as an effective disinfectant against S. oralis biofilm.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
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 score0.095
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.064
GPT teacher head0.431
Teacher spread0.367 · 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 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

Citations12
Published2021
Admission routes1
Has abstractyes

Explore more

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