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Record W4392044739 · doi:10.1186/s12903-024-04042-x

In vitro efficacy of Er:YAG laser-activated irrigation versus passive ultrasonic irrigation and sonic-powered irrigation for treating multispecies biofilms in artificial grooves and dentinal tubules: an SEM and CLSM study

2024· article· en· W4392044739 on OpenAlexaff
Pingping Bao, He Liu, Lan Yang, Lulu Zhang, Liwei Yang, Nannan Xiao, Jing Shen, Jiayin Deng, Ya Shen

Bibliographic record

VenueBMC Oral Health · 2024
Typearticle
Languageen
FieldDentistry
TopicEndodontics and Root Canal Treatments
Canadian institutionsUniversity of British Columbia HospitalUniversity of British Columbia
Fundersnot available
KeywordsRoot canalBiofilmBiomedical engineeringIrrigationDentinal TubuleScanning electron microscopeConfocal laser scanning microscopyMaterials scienceDentistryMedicineBiologyComposite materialBacteriaAgronomy

Abstract

fetched live from OpenAlex

BACKGROUND: Multispecies biofilms located in the anatomical intricacies of the root canal system remain the greatest challenge in root canal disinfection. The efficacy of Er:YAG laser-activated irrigation techniques for treating multispecies biofilms in these hard-to-reach areas has not been proved. The objective of this laboratory study was to evaluate the effectiveness of two Er:YAG laser-activated irrigation techniques, namely, photon-induced photoacoustic streaming (PIPS) and shock wave-enhanced emission photoacoustic streaming (SWEEPS), in treating multispecies biofilms within apical artificial grooves and dentinal tubules, in comparison with conventional needle irrigation (CNI), passive ultrasonic irrigation (PUI), and sonic-powered irrigation (EDDY). Two types of multispecies root canal biofilm models were established in combination with two assessment methods using scanning electron microscopy (SEM) and confocal laser scanning microscopy (CLSM) with the aim to obtain more meaningful results. METHODS: Ninety extracted human single-rooted premolars were chosen for two multispecies biofilm models. Each tooth was longitudinally split into two halves. In the first model, a deep narrow groove was created in the apical segment of the canal wall. After cultivating a mixed bacterial biofilm for 4 weeks, the split halves were reassembled and subjected to five irrigation techniques: CNI, PUI, EDD, PIPS, and SWEEPS. The residual biofilms inside and outside the groove in Model 1 were analyzed using SEM. For Model 2, the specimens were split longitudinally once more to evaluate the percentage of killed bacteria in the dentinal tubules across different canal sections (apical, middle, and coronal thirds) using CLSM. One-way analysis of variance and post hoc multiple comparisons were used to assess the antibiofilm efficacy of the 5 irrigation techniques. RESULTS: Robust biofilm growth was observed in all negative controls after 4 weeks. In Model 1, within each group, significantly fewer bacteria remained outside the groove than inside the groove (P < 0.05). SWEEPS, PIPS and EDDY had significantly greater biofilm removal efficacy than CNI and PUI, both from the outside and inside the groove (P < 0.05). Although SWEEPS was more effective than both PIPS and EDDY at removing biofilms inside the groove (P < 0.05), there were no significant differences among these methods outside the groove (P > 0.05). In Model 2, SWEEPS and EDDY exhibited superior bacterial killing efficacy within the dentinal tubules, followed by PIPS, PUI, and CNI (P < 0.05). CONCLUSION: Er:YAG laser-activated irrigation techniques, along with EDDY, demonstrated significant antibiofilm efficacy in apical artificial grooves and dentinal tubules, areas that are typically challenging to access.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.378
Threshold uncertainty score0.979

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 head0.065
GPT teacher head0.374
Teacher spread0.310 · 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 designObservational
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

Citations27
Published2024
Admission routes1
Has abstractyes

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