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Record W4388043205 · doi:10.3897/folmed.65.e90085

Evaluation of the flexural strength of orthodontic acrylic resin incorporated with propolis nanoparticles: an in vitro study

2023· article· en· W4388043205 on OpenAlexaff
Azam Akhavan, Sepideh Arab, Negin Eslamiamirabadi, Ahmad Sodagar, Fatemeh Safari

Bibliographic record

VenueFolia Medica · 2023
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicBee Products Chemical Analysis
Canadian institutionsMcGill University
Fundersnot available
KeywordsFlexural strengthCrossheadUniversal testing machineAcrylic resinMaterials scienceComposite materialDentistryMedicineUltimate tensile strength

Abstract

fetched live from OpenAlex

Aim : Nanopropolis has become the subject of interest in medicine and dentistry as a natural product due to its outstanding properties, particularly antimicrobial activity. This study aimed at investigating the effect of nanopropolis on flexural strength of polymethyl methacrylate (PMMA). Materials and methods : Three groups of two acrylic resin brands namely Acropars and Triplex containing 0 (control group), 0.5%, and 1% of nanopropolis were prepared in 64×10.0×3.3 mm according to ISO 20795-2 (2013). Fifteen samples were allocated to each concentration. Flexural strength was determined following immersion in water and incubation at 37°C for 50±2 hours using a universal testing machine at a crosshead speed of 5±1 mm/min. Data were analyzed using ANOVA, Tukey HSD, and t -test. P <0.05 was set as statistical significance. Results : Control groups of Acropars and Triplex showed the highest mean flexural strength within their own group which both were higher than the recommended 50 MPa. The mean flexural strength of Triplex incorporated with 0.5 and 1% of nanopropolis was higher than that of Acropars with the same percentage. Conclusions : The mean flexural strength of Triplex remained above the recommended value of 50 MPa after incorporation of both 0.5 and 1% nanopropolis. However, that of Acropars dropped below it.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.447
Threshold uncertainty score0.208

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
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 head0.056
GPT teacher head0.284
Teacher spread0.228 · 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

Citations0
Published2023
Admission routes1
Has abstractyes

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