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Record W4321459740 · doi:10.3390/pharmaceutics15030715

Silver Nanoparticles and Their Therapeutic Applications in Endodontics: A Narrative Review

2023· review· en· W4321459740 on OpenAlex
Farzaneh Afkhami, Parisa Forghan, James L. Gutmann, Anil Kishen

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenuePharmaceutics · 2023
Typereview
Languageen
FieldMaterials Science
TopicNanoparticles: synthesis and applications
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsRoot canalDentinal TubuleSilver nanoparticleEndodonticsDentinDentistryCytotoxicityChemistryAntimicrobialNanoparticleNanotechnologyMaterials scienceMedicine

Abstract

fetched live from OpenAlex

The efficient elimination of microorganisms and their byproducts from infected root canals is compromised by the limitations in conventional root canal disinfection strategies and antimicrobials. Silver nanoparticles (AgNPs) are advantageous for root canal disinfection, mainly due to their wide-spectrum anti-microbial activity. Compared to other commonly used nanoparticulate antibacterials, AgNPs have acceptable antibacterial properties and relatively low cytotoxicity. Owing to their nano-scale, AgNPs penetrate deeper into the complexities of the root canal systems and dentinal tubules, as well as enhancing the antibacterial properties of endodontic irrigants and sealers. AgNPs gradually increase the dentin hardness in endodontically treated teeth and promote antibacterial properties when used as a carrier for intracanal medication. The unique properties of AgNPs make them an ideal additive for different endodontic biomaterials. However, the possible side effects of AgNPs, such as cytotoxicity and tooth discoloration potential, merits further research.

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.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.985
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
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 head0.156
GPT teacher head0.419
Teacher spread0.264 · 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