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Record W2079526379 · doi:10.1002/jbm.b.30776

Antimicrobial properties of nanocrystalline tetracalcium phosphate cements

2007· article· en· W2079526379 on OpenAlexaff
Uwe Gbureck, Oliver Knappe, Norbert Hofmann, Jake E. Barralet

Bibliographic record

VenueJournal of Biomedical Materials Research Part B Applied Biomaterials · 2007
Typearticle
Languageen
FieldDentistry
TopicEndodontics and Root Canal Treatments
Canadian institutionsMcGill University
Fundersnot available
KeywordsNanocrystalline materialAntimicrobialPhosphateChemistryMaterials scienceNanotechnologyOrganic chemistry

Abstract

fetched live from OpenAlex

The antimicrobial properties of cements prepared from mechanically activated tetracalcium phosphate (maTTCP) were tested with the agar diffusion test using Streptococcus salivarius, Staphylococcus epidermidis, and a clinically isolated plaque mixture. All maTTCP cements showed a significantly higher antimicrobial potency as revealed by inhibition zones of approximately 3-5 mm width, compared with a commercial Ca(OH)(2)//salicylate cement which only produced small inhibition zones around the cement specimens of 1.5 mm or less. This behavior was explained by the formation of amorphous Ca(OH)(2) during setting of maTTCP cements, which is thought to have a higher solubility and may release more OH(-) ions than conventional Ca(OH)(2)//salicylate cements. In fact, the pH value in the agar gel around the specimens was higher in the case of maTTCP cements (7.8-8.7) compared with the Ca(OH)(2)//salicylate control (7.0-8.0). The maTTCP cements did not affect the photoactivation of resin-based composites, and their antimicrobial activity is making them interesting candidates for the use as pulp-capping agents, endodontic sealers, or cavity liners in dentistry.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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)0.0010.000

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 head0.080
GPT teacher head0.361
Teacher spread0.281 · 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 source (direct Gemma or distilled Codex), 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

Citations23
Published2007
Admission routes1
Has abstractyes

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Same venueJournal of Biomedical Materials Research Part B Applied BiomaterialsSame topicEndodontics and Root Canal TreatmentsFrench-language works237,207