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Record W2584440576 · doi:10.1002/jbm.b.33856

The impact of gallium content on degradation, bioactivity, and antibacterial potency of zinc borate bioactive glass

2017· article· en· W2584440576 on OpenAlexaff
Alireza Rahimnejad Yazdi, Lawrence Torkan, Wendy Stone, Mark R. Towler

Bibliographic record

VenueJournal of Biomedical Materials Research Part B Applied Biomaterials · 2017
Typearticle
Languageen
FieldEngineering
TopicBone Tissue Engineering Materials
Canadian institutionsUniversity of OttawaToronto Metropolitan UniversitySt. Michael's Hospital
Fundersnot available
KeywordsGalliumZincAmorphous solidNuclear chemistryAntibacterial activityBoronMaterials scienceChemistryMetallurgyBacteriaOrganic chemistry

Abstract

fetched live from OpenAlex

Abstract Zinc borate glasses with increasing gallium content (0, 2.5, 5, 10, and 15 Wt % Ga) were synthesized and their degradation, bioactivity in simulated body fluid (SBF), and antibacterial properties were investigated. ICP measurements showed that increased gallium content in the glass resulted in increased gallium ion release and decreased release of other ions. Degradability declined with the addition of gallium, indicating the formation of more symmetric BO3 units with three bridging oxygens and asymmetric BO3 units with two bridging oxygens in the glass network as the gallium content in the series increased. The formation of amorphous CaP on the glass surface after 24 h of incubation in SBF was confirmed by SEM, XRD, and FTIR analyses. Finally, antibacterial evaluation of the glasses using the agar disc‐diffusion method demonstrated that the addition of gallium increased the antibacterial potency of the glasses against P. aeruginosa (Gram‐negative) while decreasing it against S. epidermidis (Gram‐positive); considering the ion release trends, this indicates that the gallium ion is responsible for the glasses’ antibacterial behavior against P. aeruginosa while the zinc ion controls the antibacterial activity against S. epidermidis. The statistical significance of the observed trends in the measurements were confirmed by applying the Kruskal‐Wallis H Test. © 2017 Wiley Periodicals, Inc. J Biomed Mater Res Part B: Appl Biomater, 106B: 367–376, 2018.

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)0.000
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 score0.002
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

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 head0.342
Teacher spread0.278 · 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

Citations43
Published2017
Admission routes1
Has abstractyes

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