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Record W1867405856 · doi:10.1002/jbm.a.35223

Investigating the solubility and cytocompatibility of CaO–Na<sub>2</sub>O–SiO<sub>2</sub>/TiO<sub>2</sub> bioactive glasses

2014· article· en· W1867405856 on OpenAlexaff
Anthony W. Wren, Aisling Y. Coughlan, Courtney M. Smith, Sarah P. Hudson, Fathima Laffir, Mark R. Towler

Bibliographic record

VenueJournal of Biomedical Materials Research Part A · 2014
Typearticle
Languageen
FieldEngineering
TopicBone Tissue Engineering Materials
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsMaterials scienceSolubilityParticle sizeTitaniumBioactive glassNuclear chemistryCalciumChemical engineeringMineralogyPopulationComposite materialMetallurgyOrganic chemistryChemistry

Abstract

fetched live from OpenAlex

This study aims to investigate the solubility of a series of titanium (TiO2 )-containing bioactive glasses and their subsequent effect on cell viability. Five glasses were synthesized in the composition range SiO2 -Na2 O-CaO with 5 mol % of increments TiO2 substituted for SiO2 . Glass solubility was investigated with respect to (1) exposed surface area, (2) particle size, (3) incubation time, and (4) compositional effects. Ion release profiles showed that sodium (Na(+) ) presented high release rates after 1 day and were unchanged between 7 and 14 days. Calcium (Ca(2+) ) release presented a significant change at each time period and was also composition dependent, where a reduction in Ca(2+) release is observed with an increase in TiO2 concentration. Silica (Si(4+) ) release did not present any clear trends while no titanium (Ti(4+) ) was released. Cell numbers were found to increase up to 44%, compared to the growing control population, with a reduction in particle size and with the inclusion of TiO2 in the glass composition.

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)0.013
metaresearch head score (Gemma)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
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 score0.014
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0130.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0010.001
Science and technology studies0.0000.002
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0000.001
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 head0.033
GPT teacher head0.293
Teacher spread0.260 · 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.

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

Citations12
Published2014
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Biomedical Materials Research Part ASame topicBone Tissue Engineering MaterialsFrench-language works237,207