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Nano sized gallium oxide surface features for enhanced antimicrobial and osteo-integrative responses

2023· article· en· W4378473446 on OpenAlexaff
Bríd Murphy, Carolina Martins, Mimma Maggio, David A. Hoey

Bibliographic record

VenueColloids and Surfaces B Biointerfaces · 2023
Typearticle
Languageen
FieldEngineering
TopicBone Tissue Engineering Materials
Canadian institutionsTrinity College
Fundersnot available
KeywordsGalliumOsseointegrationNanodotMaterials scienceNanotechnologyGallium 67 scanImplantSurgeryMedicine

Abstract

fetched live from OpenAlex

Gallium oxide has known beneficial osteo-integrative properties. This may have importance for improving the osteointegration of orthopedic implants. At high concentrations gallium is cytotoxic. Therefore, integration of gallium into implant devices must be carefully controlled to limit its concentration and release. A strategy based on surface doping of gallium although challenging seems an appropriate approach to limit dose amounts to minimize cytotoxicity and maximize osteointegration benefits. In this work we develop a novel form of patterned surface doping via a block copolymer-based surface chemistry that enables very low gallium content but enhanced osteointegration as proven by comprehensive bioassays. Polystyrene-b-poly 4vinyl pyridine (PS-b-P4VP) BCP (block copolymer) films were produced on surfaces. Selective infiltration of the BCP pattern with a gallium salt precursor solution and subsequent UV-ozone treatment produced a surface pattern of gallium oxide nanodots as evidenced by atomic force and scanning electron microscopy. A comprehensive study of the bioactivity was carried out, including antimicrobial and sterility testing, gallium ion release kinetics and the interaction with human marrow mesenchymal stomal cells and mononuclear cells. Comparing the data from osteogenesis media assay tests with osteoclastogenesis tests demonstrated the potential for the gallium oxide nanodot doping to improve osteointegration properties of a surface.

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)0.000
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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.009
GPT teacher head0.233
Teacher spread0.224 · 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

Citations4
Published2023
Admission routes1
Has abstractyes

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