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Record W4321765987 · doi:10.1088/1748-605x/acbf16

<i>In vitro</i> cellular biocompatibility and <i>in vivo</i> degradation behavior of calcium phosphate-coated ZK60 magnesium alloy

2023· article· en· W4321765987 on OpenAlexaff
Le Thi Trang, Hai Van Le, Sachiko Hiromoto, O Minho, Equo Kobayashi, Nam Viet Nguyen

Bibliographic record

VenueBiomedical Materials · 2023
Typearticle
Languageen
FieldMaterials Science
TopicMagnesium Alloys: Properties and Applications
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsBiocompatibilityMaterials scienceCorrosionCoatingMagnesium alloyAlloyMagnesiumDissolutionSimulated body fluidCalciumLayer (electronics)MetallurgyChemical engineeringComposite materialScanning electron microscope

Abstract

fetched live from OpenAlex

Abstract Calcium phosphate (Ca-P) surface coating is a simple but effective way to enhance both corrosion resistance and biocompatibility of ZK60 magnesium alloy. However, cell compatibility on different Ca-P layers coated on ZK60 alloy has seldom been investigated. In this study, the effects of type, morphology and corrosion protection of several Ca-P coatings formed at pH 6.5, 7.8 and 10.2 on cell behavior were examined by using an osteoblastic cell line MC3T3-E1. Furthermore, in vivo behavior in rabbits of the alloy coated with the optimum Ca-P layer was also studied. It was found that the surface factors governed the cell morphology and density. The coating morphology plays a dominant role in these surface factors. The sample coated at pH 7.8 showed the best cellular biocompatibility, suggesting that the hydroxyapatite (HAp) layer formed at pH 7.8 was the optimum coating. In rabbits, this optimum coating enhanced remarkably the corrosion resistance of the alloy. During implantation, the outermost crystals of the HAp coating were shortened and thinned due to the dissolution of HAp caused by the body fluid of the rabbits. It is indicated that ZK60 alloy coated at pH 7.8 can be applied as a biodegradable implant.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
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 score0.003
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)0.0010.000

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 head0.023
GPT teacher head0.262
Teacher spread0.239 · 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

Citations13
Published2023
Admission routes1
Has abstractyes

Explore more

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