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Record W2048465298 · doi:10.1002/jbm.a.32275

Titanium crystal orientation as a tool for the improved and regulated cell attachment

2008· article· en· W2048465298 on OpenAlexafffund
Shahab Faghihi, Fereshteh Azari, Jerzy A. Szpunar, Hojatollah Vali, Maryam Tabrizian

Bibliographic record

VenueJournal of Biomedical Materials Research Part A · 2008
Typearticle
Languageen
FieldEngineering
TopicBone Tissue Engineering Materials
Canadian institutionsMcGill University
FundersCanadian Institutes of Health Research
KeywordsMaterials scienceOsseointegrationAdhesionTitaniumCell adhesionBiomaterialOsteoblastFibroblastCellNanotechnologyImplantBiophysicsBiomedical engineeringCell cultureIn vitroComposite materialChemistryMetallurgyBiologyMedicineBiochemistry

Abstract

fetched live from OpenAlex

Cell adhesion is a fundamental process that controls cell proliferation, migration, and differentiation and is crucial for biomaterial-tissue integration. Osteoblast attachment on the surfaces of implant materials is, therefore, essential for the proper function of any implant in which osseointegration is required. Although many reports are available on osteoblast attachment using different surface modification, there is no specific report, so far, that investigates the effect of atomic order of specific crystallographic orientation of substrates on cell behavior. A novel coculture system is proposed to show the differential response of preosteoblast and fibroblast cell lines to the titanium single-crystal substrates. Our investigation has shown that surface recognition by the cell is influenced by the atomic structure of the surface leading to cell-type-specific adhesion. The degree of preosteoblast attachment is significantly higher on the Ti-(1120), whereas the fibroblast adhesion is increased on the Ti-(1010). This demonstrates that the three distinct faces of titanium substrates differ greatly in their capacity to serve as cell adhesive substrates. It also provides clear evidence for the role of crystal structure in regulating and improving cell-substrate interactions relevant for the optimal function of bone implant materials.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.107
Threshold uncertainty score0.347

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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 head0.031
GPT teacher head0.315
Teacher spread0.285 · 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.

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

Citations20
Published2008
Admission routes2
Has abstractyes

Explore more

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