MétaCan
Menu
Back to cohort
Record W1998750830 · doi:10.1166/jbn.2013.1680

Biocompatibility Testing of Single-Walled Carbon Nanotubes on Murine Preosteoblasts: Higher Osteoblastic Differentiation with BMP-9 Than with BMP-2

2013· article· en· W1998750830 on OpenAlexafffund
Yasaman Alinejad, Olivier Drevelle, Alex Daviau, Nathalie Faucheux, Gervais Soucy

Bibliographic record

VenueJournal of Biomedical Nanotechnology · 2013
Typearticle
Languageen
FieldEngineering
TopicBone Tissue Engineering Materials
Canadian institutionsUniversité de Sherbrooke
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsBone morphogenetic protein 2Alkaline phosphataseSMADBone morphogenetic proteinMesenchymal stem cellBone morphogenetic protein 7Carbon nanotubeChemistryMaterials scienceCell growthCell biologyBiophysicsNanotechnologyBiochemistryEnzymeSignal transductionBiologyIn vitro

Abstract

fetched live from OpenAlex

Carbon nanotubes (CNTs) have been used in orthopaedic applications because of their exceptional mechanical properties. However, the influence of CNTs on the behaviour of bone-forming cells and on the ability of these cells to respond to growth factors, such as bone morphogenetic proteins (BMPs), remains poorly known. Therefore, in the present study, single-walled CNTs (SWCNTs) were synthesised using an induction thermal plasma process and purified using a multistep procedure. The impact of these purified SWCNTs on the Smad activation, cell proliferation and differentiation, with or without BMP-2 and BMP-9 (1.92 nM), was also studied using western blot, mitochondrial enzymatic activity, TUNEL, RT-PCR and alkaline phosphatase activity analyses. Pre-treatment of MC3T3-E1 preosteoblasts with SWCNTs accelerated the Smad1/5/8 activation, induced by both BMP-2 and BMP-9, within 15 min. It also slightly affected their proliferation at 48 h without apoptosis. Interestingly, at 72 h, BMP-9 favoured the differentiation of MC3T3-E1 preosteoblasts pretreated with SWCNTs to a larger extent than BMP-2 did. Therefore, the combination of BMP-9 with SWCNTs appears to be a promising avenue for bone applications.

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 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 score0.050
Threshold uncertainty score0.914

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.186
Teacher spread0.177 · 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

Citations1
Published2013
Admission routes2
Has abstractyes

Explore more

Same venueJournal of Biomedical NanotechnologySame topicBone Tissue Engineering MaterialsFrench-language works237,207