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Record W2293011441 · doi:10.1039/c5nr07059g

Graphene induces spontaneous cardiac differentiation in embryoid bodies

2016· article· en· W2293011441 on OpenAlexaff
Samad Ahadian, Yuanshu Zhou, Shukuyo Yamada, Mehdi Estili, Xiaobin Liang, K. Nakajima, Hitoshi Shiku, Tomokazu Matsue

Bibliographic record

VenueNanoscale · 2016
Typearticle
Languageen
FieldEngineering
TopicGraphene and Nanomaterials Applications
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsEmbryoid bodyGrapheneStimulationCellular differentiationEmbryonic stem cellCell biologyStem cellBiophysicsChemistryMaterials scienceNanotechnologyBiologyGeneBiochemistryEndocrinologyAdult stem cell

Abstract

fetched live from OpenAlex

Graphene was embedded into the structure of mouse embryoid bodies (EBs) using the hanging drop technique. The inclusion of 0.2 mg per mL graphene in the EBs did not affect the viability of the stem cells. However, the graphene decreased the stem cell proliferation, probably by accelerating cell differentiation. The graphene also enhanced the mechanical properties and electrical conductivity of the EBs. Interestingly, the cardiac differentiation of the EB-graphene was significantly greater than that of the EBs at day 5 of culture, as confirmed by high-throughput gene analysis. Electrical stimulation (voltage, 4 V; frequency, 1 Hz; and duration, 10 ms for 2 continuous days) further enhanced the cardiac differentiation of the EBs, as demonstrated by analyses of the cardiac protein and gene expression and the beating activity of the EBs. Taken together, the results demonstrated that graphene played a major role in directing the cardiac differentiation of EBs, which has potential cell therapy and tissue regeneration 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 score0.217
Threshold uncertainty score0.319

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.007
GPT teacher head0.197
Teacher spread0.190 · 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

Citations45
Published2016
Admission routes1
Has abstractyes

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