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Record W2804327040 · doi:10.1126/sciadv.aat0351

Graphene biointerfaces for optical stimulation of cells

2018· article· en· W2804327040 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueScience Advances · 2018
Typearticle
Languageen
FieldNeuroscience
TopicNeuroscience and Neural Engineering
Canadian institutionsStemcell Technologies
FundersNational Heart, Lung, and Blood InstituteNational Center for Advancing Translational SciencesAmerican Heart AssociationNational Science Foundation
KeywordsGrapheneStimulationMaterials scienceNanotechnologyOptoelectronicsNeuroscienceBiology

Abstract

fetched live from OpenAlex

Noninvasive stimulation of cells is crucial for the accurate examination and control of their function both at the cellular and the system levels. To address this need, we present a pioneering optical stimulation platform that does not require genetic modification of cells but instead capitalizes on unique optoelectronic properties of graphene, including its ability to efficiently convert light into electricity. We report the first studies of optical stimulation of cardiomyocytes via graphene-based biointerfaces (G-biointerfaces) in substrate-based and dispersible configurations. The efficiency of stimulation via G-biointerfaces is independent of light wavelength but can be tuned by changing the light intensity. We demonstrate that an all-optical evaluation of use-dependent drug effects in vitro can be enabled using substrate-based G-biointerfaces. Furthermore, using dispersible G-biointerfaces in vivo, we perform optical modulation of the heart activity in zebrafish embryos. Our discovery is expected to empower numerous fundamental and translational biomedical studies.

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.

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)0.001
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 score0.008
Threshold uncertainty score0.697

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.002
Scholarly communication0.0000.001
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 head0.034
GPT teacher head0.322
Teacher spread0.288 · 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