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Record W1997935415 · doi:10.1586/14779072.2014.864951

See the light: can optogenetics restore healthy heartbeats? And, if it can, is it really worth the effort?

2013· review· en· W1997935415 on OpenAlexfundno aff
Patrick M. Boyle, Emilia Entcheva, Natalia A. Trayanova

Bibliographic record

VenueExpert Review of Cardiovascular Therapy · 2013
Typereview
Languageen
FieldNeuroscience
TopicNeuroscience and Neural Engineering
Canadian institutionsnot available
FundersNational Heart, Lung, and Blood InstituteNatural Sciences and Engineering Research Council of CanadaNational Institutes of HealthNational Science Foundation
KeywordsOptogeneticsNeuroscienceMedicineComputer scienceMedical physicsPsychology

Abstract

fetched live from OpenAlex

Cardiac optogenetics is an exciting new methodology in which light-sensitive ion channels are expressed in heart tissue to enable optical control of bioelectricity. This technology has the potential to open new avenues for safely and effectively treating rhythm disorders in the heart with gentle beams of light. Recently, we developed a comprehensive framework for modeling cardiac optogenetics. Simulations conducted in this platform will provide insights to guide in vitro investigation and steer the development of therapeutic applications - these are the first steps toward clinical translation. In this editorial, we review literature relevant to light-sensitive protein delivery and intracardiac illumination to provide a holistic feasibility assessment for optogenetics-based arrhythmia termination therapy. We then draw on examples from computational work to show that the optical control paradigm has undeniable advantages that cannot be attained with conventional electrotherapy. Hence, we argue that cardiac optogenetics is more than a flashy substitute for current approaches.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.970
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0030.003
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0020.000
Research integrity0.0000.001
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 head0.080
GPT teacher head0.350
Teacher spread0.271 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations18
Published2013
Admission routes1
Has abstractyes

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