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Record W4413470426 · doi:10.1016/j.xpro.2025.104044

Protocol for dual-optical mapping of voltage and calcium sensors in human pluripotent stem cell-derived cardiomyocytes

2025· article· en· W4413470426 on OpenAlexafffund
Faisal J. Alibhai, Arya Masoumi, Nathan G. Kim, Juliana Gomez-Garcia, Peter Lee, Michael A. Laflamme

Bibliographic record

VenueSTAR Protocols · 2025
Typearticle
Languageen
FieldNeuroscience
TopicNeuroscience and Neural Engineering
Canadian institutionsStem Cell NetworkUniversity of WaterlooUniversity of TorontoMcMaster UniversityUniversity Health Network
FundersCanada Research ChairsUniversity of California Berkeley
KeywordsInduced pluripotent stem cellDual (grammatical number)Protocol (science)Human Induced Pluripotent Stem CellsVoltageCell biologyComputer scienceNeuroscienceBiologyElectrical engineeringEngineeringMedicineEmbryonic stem cellBiochemistryPathology

Abstract

fetched live from OpenAlex

Simultaneous acquisition of fluorescent voltage and intracellular calcium signals requires spectrally distinct indicators and a high-speed imaging setup. Here, we present a protocol for the optical mapping of these key physiological parameters in human pluripotent stem cell-derived cardiomyocyte (hPSC-CM) monolayers. We describe steps for using spectrally compatible voltage and calcium sensors involving fluorescent dyes and genetically encoded sensors. We also detail procedures for building a driver capable of controlling light-emitting diodes (LEDs) in synchrony with a high-speed camera during image acquisition.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Protocol · Consensus signal: none
Teacher disagreement score0.011
Threshold uncertainty score0.037

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0110.004

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 head0.079
GPT teacher head0.352
Teacher spread0.273 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreProtocol

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

Citations4
Published2025
Admission routes2
Has abstractyes

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