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A Flexible Biosensing Platform for High-Throughput Measurement of Cardiomyocyte Contractility

2023· article· en· W4322731533 on OpenAlexaff
Wenkun Dou, Jason T. Maynes, Yu Sun

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldNeuroscience
TopicNeuroscience and Neural Engineering
Canadian institutionsHospital for Sick ChildrenUniversity of Toronto
FundersHealth Research
KeywordsContractilityBiomedical engineeringBiosensorThroughputMaterials scienceHigh-throughput screeningNanotechnologyChemistryComputer scienceCardiologyMedicineWirelessBiochemistry

Abstract

fetched live from OpenAlex

Contractile force generated by cardiomyocyte beatings is critical for pumping oxygenated blood supply from the heart to other organs. Measuring cardiomyocyte contractility is critical in exploring cardiac disease mechanisms and quantifying drug efficacy. This paper reports a novel biosensing platform that is integrated with an ultrathin membrane array, flexible carbon black (CB)-PDMS strain sensors, and carbon fiber electrodes for continuous and high-throughput measurement of contractility, beating rate, and beating rhythm in a monolayer of human induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs). The flexible biosensing array has been utilized to conduct high-throughput measurement of cardiomyocyte contractile function in responses to cardiac drug candidates.

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)0.001
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 score0.009
Threshold uncertainty score0.441

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 studies0.0000.000
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 head0.144
GPT teacher head0.306
Teacher spread0.162 · 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

Citations0
Published2023
Admission routes1
Has abstractyes

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