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Record W2743513831 · doi:10.1002/cptx.30

Use of Human Pluripotent Stem Cell Derived‐Cardiomyocytes to Study Drug‐Induced Cardiotoxicity

2017· article· en· W2743513831 on OpenAlexafffund
A. Maillet, Kim Peng Tan, Liam R. Brunham

Bibliographic record

VenueCurrent Protocols in Toxicology · 2017
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicPluripotent Stem Cells Research
Canadian institutionsSt. Paul's HospitalUniversity of British Columbia
FundersCanadian Institutes of Health ResearchBiomedical Research CouncilMinistry of Education - SingaporeNational University of Singapore
KeywordsCardiotoxicityInduced pluripotent stem cellCRISPRDrugDrug discoveryPharmacologyChemistryBiologyMedicineEmbryonic stem cellBioinformaticsGeneToxicityInternal medicineBiochemistry

Abstract

fetched live from OpenAlex

Drug-induced cardiotoxicity is the one of the most common causes of drug withdrawal from market. A major barrier in managing the risk of drug-induced cardiotoxicity has been the lack of relevant models to study cardiac safety. Human pluripotent stem cell-derived cardiomyocytes (hPSC-CMs) have great potential in drug discovery and cardiotoxcity screens as they display many characteristics of the human myocardium and offer unlimited supply. This unit describes how to use pluripotent stem cells derived cardiomyocytes to study drug-induced cardiotoxicty using doxorubicin as an example. We present a workflow that explains procedure for editing hPSC using the CRISPR/Cas9 system and for differentiation of hPSC into cardiomyocytes. We also report protocols to study drug effect on ROS production, intracellular calcium concentration, formation of DNA double strand breaks, gene expression and electrophysiological properties of hPSC-CMs. © 2017 by John Wiley & Sons, Inc.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.032
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.002
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 head0.119
GPT teacher head0.398
Teacher spread0.279 · 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 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

Citations3
Published2017
Admission routes2
Has abstractyes

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