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Record W3048130904 · doi:10.1016/j.stemcr.2020.07.006

Single-Cell Mechanical Analysis of Human Pluripotent Stem Cell-Derived Cardiomyocytes for Drug Testing and Pathophysiological Studies

2020· article· en· W3048130904 on OpenAlexaff
Nimer Ballan, Naim Shaheen, Gordon Keller, Lior Gepstein

Bibliographic record

VenueStem Cell Reports · 2020
Typearticle
Languageen
FieldEngineering
Topic3D Printing in Biomedical Research
Canadian institutionsPrincess Margaret Cancer CentreUniversity of TorontoOntario Institute for Cancer Research
FundersEuropean Research CouncilIsrael Science Foundation
KeywordsContractilityInotropeInduced pluripotent stem cellVerapamilDoxorubicinBiologyPharmacologyStem cellLusitropyDrug discoveryDrugCell biologyInternal medicineDiastoleMedicineBioinformaticsEndocrinologyChemotherapyCalciumEmbryonic stem cellBiochemistry

Abstract

fetched live from OpenAlex

Current platforms for studying the mechanical properties of human pluripotent stem cell-derived cardiomyocytes (hPSC-CMs) as single cells do not measure forces directly, require numerous assumptions, and cannot study cell mechanics at different loading conditions. We present a method for directly measuring the active and passive forces generated by single-cell hPSC-CMs at different stretch levels. Utilizing this technique, single hPSC-CMs exhibited positive length-tension relationship and appropriate inotropic, klinotropic, and lusitropic changes in response to pharmacological treatments (isoproterenol and verapamil). The unique potential of the approach for drug testing and disease modeling was exemplified by doxorubicin and omecamtiv mecarbil drug studies revealing their known actions to suppress (doxorubicin) or augment (omecamtiv mecarbil at low dose) cardiomyocyte contractility, respectively. Finally, mechanistic insights were gained regarding the cellular effects of these drugs as doxorubicin treatment led to cellular mechanical alternans and high doses of omecamtiv mecarbil suppressed contractility and worsened the cellular diastolic properties.

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 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 score0.030
Threshold uncertainty score0.927

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
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 head0.070
GPT teacher head0.276
Teacher spread0.206 · 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

Citations20
Published2020
Admission routes1
Has abstractyes

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