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Record W2996325746 · doi:10.1021/acsphotonics.9b01152

Marker-Free Automatic Quantification of Drug-Treated Cardiomyocytes with Digital Holographic Imaging

2019· article· en· W2996325746 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueACS Photonics · 2019
Typearticle
Languageen
FieldPhysics and Astronomy
TopicDigital Holography and Microscopy
Canadian institutionsCentre intégré universitaire de santé et de services sociaux de la Capitale-NationaleUniversité LavalCentres Intégré Universitaires de Santé et de Services Sociaux
FundersNational Research Foundation of KoreaSchweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung
KeywordsInduced pluripotent stem cellPhase imagingDrugBiomedical engineeringDrug discoveryBradycardiaComputer scienceMaterials scienceChemistryMicroscopyMedicinePharmacologyBiologyBioinformaticsInternal medicinePhysicsOptics

Abstract

fetched live from OpenAlex

We use quantitative phase digital holographic microscopy (QP-DHM) to image and quantify the beating movement of cardiomyocytes, derived from induced pluripotent stem cells (iPSCs), in control and drug-treated conditions. The development of an analysis algorithm has allowed extracting from the recorded quantitative phase signal (QPS) a set of several parameters that can efficiently characterize the cardiomyocytes beating patterns. Based on this approach, we monitored the effects of E-4031 (a class III antiarrhythmic drug) and isoprenaline (a common medication for bradycardia and heart block problems) on the cardiomyocyte beating patterns. Our results show that some effects specific to the mode of action of the drugs used can be identified. This stresses that QP-DHM can represent a promising label-free approach to identify new drug candidates by measuring their effects on iPSC-derived cardiomyocytes.

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.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.620
Threshold uncertainty score0.608

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.004
GPT teacher head0.204
Teacher spread0.200 · 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