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Record W2027655328 · doi:10.1097/fjc.0b013e31821823f5

Stem Cells for Drug Screening

2011· article· en· W2027655328 on OpenAlexaff
Rebecca R. Laposa

Bibliographic record

VenueJournal of Cardiovascular Pharmacology · 2011
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicPluripotent Stem Cells Research
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsInduced pluripotent stem cellCardiotoxicityEmbryonic stem cellStem cellDrugDrug discoveryMedicinePharmacologyHuman Induced Pluripotent Stem CellsDrug toxicityBiologyBioinformaticsToxicityCell biologyInternal medicine

Abstract

fetched live from OpenAlex

Current drug screening methods are insufficiently predictive of clinical toxicity and efficacy. Recent advances in stem cell technology have the potential to improve drug screening. For tests of cardiotoxicity and efficacy of cardioactive drugs, cardiomyocytes derived from human embryonic stem cells and/or human induced pluripotent stem cells, collectively termed human pluripotent stem cells (hPSCs), have been utilized as model alternatives to current drug screening platforms. In this review, we report on recent advances in the differentiation of hPSCs to cardiomyocytes and summarize the evidence for pharmacological responses in hPSC-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.

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)0.002
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 score0.277
Threshold uncertainty score0.568

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
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 head0.037
GPT teacher head0.283
Teacher spread0.246 · 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

Citations17
Published2011
Admission routes1
Has abstractyes

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