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Record W2993193401 · doi:10.1016/j.stemcr.2019.11.001

Transcriptomically Guided Mesendoderm Induction of Human Pluripotent Stem Cells Using a Systematically Defined Culture Scheme

2019· article· en· W2993193401 on OpenAlexafffund
Richard L. Carpenedo, Sarah Y. Kwon, Ryan Tanner, Julien Yockell‐Lelièvre, Chandarong Choey, Carole Doré, Mirabelle Ho, Duncan J. Stewart, Theodore J. Perkins, William L. Stanford

Bibliographic record

VenueStem Cell Reports · 2019
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicPluripotent Stem Cells Research
Canadian institutionsOttawa HospitalUniversity of Ottawa
FundersCanadian Institutes of Health ResearchNatural Sciences and Engineering Research Council of CanadaCanada Research Chairs
KeywordsEndodermMesodermBiologyInduced pluripotent stem cellCell biologyProgenitor cellCellular differentiationEctodermChondrogenesisStem cellDirected differentiationEmbryonic stem cellEmbryoEmbryogenesisGenetics

Abstract

fetched live from OpenAlex

Human pluripotent stem cells (hPSCs) are an essential cell source in tissue engineering, studies of development, and disease modeling. Efficient, broadly amenable protocols for rapid lineage induction of hPSCs are of great interest in the stem cell biology field. We describe a simple, robust method for differentiation of hPSCs into mesendoderm in defined conditions utilizing single-cell seeding (SCS) and BMP4 and Activin A (BA) treatment. BA treatment was readily incorporated into existing protocols for chondrogenic and endothelial progenitor cell differentiation, while fine-tuning of BA conditions facilitated definitive endoderm commitment. After prolonged differentiation in vitro or in vivo, BA pretreatment resulted in higher mesoderm and endoderm levels at the expense of ectoderm formation. These data demonstrate that SCS with BA treatment is a powerful method for induction of mesendoderm that can be adapted for use in mesoderm and endoderm differentiation.

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 score0.005
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 science0.0000.000
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 head0.025
GPT teacher head0.270
Teacher spread0.245 · 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

Citations8
Published2019
Admission routes2
Has abstractyes

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