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Record W4210642219 · doi:10.1016/j.stemcr.2022.01.003

Leveraging interacting signaling pathways to robustly improve the quality and yield of human pluripotent stem cell-derived hepatoblasts and hepatocytes

2022· article· en· W4210642219 on OpenAlexafffund
Claudia Raggi, Marie-Agnès M’Callum, Quang Toan Pham, Perrine Gaub, Silvia Selleri, Nissan Baratang, C. Mangahas, Gaël Cagnone, Bruno Reversade, Jean‐Sébastien Joyal, Massimiliano Paganelli

Bibliographic record

VenueStem Cell Reports · 2022
Typearticle
Languageen
FieldMedicine
TopicLiver physiology and pathology
Canadian institutionsUniversité de MontréalCentre Hospitalier Universitaire Sainte-Justine
FundersCanadian Institutes of Health Research
KeywordsBiologyInduced pluripotent stem cellEmbryonic stem cellCell biologyStem cellComputational biologyHuman Induced Pluripotent Stem CellsHepatocyteNeuroscienceIn vitroGeneGenetics

Abstract

fetched live from OpenAlex

Pluripotent stem cell (PSC)-derived hepatocyte-like cells (HLCs) have shown great potential as an alternative to primary human hepatocytes (PHHs) for in vitro modeling. Several differentiation protocols have been described to direct PSCs toward the hepatic fate. Here, by leveraging recent knowledge of the signaling pathways involved in liver development, we describe a robust, scalable protocol that allowed us to consistently generate high-quality bipotent human hepatoblasts and HLCs from both embryonic stem cells and induced PSC (iPSCs). Although not yet fully mature, such HLCs were more similar to adult PHHs than were cells obtained with previously described protocols, showing good potential as a physiologically representative alternative to PHHs for in vitro modeling. PSC-derived hepatoblasts effectively generated with this protocol could differentiate into mature hepatocytes and cholangiocytes within syngeneic liver organoids, thus opening the way for representative human 3D in vitro modeling of liver development and pathophysiology.

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 score0.010
Threshold uncertainty score0.613

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.058
GPT teacher head0.278
Teacher spread0.220 · 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

Citations25
Published2022
Admission routes2
Has abstractyes

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