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Record W3082489904 · doi:10.1101/2020.09.02.280172

Leveraging complex interactions between signaling pathways involved in liver development to robustly improve the maturity and yield of pluripotent stem cell-derived hepatocytes

2020· preprint· en· W3082489904 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

VenuebioRxiv (Cold Spring Harbor Laboratory) · 2020
Typepreprint
Languageen
FieldMedicine
TopicLiver physiology and pathology
Canadian institutionsUniversité de MontréalCentre Hospitalier Universitaire Sainte-Justine
FundersStem Cell Network
KeywordsInduced pluripotent stem cellCell biologyHepatocyteBiologyComputational biologyDrug discoveryPhenotypeIn vitroStem cellSignal transductionBioinformaticsEmbryonic stem cellBiochemistry

Abstract

fetched live from OpenAlex

SUMMARY Pluripotent stem cell (PSC)-derived hepatocyte-like cells (HLC) have shown great potential as an alternative to primary human hepatocytes (PHH) for in vitro modeling. Several differentiation protocols have been described to direct PSC towards the hepatic fate, although the resulting HLC have shown more a fetal than adult phenotype. Here, by leveraging recent knowledge of the signaling pathways involved in liver development, we describe a robust, scalable protocol that allows to consistently generate high-quality HLC from both ESC and iPSC. Such HLC are comparable to adult PHH in terms of key mature liver functions and proved suitable to assess drug hepatotoxicity, as a proof of concept of their potential as a physiologically representative alternative for in vitro modeling.

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)0.000
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 score0.406
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 science0.0000.001
Research integrity0.0000.001
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 head0.073
GPT teacher head0.240
Teacher spread0.167 · 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

Citations4
Published2020
Admission routes2
Has abstractyes

Explore more

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