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Record W3026744271 · doi:10.3791/61256

Hepatic Progenitor Specification from Pluripotent Stem Cells using a Defined Differentiation System

2020· article· en· W3026744271 on OpenAlexaff
Jose Meseguer-Ripolles, Yu Wang, Agnes Sorteberg, Aishwariya Sharma, Nan-Linda Ding, Baltasar Lucendo‐Villarin, Philipp Krämer, Charis‐Patricia Segeritz, David C. Hay

Bibliographic record

VenueJournal of Visualized Experiments · 2020
Typearticle
Languageen
FieldMedicine
TopicLiver physiology and pathology
Canadian institutionsStemcell Technologies
FundersMedical Research CouncilUK Regenerative Medicine Platform
KeywordsProgenitor cellInduced pluripotent stem cellEmbryonic stem cellDirected differentiationEconomic shortageStem cellProgenitorBiologyCellular differentiationTransplantationImmunologyMedicineCell biologyInternal medicine

Abstract

fetched live from OpenAlex

Liver disease is an escalating global health issue. While liver transplantation is an effective mode of therapy, patient mortality has increased due to shortages in donor organ availability. Organ scarcity also affects the routine supply of human hepatocytes for basic research and the clinic. Therefore, the development of renewable sources of human liver progenitor cells is desirable and is the goal of this study. To be able to effectively generate and deploy human liver progenitors on a large scale, a reproducible hepatic progenitor differentiation system was developed. This protocol aids experimental reproducibility between users in a range of cell cultureware formats and permits differentiations using both, human embryonic and induced pluripotent stem cell lines. These are important advantages over current differentiation systems that will enhance the basic research and may pave the way towards clinical product development.

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 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 score0.012
Threshold uncertainty score0.456

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 head0.078
GPT teacher head0.380
Teacher spread0.302 · 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

Citations2
Published2020
Admission routes1
Has abstractyes

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