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Record W2946375410 · doi:10.3791/59965

Serum Free Production of Three-dimensional Human Hepatospheres from Pluripotent Stem Cells

2019· article· en· W2946375410 on OpenAlexaff
Baltasar Lucendo‐Villarin, Hassan Rashidi, Sharmin Alhaque, Lena Fischer, Jose Meseguer-Ripolles, Yu Wang, Cliona O’Farrelly, Michael Themis, David C. Hay

Bibliographic record

VenueJournal of Visualized Experiments · 2019
Typearticle
Languageen
FieldEngineering
Topic3D Printing in Biomedical Research
Canadian institutionsTrinity College
FundersMedical Research CouncilUK Regenerative Medicine Platform
KeywordsInduced pluripotent stem cellEmbryonic stem cellStem cellCell biologyBiologyHuman Induced Pluripotent Stem CellsRegenerative medicineBiochemistry

Abstract

fetched live from OpenAlex

The development of renewable sources of liver tissue is required to improve cell-based modelling, and develop human tissue for transplantation. Human embryonic stem cells (hESCs) and human induced pluripotent stem cells (hiPSCs) represent promising sources of human liver spheres. We have developed a serum free and defined method of cellular differentiation to generate three-dimensional human liver spheres formed from human pluripotent stem cells. A potential limitation of the technology is the production of dense spheres with dead material inside. In order to circumvent this, we have employed agarose microwell technology at defined cell densities to control the size of the 3D spheres, preventing the generation of apoptotic and/or necrotic cores. Notably, the spheres generated by our approach display liver function and stable phenotype, representing a valuable resource for basic and applied scientific research. We believe that our approach could be used as a platform technology to develop further tissues to model and treat human disease and in the future may permit the generation of human tissue with complex tissue architecture.

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 categoriesInsufficient payload (model declined to judge)
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 score0.003
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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)0.0010.000

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 head0.032
GPT teacher head0.359
Teacher spread0.328 · 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

Citations12
Published2019
Admission routes1
Has abstractyes

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