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Record W2605582220 · doi:10.21037/sci.2017.03.08

De-liver CLiPs and revitalize hepatocytes

2017· letter· en· W2605582220 on OpenAlexaff
Ali-Reza Sadri, Saeid Amini‐Nik

Bibliographic record

VenueStem Cell Investigation · 2017
Typeletter
Languageen
FieldMedicine
TopicLiver physiology and pathology
Canadian institutionsHealth Sciences CentreSunnybrook Health Science CentreUniversity of Toronto
Fundersnot available
KeywordsProgenitor cellLiver regenerationRegeneration (biology)HepatocyteReprogrammingBiologyHomeostasisCell biologyStem cellLiver injuryIn vivoProgenitorCancer researchPathologyCellIn vitroMedicineEndocrinologyBiochemistryGenetics

Abstract

fetched live from OpenAlex

The liver is a highly quiescent organ, which has made the existence of hepatic stem cells debatable (1). However, upon injury, the liver shows a remarkable regenerative capacity unmatched by most human organs (2,3). Recent findings shed new light on the origins of liver progenitor cells (LPCs) and source of new hepatocytes during homeostasis and repair. Numerous groups have found that mature hepatocytes (MHs) themselves are the source of hepatic progenitor cells that contribute to liver regeneration (4,5). These findings highlight the plasticity of hepatocytes and a potential target for cell-based therapy in patients with severe liver dysfunction. If hepatocytes have that capacity in vivo , reprogramming of MHs in vitro might be a step in attaining liver regeneration.

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: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: none
Teacher disagreement score0.597
Threshold uncertainty score0.829

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 integrity0.0010.001
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 head0.047
GPT teacher head0.257
Teacher spread0.210 · 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 designNot applicable
Domainnot available
GenreCommentary

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
Published2017
Admission routes1
Has abstractyes

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