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Role of Autophagy in Chronic Liver Inflammation and Fibrosis

2019· review· en· W2922391060 on OpenAlexaff
Yujen Tseng, Ling Dong, Yufeng Liu, Nuo Xu, Shu‐Qiang Weng, Harry L.A. Janssen, Shengdi Wu

Bibliographic record

VenueCurrent Protein and Peptide Science · 2019
Typereview
Languageen
FieldMedicine
TopicAutophagy in Disease and Therapy
Canadian institutionsUniversity of TorontoUniversity Health Network
Fundersnot available
KeywordsAutophagyHepatologyInflammationFibrosisGlycogenolysisBiologyLiver fibrosisMedicineMetabolismInternal medicineBiochemistry

Abstract

fetched live from OpenAlex

Autophagy entails the removal of dysfunctional components to maintain cellular homeostasis. Over the years, studies of autophagy demonstrated its complex physiological and pathological roles in the liver. Apart from regulation of normal metabolic functions such as glycogenolysis, glycogenesis, and β-oxidation, autophagy also contributes to the modulation of various liver diseases. In this review, we provide a concise overview of the role of autophagy in regulating hepatic metabolism in healthy conditions and various chronic liver diseases. A well-rounded understanding of the role of autophagy may provide insight for future medical advancements in the field of hepatology.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.002

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 head0.042
GPT teacher head0.350
Teacher spread0.308 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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

Citations11
Published2019
Admission routes1
Has abstractyes

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