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Record W2464216456 · doi:10.7555/jbr.30.20150137

Complex role of autophagy in regulation of hepatic lipid andlipoprotein metabolism

2016· article· en· W2464216456 on OpenAlexaff
Zamani Mostafa

Bibliographic record

VenueJournal of Biomedical Research · 2016
Typearticle
Languageen
FieldMedicine
TopicAutophagy in Disease and Therapy
Canadian institutionsHospital for Sick ChildrenUniversity of Toronto
Fundersnot available
KeywordsAutophagyVery low-density lipoproteinLipid metabolismCell biologyLipid dropletBiogenesisApolipoprotein BLipoproteinChemistryBiochemistryBiologyCholesterolGene

Abstract

fetched live from OpenAlex

Discovering new therapeutic interventions to treat lipid and lipoprotein disorders is of great interest and the discovery of autophagy as a regulator of lipid metabolism has opened up new avenues for targeting modulators of this pathway. Autophagy is a degradative process that targets cellular components to the lysosome and recent studies have indicated a role for autophagy in regulating hepatic lipid metabolism (known as lipophagy) as well as lipoprotein assembly. Autophagy directly targets apolipoprotein B-100 (apoB100), the structural protein component of very low-density lipoproteins (VLDLs), and further targets lipid droplets (LDs), the cellular storage for neutral lipids. Autophagy thus plays a complex and dual role in VLDL particle assembly by regulating apoB100 degradation as well as aiding the maturation of VLDL particles by hydrolyzing lipid from LDs. The purpose of this article is to review our current understanding of molecular and cellular mechanisms mediating authophagic control of hepatic lipid biogenesis and VLDL production as well as dysregulation in insulin resistance and dyslipidemia.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.001
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
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 head0.052
GPT teacher head0.389
Teacher spread0.337 · 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 designObservational
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

Citations26
Published2016
Admission routes1
Has abstractyes

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