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The Roles of Cytoplasmic Lipid Droplets in Modulating Intestinal Uptake of Dietary Fat

2021· article· en· W3184483730 on OpenAlexaff
Alyssa S. Zembroski, Changting Xiao, Kimberly K. Buhman

Bibliographic record

VenueAnnual Review of Nutrition · 2021
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicLipid metabolism and biosynthesis
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsEnterocyteLipid metabolismLipid dropletEndocrinologyInternal medicineChylomicronHyperlipidemiaPeroxisomeBiologyReceptorChemistrySmall intestineBiochemistryLipoproteinCholesterolDiabetes mellitusVery low-density lipoproteinMedicine

Abstract

fetched live from OpenAlex

Dietary fat absorption is required for health but also contributes to hyperlipidemia and metabolic disease when dysregulated. One step in the process of dietary fat absorption is the formation of cytoplasmic lipid droplets (CLDs) in small intestinal enterocytes; these CLDs serve as dynamic triacylglycerol storage organelles that influence the rate at which dietary fat is absorbed. Recent studies have uncovered novel factors regulating enterocyte CLD metabolism that in turn influence the absorption of dietary fat. These include peroxisome proliferator-activated receptor α activation, compartmentalization of different lipid pools, the gut microbiome, liver X receptor and farnesoid X receptor activation, obesity, and physiological factors stimulating CLD mobilization. Understanding how enterocyte CLD metabolism is regulated is key in modulating the absorption of dietary fat in the prevention of hyperlipidemia and its associated metabolic disorders.

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)0.001
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 score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.010
GPT teacher head0.261
Teacher spread0.251 · 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

Citations14
Published2021
Admission routes1
Has abstractyes

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