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Record W4403004702 · doi:10.3389/jpps.2024.13375

Targeting ketone body metabolism to treat fatty liver disease

2024· review· en· W4403004702 on OpenAlexafffundvenue
Sora Kwon, Reshani Jeyaratnam, Kyoung-Han Kim

Bibliographic record

VenueJournal of Pharmacy & Pharmaceutical Sciences · 2024
Typereview
Languageen
FieldMedicine
TopicLiver Disease Diagnosis and Treatment
Canadian institutionsUniversity of Ottawa
FundersNatural Sciences and Engineering Research Council of CanadaUniversities Space Research AssociationGovernment of OntarioDiabetes CanadaMitacsHeart and Stroke Foundation of Canada
KeywordsKetogenesisKetone bodiesCirrhosisSteatohepatitisFatty liverLiver diseaseHepatocellular carcinomaMedicineFatty acid metabolismEndocrinologyInternal medicineDiseaseMetabolismBiology

Abstract

fetched live from OpenAlex

Metabolic dysfunction-associated steatotic liver disease (MASLD) is a metabolic disorder marked by excessive accumulation of lipids within the liver. If untreated, this condition can progress to metabolic dysfunction-associated steatohepatitis (MASH), fibrosis, cirrhosis, and ultimately, hepatocellular carcinoma (HCC). Given the liver's pivotal role in glucose and fatty acid metabolism, disruptions in these processes are commonly observed in MASLD. Ketone bodies, crucial energy metabolites primarily produced in the liver, are also closely related to the progression of MASLD. Recent studies have demonstrated that disrupted ketogenesis not only accompanies MASLD, but may also play a causal role in its development and progression. Moreover, activation of the ketogenic pathway has been suggested as a promising strategy for reducing excessive hepatic fat accumulation. This review focuses on the regulation of ketogenesis in MASLD, emphasizing the significance of dietary and pharmacological interventions as potential therapeutic approaches to treat fatty liver disease.

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 score0.004
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0040.002

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 head0.151
GPT teacher head0.484
Teacher spread0.333 · 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

Citations9
Published2024
Admission routes3
Has abstractyes

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