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Record W2902973961 · doi:10.1111/bph.14556

Hydrogen sulfide and hepatic lipid metabolism – a critical pairing for liver health

2018· review· en· W2902973961 on OpenAlexafffund
Julie J. Loiselle, Guangdong Yang, Lingyun Wu

Bibliographic record

VenueBritish Journal of Pharmacology · 2018
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicSulfur Compounds in Biology
Canadian institutionsLaurentian UniversityHealth Sciences North
FundersNatural Sciences and Engineering Research Council of CanadaHeart and Stroke Foundation of Canada
KeywordsLipid metabolismHydrogen sulfideLiver diseaseBiologyMedicineChemistryBioinformaticsBiochemistry

Abstract

fetched live from OpenAlex

Hydrogen sulfide (H 2 S) is the most recently recognized gasotransmitter, influencing a wide range of physiological processes. As a critical regulator of metabolism, H 2 S has been suggested to be involved in the pathology of many diseases, particularly obesity, diabetes and cardiovascular disorders. Its involvement in liver health has been brought to light more recently, particularly through knockout animal models, which show severe hepatic lipid accumulation upon ablation of H 2 S metabolic pathways. A complex relationship between H 2 S and lipid metabolism in the liver is emerging, which has significant implications for liver disease establishment and/or progression, regardless of the disease‐causing agent. In this review, we discuss the critical importance of H 2 S in hepatic lipid metabolism. We then describe the animal models so far related with H 2 S and lipid‐associated liver disease, as well as H 2 S‐based treatments available. Finally, we highlight important considerations for future studies and identify areas in which much still remains to be determined. Linked Articles This article is part of a themed section on Hydrogen Sulfide in Biology & Medicine. To view the other articles in this section visit http://onlinelibrary.wiley.com/doi/10.1111/bph.v177.4/issuetoc

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.916
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.000
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 head0.042
GPT teacher head0.376
Teacher spread0.334 · 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.

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

Citations38
Published2018
Admission routes2
Has abstractyes

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