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Record W4320718300 · doi:10.1139/bcb-2022-0266

Lipid-induced alteration in retinoic acid signaling leads to mitochondrial dysfunction in HepG2 and Huh7 cells

2023· article· en· W4320718300 on OpenAlexvenueno aff
Eshani Karmakar, Nabanita Das, Bidisha Mukherjee, Prosenjit Das, Satinath Mukhopadhyay, Sib Sankar Roy

Bibliographic record

VenueBiochemistry and Cell Biology · 2023
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicRetinoids in leukemia and cellular processes
Canadian institutionsnot available
FundersScience and Engineering Research BoardCouncil of Scientific and Industrial Research, IndiaDepartment of Science and Technology, Ministry of Science and Technology, India
KeywordsRetinoidRetinoic acidReactive oxygen speciesInflammationAcyltransferaseLipid metabolismEndocrinologyHomeostasisCell biologyBiologyMitochondrionInternal medicineBiochemistryImmunologyMedicineEnzyme

Abstract

fetched live from OpenAlex

A surfeit of mitochondrial reactive oxygen species (ROS) and inflammation serve as obligatory mediators of lipid-associated hepatocellular maladies. While retinoid homeostasis is essential in restoring systemic energy balance, its role in hepatic mitochondrial function remains elusive. The role of lecithin-retinol acyltransferase (LRAT) in maintenance of retinoid homeostasis is appreciated earlier; however, its role in modulating retinoic acid (RA) bioavailability upon lipid-imposition is unexplored. We identified LRAT overexpression in high-fat diet (HFD)-fed rats and palmitate-treated hepatoma cells. Elevation in LRAT expression depletes RA production and deregulates RA signaling. This altered RA metabolism enhances fat accumulation, accompanied by inflammation that leads to impaired mitochondrial function through enhanced ROS generation. Hence, LRAT inhibition could be a novel approach preventing lipid-induced mitochondrial dysfunction in hepatoma cells.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.001

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 head0.009
GPT teacher head0.238
Teacher spread0.228 · 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 designBench or experimental
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

Citations2
Published2023
Admission routes1
Has abstractyes

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