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Record W2887131246 · doi:10.1111/gtc.12627

<scp>JMJD</scp>2B/<scp>KDM</scp>4B inactivation in adipose tissues accelerates obesity and systemic metabolic abnormalities

2018· article· en· W2887131246 on OpenAlexafffund
Changkeun Kang, Kayoko Saso, Kazushige Ota, Masahito Kawazu, Takeshi Ueda, Hitoshi Okada

Bibliographic record

VenueGenes to Cells · 2018
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicEpigenetics and DNA Methylation
Canadian institutionsUniversity Health NetworkOntario Institute for Cancer Research
FundersJapan Society for the Promotion of ScienceCanadian Institutes of Health ResearchMinistry of Education, Culture, Sports, Science and TechnologyCancer Research Institute
KeywordsBiologyAdipogenesisEndocrinologyInternal medicineAdipose tissueGene knockdownObesityEpigeneticsRegulatorSteatosisGeneGenetics

Abstract

fetched live from OpenAlex

Obesity is a serious global health issue; however, the roles of genetics and epigenetics in the onset and progression of obesity are still not completely understood. The aim of this study was to determine the role of Kdm4b, which belongs to a subfamily of histone demethylases, in adipogenesis and fat metabolism in vivo. We established conditional Kdm4b knockout mice. Inactivation of Kdm4b in adipocytes (K4bKO) induced profound obesity in mice on a high fat diet (HFD). The HFD-fed K4bKO mice exhibited an increased volume of fat mass and higher expression levels of adipogenesis-related genes. In contrast, the genes involved in energy expenditure and mitochondrial functions were down-regulated. Supporting these findings, the energy expenditure of Kdm4b-deficient cells was markedly decreased. In addition, progression of glucose intolerance and hepatic steatosis with hepatocellular damages was observed. These data indicate that Kdm4b is a critical regulator of systemic metabolism via enhancing energy expenditure in adipocytes.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
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 score0.083
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

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)0.0000.000

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 head0.012
GPT teacher head0.249
Teacher spread0.237 · 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 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

Citations23
Published2018
Admission routes2
Has abstractyes

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