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Record W1488310572 · doi:10.1080/23328940.2015.1011564

Regulation of brown adipose tissue recruitment, metabolism and thermogenic function by peroxisome proliferator-activated receptor<b>γ</b>

2015· review· en· W1488310572 on OpenAlexaff
Andressa Bolsoni Lopes, Yves Deshaies, William T. Festuccia

Bibliographic record

VenueTemperature · 2015
Typereview
Languageen
FieldMedicine
TopicAdipose Tissue and Metabolism
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsThermogenesisBrown adipose tissueAdipose tissueThermogeninEndocrinologyInternal medicineLipolysisPeroxisome proliferator-activated receptorReceptorBiologyPRDM16White adipose tissueHomeothermyPeroxisomeCell biologyThermoregulationMedicine

Abstract

fetched live from OpenAlex

Brown adipose tissue contributes importantly to homeothermy and energy balance in rodents due its ability under demand to produce heat through a process denominated nonshivering thermogenesis. Such thermogenic ability of brown adipocytes relies on the activity of mitochondrial uncoupling protein 1 that, when properly activated, dissipates energy from oxidative metabolism as heat. Brown adipose tissue sympathetic innervation through norepinephrine release not only induces brown adipocyte lipolysis and thermogenesis, but also acts as the major determinant of tissue mass, cellularity and mitochondrial content. Several pieces of evidence gathered over the years indicate that, in addition to tissue sympathetic innervation, the nuclear receptor peroxisome proliferator-activated receptor γ plays an important role in regulating the development, metabolism and thermogenic function of brown adipose tissue. Herein we review the main evidence supporting such key role of peroxisome proliferator-activated receptor γ to brown fat biology and discuss the future directions of this important area of research.

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 score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
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 head0.055
GPT teacher head0.330
Teacher spread0.275 · 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

Citations7
Published2015
Admission routes1
Has abstractyes

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