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Record W3173436623 · doi:10.3389/fphys.2021.689747

Lactate Fluxes and Plasticity of Adipose Tissues: A Redox Perspective

2021· review· en· W3173436623 on OpenAlexaff
Damien Lagarde, Yannick Jeanson, Jean‐Charles Portais, Anne Galinier, Isabelle Ader, Louis Casteilla, Audrey Carrière

Bibliographic record

VenueFrontiers in Physiology · 2021
Typereview
Languageen
FieldMedicine
TopicAdipose Tissue and Metabolism
Canadian institutionsMcGill University
FundersCentre National de la Recherche ScientifiqueSociété Française de NutritionInstitut National de la Santé et de la Recherche MédicaleAgence Nationale de la Recherche
KeywordsAdipose tissueIntracellularCell biologyHomeostasisBiologyOxidative phosphorylationGlycolysisFlux (metallurgy)MetabolismMitochondrionAdipocyteRegulatorBiochemistryChemistry

Abstract

fetched live from OpenAlex

Lactate, a metabolite produced when the glycolytic flux exceeds mitochondrial oxidative capacities, is now viewed as a critical regulator of metabolism by acting as both a carbon and electron carrier and a signaling molecule between cells and tissues. In recent years, increasing evidence report its key role in white, beige, and brown adipose tissue biology, and highlights new mechanisms by which lactate participates in the maintenance of whole-body energy homeostasis. Lactate displays a wide range of biological effects in adipose cells not only through its binding to the membrane receptor but also through its transport and the subsequent effect on intracellular metabolism notably on redox balance. This study explores how lactate regulates adipocyte metabolism and plasticity by balancing intracellular redox state and by regulating specific signaling pathways. We also emphasized the contribution of adipose tissues to the regulation of systemic lactate metabolism, their roles in redox homeostasis, and related putative physiopathological repercussions associated with their decline in metabolic diseases and aging.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.972
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0040.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.023
GPT teacher head0.325
Teacher spread0.301 · 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

Citations44
Published2021
Admission routes1
Has abstractyes

Explore more

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