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Record W3213146746 · doi:10.3791/63066

Determining Basal Energy Expenditure and the Capacity of Thermogenic Adipocytes to Expend Energy in Obese Mice

2021· article· en· W3213146746 on OpenAlexaff
Michaël Shum, Zhiqiang Zhou, Marc Liesa

Bibliographic record

VenueJournal of Visualized Experiments · 2021
Typearticle
Languageen
FieldMedicine
TopicAdipose Tissue and Metabolism
Canadian institutionsUniversité LavalNatural Sciences and Engineering Research Council of Canada
FundersNational Institute of Diabetes and Digestive and Kidney Diseases
KeywordsEnergy expenditureBasal metabolic rateBasal (medicine)EndocrinologyThermogenesisInternal medicineEnergy balanceEnergy metabolismSpecific dynamic actionResting energy expenditureObesityChemistryBiologyMedicine

Abstract

fetched live from OpenAlex

Energy expenditure measurements are necessary to understand how changes in metabolism can lead to obesity. Basal energy expenditure can be determined in mice by measuring whole-body oxygen consumption, CO2 production, and physical activity using metabolic cages. Thermogenic brown/beige adipocytes (BA) contribute significantly to rodent energy expenditure, particularly at low ambient temperatures. Here, measurements of basal energy expenditure and total BA capacity to expend energy in obese mice are described in two detailed protocols: the first explaining how to set up the assay to measure basal energy expenditure using analysis of covariance (ANCOVA), a necessary analysis given that energy expenditure co-varies with body mass. The second protocol describes how to measure BA energy expenditure capacity in vivo in mice. This procedure involves anesthesia, needed to limit expenditure caused by physical activity, followed by the injection of beta3-adrenergic agonist, CL-316,243, which activates energy expenditure in BA. These two protocols and their limitations are described in sufficient detail to allow a successful first experiment.

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)0.001
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0050.001

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 head0.032
GPT teacher head0.376
Teacher spread0.344 · 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 designObservational
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

Citations1
Published2021
Admission routes1
Has abstractyes

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