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Record W2010106565 · doi:10.1242/jeb.036384

BRRROWN ADIPOSE TISSUE: SPECIAL FAT FOR COLD CRITTERS

2010· article· en· W2010106565 on OpenAlexaff
Jodie L. Rummer

Bibliographic record

VenueJournal of Experimental Biology · 2010
Typearticle
Languageen
FieldMedicine
TopicAdipose Tissue and Metabolism
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsHibernation (computing)FenofibrateAdipose tissueInternal medicineEndocrinologyMetabolismBrown adipose tissueAcclimatizationLipid metabolismAnimal scienceBiologyChemistryMedicineEcologyState (computer science)

Abstract

fetched live from OpenAlex

There are two major strategies available to mammals to survive winter: hibernation, where large animals drop their metabolic rate, and non-hibernation, where small mammals utilise fat stores to maintain their body temperature. According to Stuart Egginton from the University of Birmingham, textbooks state that the liver is the main organ of heat generation, ‘but this is based purely on the relative mass,’ he explains. Egginton realised that the liver could only be a thermogenic organ if it generated more heat then expected for its size. Egginton, David Hauton and Andrew Coney decided to test the liver's lipid oxidation profile to find out whether the liver really does keep non-hibernators warm during winter.Focusing on rats' body temperatures and fat metabolism as the animals were cooled, the team treated some of the rats with fenofibrate (which increases fat oxidation by the liver and could maintain the rat's body temperature during cooling if liver is the source of winter warmth) and others with dichloroacetate (which inhibits the liver's ability to oxidise fats and would force the animals to rely on other forms of heat generation to maintain their temperature) before cooling the rodents. Then they compared how the fenofibrate and dichloroacetate treated animals performed with a third group of rats that had been prepared for the cold conditions by cold acclimation.If fat metabolism by the liver was key to keeping warm, the fenofibrate treated rats should survive cooling as well as the animals that were previously acclimated to cold conditions. However, the cooled rats that had been treated with dichloroacetate would not survive cooling in good condition, as their livers cannot metabolise fats and they must rely on other, limited, energy supplies (glucose) to maintain their temperature. As the fenofibrate treated rats did not maintain their temperature as well as the well-prepared cold acclimated rats, the researchers suggested that the liver is not the critical step to heat generation.Some other fat related tissue must be responsible for the rodents' ability to defend their body temperature. Brown adipose tissue (BAT) is a fat storage tissue especially abundant in small mammals and newborn humans. BAT is highly vascularised, full of mitochondria and burns fat to produce heat in a special way. Maybe it could provide the warmth the rodents require to survive winter in addition to its supposed role in arousal?The team found that the BAT of cold acclimated rats took up fatty acids that were oxidised to generate heat. Amazingly, these rats were up to 12 times better at the conversion than the other rats. Additionally, while the other rats slowed their ventilation, the cold acclimated rats increased their breathing rate to better supply BAT with oxygenated blood and hence maintain their temperature while being cooled.The authors decided that BAT is the true ‘thermogenic machinery’ for non-hibernators, and that the liver may contribute very little to thermogenesis. Scientists think BAT fat metabolism that non-hibernators use to stay warm and remain alert during cold conditions may have been one key to the evolutionary success of early mammals.

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)0.001
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: none
Teacher disagreement score0.030
Threshold uncertainty score0.101

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.002
Scholarly communication0.0030.002
Open science0.0010.002
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0300.011

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 head0.022
GPT teacher head0.342
Teacher spread0.321 · 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".

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Citations0
Published2010
Admission routes1
Has abstractyes

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