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Maintaining Thermogenesis in Cold Exposed Humans: Relying on Multiple Metabolic Pathways

2014· article· en· W4409093077 on OpenAlexaff
Denis P. Blondin, Hans Christian Tingelstad, Olivier L. Mantha, Chantal Gosselin, François Haman

Bibliographic record

VenueComprehensive physiology · 2014
Typearticle
Languageen
FieldMedicine
TopicAdipose Tissue and Metabolism
Canadian institutionsUniversity of OttawaUniversité de Sherbrooke
Fundersnot available
KeywordsThermogenesisBiologyNeuroscienceEndocrinologyObesity

Abstract

fetched live from OpenAlex

Abstract In cold exposed humans, increasing thermogenic rate is essential to prevent decreases in core temperature. This review describes the metabolic requirements of thermogenic pathways, mainly shivering thermogenesis, the largest contributor of heat. Research has shown that thermogenesis is sustained from a combination of carbohydrates, lipids, and proteins. The mixture of fuels is influenced by shivering intensity and pattern as well as by modifications in energy reserves and nutritional status. To date, there are no indications that differences in the types of fuel being used can alter shivering and overall heat production. We also bring forth the potential contribution of nonshivering thermogenesis in adult humans via the activation of brown adipose tissue (BAT) and explore some means to stimulate the activity of this highly thermogenic tissue. Clearly, the potential role of BAT, especially in young lean adults, can no longer be ignored. However, much work remains to clearly identify the quantitative nature of this tissue's contribution to total thermogenic rate and influence on shivering thermogenesis. Identifying ways to potentiate the effects of BAT via cold acclimation and/or the ingestion of compounds that stimulate the thermogenic process may have important implications in cold endurance and survival. © 2014 American Physiological Society. Compr Physiol 4:1383‐1402, 2014.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.856
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.060
GPT teacher head0.283
Teacher spread0.223 · 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

Citations3
Published2014
Admission routes1
Has abstractyes

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