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Record W2336341493 · doi:10.1242/jeb.214.2.iia

ASPECTS OF METABOLIC REGULATION

2011· article· en· W2336341493 on OpenAlexaboutno aff
Kathryn Knight

Bibliographic record

VenueJournal of Experimental Biology · 2011
Typearticle
Languageen
FieldMedicine
TopicDiet and metabolism studies
Canadian institutionsnot available
Fundersnot available
KeywordsMetabolic control analysisFlux (metallurgy)IntracellularCarbohydrate metabolismGlucose transporterInsulin resistanceMetabolismDiffusionInternal medicineDiabetes mellitusEndocrinologyBiologyInsulinChemistryMedicineBiochemistryPhysics

Abstract

fetched live from OpenAlex

Metabolism is tightly regulated at many levels and, if disrupted, can lead to severe metabolic disorders such as insulin-resistant diabetes and cardiovascular disease. So, building an understanding of metabolic regulation is essential if we are to begin to combat many of the disorders that characterise metabolic syndrome and the obesity crisis. David Wasserman and colleagues at Vanderbilt University School of Medicine, USA discuss the control of glucose flux in muscle (p. 254). Describing the delivery of glucose to muscle tissue, transport in and glucose phosphorylation – which traps glucose in the muscle and completes the uptake process – Wasserman explains that glucose uptake is under distributed control by all three of these processes. Ultimately, the team hopes that ‘one or more of these steps should be effective targets for treatment of glucose intolerance and insulin resistance’.Considering the role of diffusion in metabolic processes, Stephen Kinsey and colleagues from the University of North Carolina, Wilmington and Florida State University, USA explain that ‘metabolic processes are often represented as a group of metabolites that interact through enzymatic reactions’. However, they go on to add that diffusion may exert greater control over reaction rates as distances increase and reaction rates rise or diffusion coefficients decrease. Focusing on muscle fibres, which vary enormously in size, Kinsey and his colleagues discuss the effects of muscle fibre organisation and the intracellular environment on metabolic diffusion (p. 263) and conclude that ‘metabolic processes in muscles... are not greatly limited by diffusion,’ but add, ‘the influence of diffusion is apparent in patterns of fibre growth and metabolic organization’.Life in the cold has profound effects on metabolism for ectotherms. Kristin O'Brien from the University of Alaska Fairbanks, USA says, ‘As temperature declines, one of the greatest challenges is maintaining the production of ATP’. One strategy for survival in frigid conditions is to increase the levels of enzymes involved in aerobic metabolism by increasing the volume of mitochondria in cells. O'Brien reviews the current understanding of the molecular pathways that govern mitochondrial molecular remodelling (p. 275). She also outlines the consequences of increased mitochondrial density, such as increased lipid densities, raised oxygen solubility and reduced diffusion distances. As well as increasing protein synthesis levels, O'Brien explains that cold-adapted fish also increase membrane synthesis rates, and she speculates about the signalling molecules that regulate the process of mitochondrial biosynthesis.Animals also have to select which metabolic fuels they use in response to different energetic demands. Jean-Michel Weber from the University of Ottawa, Canada explains that each fuel type has different strengths and weaknesses. Lipids are light to transport and abundant but are insoluble in water and slow to produce ATP. Alternatively, carbohydrates can synthesise ATP rapidly but are heavy and scarce, so a particular fuel is only selected for use when its advantages outweigh its disadvantages. Weber explains that animals use a variety of strategies to optimise fuel use (p. 286), including AMPK regulation of fuel selection and the recruitment of specific muscle fibre types that metabolise the most appropriate fuel for a particular activity. Migrating animals that maintain intense exercise for days at a time must also be able to sustain record fluxes of fuel to their locomotory muscles. They do so by boosting lipid mobilisation, transport and oxidation to maximise aerobic ATP production during their marathon odysseys.

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)0.002
metaresearch head score (Gemma)0.005
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 score0.019
Threshold uncertainty score0.065

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0020.009
Scholarly communication0.0060.005
Open science0.0010.004
Research integrity0.0030.005
Insufficient payload (model declined to judge)0.0190.008

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 head0.044
GPT teacher head0.323
Teacher spread0.279 · 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

Citations2
Published2011
Admission routes1
Has abstractyes

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