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Quantification of subcellular glycogen in resting human muscle: granule size, number, and location

2002· article· en· W2143531882 on OpenAlexaff
I. Marchand, K. Chorneyko, Mark A. Tarnopolsky, Shanna Hamilton, Jane Shearer, Jim R. Potvin, Terry E. Graham

Bibliographic record

VenueJournal of Applied Physiology · 2002
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMuscle metabolism and nutrition
Canadian institutionsUniversity of WindsorMcMaster UniversityUniversity of Guelph
Fundersnot available
KeywordsMyofibrilGlycogenGranule (geology)Subcellular localizationChemistrySkeletal muscleBiologyHuman muscleAnatomyBiophysicsBiochemistryCytoplasm

Abstract

fetched live from OpenAlex

A few qualitative investigations suggested that location of muscle glycogen (G) granules in specific sites may be associated with distinct metabolic roles. Similarly, it has been suggested that the acid-soluble and -insoluble G fractions (macro- and proglycogen, respectively) are different metabolic pools and also could exist as separate entities. We employed a transmission electron microscopic technique to quantify subcellular G particle size, number, and location in human vastus lateralis biopsies of 11 resting men. The intra- and interobserver variability for the various measures was generally <4%. Granule size and number were quantified in subcellular compartments (subsarcolemmal, intra- and intermyofibrillar). Subcellular location was critical: G was more densely concentrated in the subsarcolemmal than in the myofibrillar space, whereas the single-particle volume was greater in the latter. Single-particle diameter ranged from 10 to 44 etam and followed a continuous, normal distribution. This implies that proglycogen is not a distinct entity, but rather that pro- and macroglycogen are divisions of smaller and larger molecules. These results demonstrate a compartmentalized pattern of subcellular G deposition in human skeletal muscle for both the size and density of granules.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.229
Threshold uncertainty score0.296

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.015
GPT teacher head0.244
Teacher spread0.229 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations116
Published2002
Admission routes1
Has abstractyes

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