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Record W2737355521 · doi:10.1002/jms.3957

Myofiber metabolic type determination by mass spectrometry imaging

2017· article· en· W2737355521 on OpenAlexfundno aff
Delphine Centeno, Annie Vénien, Estelle Pujos‐Guillot, Thierry Astruc, Christophe Chambon, Laëtitia Théron

Bibliographic record

VenueJournal of Mass Spectrometry · 2017
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMuscle metabolism and nutrition
Canadian institutionsnot available
FundersGeneralitat de CatalunyaCanadian Institute for Advanced Research
KeywordsChemistryGlycolysisMass spectrometryMyocyteFiber typeMass spectrometry imagingMatrix-assisted laser desorption/ionizationOxidative phosphorylationBiochemistryMetabolismBiophysicsFiberChromatographyCell biologyDesorptionBiology

Abstract

fetched live from OpenAlex

Matrix assisted laser desorption/ionization (MALDI) mass spectrometry imaging is a powerful tool that opens new research opportunities in the field of biology. In this work, predictive model was developed to discriminate metabolic myofiber types using the MALDI spectral data. Rat skeletal muscles are constituted of type I and type IIA fiber, which have an oxidative metabolism for glycogen degradation, and type IIX and type IIB fiber which have a glycolytic metabolism, present in different proportions according to the muscle function and physiological state. So far, myofiber type is determined by histological methods that are time consuming. Thanks to the predictive model, we were able to predict not only the metabolic fiber type but also their location, on the same muscle section that was used for MALDI imaging. Copyright © 2017 John Wiley & Sons, Ltd.

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)0.001
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 score0.098
Threshold uncertainty score0.790

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 science0.0010.000
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 head0.008
GPT teacher head0.262
Teacher spread0.254 · 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

Citations6
Published2017
Admission routes1
Has abstractyes

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