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Molecular events and signalling pathways involved in skeletal muscle disuse‐induced atrophy and the impact of countermeasures

2009· review· en· W2104654683 on OpenAlexafffund
Angèle Chopard, Steven Hillock, Bernard J. Jasmin

Bibliographic record

VenueJournal of Cellular and Molecular Medicine · 2009
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMuscle Physiology and Disorders
Canadian institutionsUniversity of Ottawa
FundersCanadian Institutes of Health ResearchCanadian Space Agency
KeywordsMuscle atrophySkeletal muscleAtrophyBed restDeconditioningCachexiaBiologySarcopeniaWastingMedicineBioinformaticsNeuroscienceInternal medicineEndocrinologyCancer

Abstract

fetched live from OpenAlex

Disuse-induced skeletal muscle atrophy occurs following chronic periods of inactivity such as those involving prolonged bed rest, trauma and microgravity environments. Deconditioning of skeletal muscle is mainly characterized by a loss of muscle mass, decreased fibre cross-sectional area, reduced force, increased fatigability, increased insulin resistance and transitions in fibre types. A description of the role of specific transcriptional mechanisms contributing to muscle atrophy by altering gene expression during muscle disuse has recently emerged and focused primarily on short period of inactivity. A better understanding of the transduction pathways involved in activation of proteolytic and apoptotic pathways continues to represent a major objective, together with the study of potential cross-talks in these cellular events. In parallel, evaluation of the impact of countermeasures at the cellular and molecular levels in short- and long-term disuse experimentations or microgravity environments should undoubtedly and synergistically increase our basic knowledge in attempts to identify new physical, pharmacological and nutritional targets to counteract muscle atrophy. These investigations are important as skeletal muscle atrophy remains an important neuromuscular challenge with impact in clinical and social settings affecting a variety of conditions such as those seen in aging, cancer cachexia, muscle pathologies and long-term space exploration.

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: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.714
Threshold uncertainty score0.939

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.019
GPT teacher head0.290
Teacher spread0.271 · 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
GenreReview

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

Citations91
Published2009
Admission routes2
Has abstractyes

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