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Record W4380046457 · doi:10.1016/j.isci.2023.107047

Toward countering muscle and bone loss with spaceflight: GSK3 as a potential target

2023· article· en· W4380046457 on OpenAlexafffund
Ryan W. Baranowski, Jessica L. Braun, Briana L. Hockey, Jenalyn L. Yumol, Mia S. Geromella, Colton J. F. Watson, Nigel Kurgan, Holt N. Messner, Kennedy C. Whitley, Adam J. MacNeil, Guillemette Gauquelin‐Koch, Fabrice Bertile, William Gittings, Rene Vandenboom, Wendy E. Ward, Val A. Fajardo

Bibliographic record

VenueiScience · 2023
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMuscle Physiology and Disorders
Canadian institutionsBrock University
FundersNatural Sciences and Engineering Research Council of CanadaCanadian Institutes of Health ResearchCanadian Space Agency
KeywordsSpaceflightGSK3BHindlimbGSK-3SerineSkeletal musclePhosphorylationChemistryGlycogen synthaseCell biologyBiologyAnatomyBiochemistryPhysics

Abstract

fetched live from OpenAlex

We examined the effects of ∼30 days of spaceflight on glycogen synthase kinase 3 (GSK3) content and inhibitory serine phosphorylation in murine muscle and bone samples from four separate missions (BION-M1, rodent research [RR]1, RR9, and RR18). Spaceflight reduced GSK3β content across all missions, whereas its serine phosphorylation was elevated with RR18 and BION-M1. The reduction in GSK3β was linked to the reduction in type IIA fibers commonly observed with spaceflight as these fibers are particularly enriched with GSK3. We then tested the effects of inhibiting GSK3 before this fiber type shift, and we demonstrate that muscle-specific Gsk3 knockdown increased muscle mass, preserved muscle strength, and promoted the oxidative fiber type with Earth-based hindlimb unloading. In bone, GSK3 activation was enhanced after spaceflight; and strikingly, muscle-specific Gsk3 deletion increased bone mineral density in response to hindlimb unloading. Thus, future studies should test the effects of GSK3 inhibition during spaceflight.

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 score0.515
Threshold uncertainty score0.263

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 head0.008
GPT teacher head0.236
Teacher spread0.228 · 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

Citations14
Published2023
Admission routes2
Has abstractyes

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