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Record W3216368787

Muscle-bone properties after prolonged voluntary wheel running in a mouse model of dominant severe osteogenesis imperfecta.

2021· article· en· W3216368787 on OpenAlexaff
Josephine T. Tauer, Gustavo Henrique Rigo Canevazzi, Justine Schiettekatte-Maltais, Frank Rauch, Raynald Bergeron, Louis‐Nicolas Veilleux

Bibliographic record

VenuePubMed · 2021
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicConnective tissue disorders research
Canadian institutionsUniversité de MontréalMcGill UniversityShriners Hospitals for Children - Canada
Fundersnot available
KeywordsOsteogenesis imperfectaWheel runningRegimenForelimbMedicineGrip strengthSkeletal muscleBone remodelingInternal medicineEndocrinologyTurnoverPhysical therapyAnatomy
DOInot available

Abstract

fetched live from OpenAlex

OBJECTIVE: The objective of the current study is to assess the effect of a seven-week voluntary wheel running intervention on muscles and bones properties in a mouse model mimicking dominant severe osteogenesis imperfecta (OI). METHODS: ) mice either performed voluntarily wheel-running exercise for 7-weeks or remained sedentary. Running distance and speed, forelimb grip strength, isolated muscle force and fatigability as well as bone morphology and mechanical properties were assessed. RESULTS: We demonstrate that female WT and OI mice voluntarily performed exercise, although OI mice exercised less than WT littermates. The exercise regimen increased soleus muscle masses in WT and OI but increased relative grip strength in WT mice only. Specific muscle force and fatigability were similar between WT and OI mice and did not improve with exercise. Furthermore, the exercise regimen did not improve the femoral architectural and biomechanical properties in OI mice. CONCLUSION: Our study suggests that voluntary wheel running is not appropriate to assess the effects of exercise in a mouse model of OI. Findings from exercising OI mice model studies may not necessarily be transferable to humans.

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 score0.060
Threshold uncertainty score0.603

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 head0.021
GPT teacher head0.224
Teacher spread0.203 · 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

Citations8
Published2021
Admission routes1
Has abstractyes

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