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Record W2809299966 · doi:10.14814/phy2.13726

Physiological culture conditions alter myotube morphology and responses to atrophy treatments: implications for in vitro research on muscle wasting

2018· article· en· W2809299966 on OpenAlexafffund
Élodie Archer-Lahlou, Cathy Lan, R. Thomas Jagoe

Bibliographic record

VenuePhysiological Reports · 2018
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMuscle Physiology and Disorders
Canadian institutionsMcGill UniversityJewish General Hospital
FundersCanada Foundation for Innovation
KeywordsMyogenesisMuscle atrophyAtrophyAnabolismWastingC2C12MyocyteIn vivoCancerEndocrinologyInternal medicineMedicineBiologyCancer researchBiotechnology

Abstract

fetched live from OpenAlex

Standard in vitro myotube culture conditions are nonphysiological and there is increasing evidence that this may distort adaptations to both catabolic and anabolic stimuli and hamper preclinical research into mechanisms and treatments for muscle atrophy in cancer and other chronic diseases. We tested a new model of myotube culture which mimics more accurately the basal conditions for muscle tissue in patients with chronic disease, such as cancer. Myotubes derived from C2C12 myoblasts, cultured under the modified conditions were thinner, more numerous, with more uniform morphology and an increased proportion of mature myotubes. Furthermore, modified conditions led to increased expression of mir-210-3p, genes related to slow-twitch, oxidative phenotype and resistance to commonly used experimental atrophy-inducing treatments. However, treatment with a combination of drugs used in anti-cancer treatment (doxorubicin and dexamethasone) under the modified culture conditions did lead to myotube atrophy which was only partially prevented by co-administration of curcumin. The results underline the importance and potential advantages of using physiological conditions for in vivo experiments investigating mechanisms of muscle atrophy and especially for preclinical screening of therapies for cancer-related muscle wasting.

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)0.001
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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.806
Threshold uncertainty score0.641

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.088
GPT teacher head0.411
Teacher spread0.323 · 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

Citations15
Published2018
Admission routes2
Has abstractyes

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