Physiological culture conditions alter myotube morphology and responses to atrophy treatments: implications for in vitro research on muscle wasting
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".