Study of the Relation between Hypoxia and Muscle Atrophy
Bibliographic record
Abstract
Background : Skeletal muscle atrophy is an important feature of chronic obstructive pulmonary disease (COPD) and it is recognized to have considerable clinical impacts. Unfortunately, factors contributing to muscle wasting in COPD are poorly understood. Hypoxemia is typical in COPD and several evidences link hypoxic conditions and protein breakdown. We propose that hypoxia participate to muscle atrophy by increasing Ubiquitin-Proteasome (UP) system activity and by decreasing the activity of IGF/PI3K/Akt synthesis pathway. Methods: To test this hypothesis, L6 muscle myotubes were either exposed to hypoxia (1% O2) or normoxia (21% O2). Results: After 24 hours of hypoxic exposure, we found a significant rise in the chymotrypsin and caspase-like 20S proteasome activities. Proteolysis was confirmed by an accumulation of a 14 kDa actin fragment during hypoxia. An elevation of Atrogin-1 mRNA expression was also observed in similar conditions. A decline in Akt phosphorylation was noticed in hypoxia. These changes were attenuated by insulin treatment. Conclusion: Proteolysis is accentuated in myotubes exposed to hypoxia and the UP system appears to be involved. In addition, protein synthesis seems to be affected as a lower Akt activity was observed. However, the IGF/PI3K/Akt pathway can still be stimulated by a suitable signal suggesting that therapies targeting this pathway are conceivable.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".