Cellular Markers of Muscle Atrophy in Chronic Obstructive Pulmonary Disease
Bibliographic record
Abstract
Skeletal muscle atrophy in individuals with advanced chronic obstructive pulmonary disease (COPD) is associated with diminished quality of life, increased health resource use, and worsened survival. Muscle wasting results from an imbalance between protein degradation and synthesis, and is enhanced by decreased regenerative repair. We investigated the activation of cellular signaling networks known to mediate muscle atrophy and regulate muscle regenerative capacity in rodent models, in individuals with COPD (FEV(1) < 50% predicted). Nine patients with COPD and nine control individuals were studied. Quadriceps femoris muscle isometric contractile force and cross-sectional area were confirmed to be significantly smaller in the patients with COPD compared with control subjects. The vastus lateralis muscle was biopsied and muscle transcript and/or protein levels of key components of ubiquitin-mediated proteolytic systems (MuRF1, atrogin-1, Nedd4), inflammatory mediators (IkappaBalpha, NF-kappaBp65/p50), AKT network (AKT, GSK3beta, p70S6 kinase), mediators of autophagy (beclin-1, LC3), and myogenesis (myogenin, MyoD, Myf5, myostatin) were determined. Atrogin-1 and Nedd4, two ligases regulating ubiquitin-mediated protein degradation and myostatin, a negative regulator of muscle growth, were significantly increased in the muscle of patients with COPD. MuRF1, Myf5, myogenin, and MyoD were not differentially expressed. There were no differences in the level of phosphorylation of AKT, GSK3beta, p70S6kinase, or IkappaBalpha, activation of NF-kappaBp65 or NF-kappaBp50, or level of expression of beclin-1 or LC3, suggesting that AKT signaling was not down-regulated and the NF-kappaB inflammatory pathway and autophagy were not activated in the COPD muscle. We conclude that muscle atrophy associated with COPD results from the recruitment of specific regulators of ubiquitin-mediated proteolytic pathways and inhibition of muscle growth.
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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 | 0.001 | 0.001 |
| 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) | 0.001 | 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".