Proteolysis in illness-associated skeletal muscle atrophy: from pathways to networks
Bibliographic record
Abstract
Improvements in health in the past decades have resulted in increased numbers of the elderly in both developed and developing regions of the world. Advances in therapy have also increased the prevalence of patients with chronic and degenerative diseases. Muscle wasting, a feature of most chronic diseases, is prominent in the elderly and contributes to both morbidity and mortality. A major research goal has been to identify the proteolytic system(s) that is responsible for the degradation of proteins that occurs in muscle atrophy. Findings over the past 20 years have clearly confirmed an important role of the ubiquitin proteasome system in mediating muscle proteolysis, particularly that of myofibrillar proteins. However, recent observations have provided evidence that autophagy, calpains and caspases also contribute to the turnover of muscle proteins in catabolic states, and furthermore, that these diverse proteolytic systems interact with each other at various levels. Importantly, a number of intracellular signaling pathways such as the IGF1/AKT, myostatin/Smad, PGC1, cytokine/NFκB, and AMPK pathways are now known to interact and can regulate some of these proteolytic systems in a coordinated manner. A number of loss of function studies have identified promising therapeutic approaches to the prevention and treatment of wasting. However, additional biomarkers and other approaches to improve early identification of patients who would benefit from such treatment need to be developed. The current data suggests a network of interacting proteolytic and signaling pathways in muscle. Future studies are needed to improve understanding of the nature and control of these interactions and how they work to preserve muscle function under various states of growth and atrophy.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".