Influence of aging and long-term unloading on the structure and function of human skeletal muscleThis paper is one of a selection of papers published in this Special Issue, entitled 14th International Biochemistry of Exercise Conference – Muscles as Molecular and Metabolic Machines, and has undergone the Journal’s usual peer review process.
Bibliographic record
Abstract
Understanding the quantitative and qualitative changes in skeletal muscle that control changes in function is crucial in the development of countermeasures to the loss of skeletal muscle function observed with real and simulated microgravity exposure (i.e., unloading) and with aging in humans. Qualitative changes that could influence the force and power producing properties of skeletal muscle are changes in the distribution of the 3 isoforms of the main motor protein myosin heavy chain (MHC), as well as the abundance of MHC, actin (the other main contractile protein), and the force distributing the connective tissue network. Numerous studies have examined quantitative and qualitative changes in skeletal muscle, from the whole muscle to the single myofiber from individuals undergoing real and simulated space flight for a few weeks to several months, as well as from aging men and women. When considering the relative content of the main functional and structural elements (i.e., myosin, actin, collagen), it appears that human muscle appropriately scales changes in size of 10%-40% induced over a relatively short time period (1-3 months) and over the lifespan (in humans 20 to 90+ years old). The main qualitative change with unloading and aging is a redistribution of the 3 MHC isoforms, which have vastly different contractile characteristics. It is now known that the response of skeletal muscle to unloading is muscle and gender specific. In summary, changes in muscle mass (quantity) combined with the alterations in MHC distribution (quality) are the primary determinants of changes in muscle function with unloading and aging. These parameters are the key components of muscle that should be targeted with countermeasures for conditions related to muscle loss, along with considerations for muscle- and gender-specific responses.
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 | 0.000 | 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) | 0.000 | 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".