Molecular Transducers of Human Skeletal Muscle Remodeling under Different Loading States
Bibliographic record
Abstract
Loading of skeletal muscle changes the tissue phenotype reflecting altered metabolic and functional demands. In humans, heterogeneous adaptation to loading complicates the identification of the underpinning molecular regulators. A within-person differential loading and analysis strategy reduces heterogeneity for changes in muscle mass by ∼40% and uses a genome-wide transcriptome method that models each mRNA from coding exons and 3' and 5' untranslated regions (UTRs). Our strategy detects ∼3-4 times more regulated genes than similarly sized studies, including substantial UTR-selective regulation undetected by other methods. We discover a core of 141 genes correlated to muscle growth, which we validate from newly analyzed independent samples (n = 100). Further validating these identified genes via RNAi in primary muscle cells, we demonstrate that members of the core genes were regulators of protein synthesis. Using proteome-constrained networks and pathway analysis reveals notable relationships with the molecular characteristics of human muscle aging and insulin sensitivity, as well as potential drug therapies.
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 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) | 0.002 | 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".