Differential gene expression profiling of short and long term denervated muscle
Bibliographic record
Abstract
Skeletal muscle function and viability are dependent upon intact innervation. Peripheral nerve injury and muscle denervation cause muscle atrophy. Time to re-innervation is one of the most important determinants of functional outcome. While short-term denervation can result in nearly fully reversible changes in muscle mass, prolonged denervation leads to irreversible muscle impairment from profound atrophy, myocyte death and fibrosis. We performed transcriptional profiling to identify genes that were altered in expression in short-term (1 month) and long-term (3 month) denervated muscle and validated the microarray data by RT-PCR and Western blotting. Genes controlling cell death, metabolism, proteolysis, stress responses and protein synthesis/translation were altered in expression in the denervated muscle. A differential pattern of expression of genes encoding cell cycle regulators and extracellular matrix components was identified that correlated with the development of irreversible post-denervation changes. Genes encoding mediators of protein degradation were differentially expressed between 1 and 3 month denervated muscle suggesting different signaling networks are recruited over time to induce and maintain muscle atrophy. Understanding of the timing and type of pathological processes that are triggered by denervation may allow the design of interventions that delay or protect muscle from loss of nerve function.
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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) | 0.001 | 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".