Application of Animal Models: Chronic Electrical Stimulation-Induced Contractile Activity
Bibliographic record
Abstract
Unilateral, chronic low-frequency electrical stimulation (CLFS) is an experimental model that evokes numerous biochemical and physiological adaptations in skeletal muscle. These occur within a short time frame and are restricted to the stimulated muscle. The humoral effects of whole body exercise are eliminated and the nonstimulated contralateral limb can often be used as a control muscle, if possible effects on the contralateral side are considered. CLFS induces a fast-to-slow transformation of muscle because of alterations in calcium dynamics and myofibrillar proteins, and a white-to-red transformation because of changes in mitochondrial enzymes, myoglobin, and the induction of angiogenesis. These adaptations occur in a coordinated time-dependent manner and result from altered gene expression, including transcriptional and posttranscriptional processes. CLFS techniques have also been applied to myocytes in cell culture, which provide a greater opportunity for the delivery of pharmacological agents or for the application of gene transfer methodologies. Clinical applications of the CLFS technique have been limited, but they have shown potential therapeutic value in patients in whom voluntary muscle contraction is not possible due to debilitating disease and/or injury. Thus the CLFS technique has great value for studying various aspects of muscle adaptation, and its wider scientific application to a variety of neuromuscular-based disorders in humans appears to be warranted.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".