Effects of Co-Administration of Clenbuterol and Testosterone Propionate on Skeletal Muscle in Paraplegic Mice
Bibliographic record
Abstract
Spinal cord injury (SCI) is generally associated with a rapid and significant decrease in muscle mass and corresponding changes in skeletal muscle properties. Although beta(2)-adrenergic and androgen receptor agonists are anabolic substances clearly shown to prevent or reverse muscle wasting in some pathological conditions, their effects in SCI patients remain largely unknown. Here we studied the effects of clenbuterol and testosterone propionate administered separately or in combination on skeletal muscle properties and adipose tissue in adult CD1 mice spinal-cord-transected (Tx) at the low-thoracic level (i.e., induced complete paraplegia). Administered shortly post-Tx, these substances were found to differentially reduce loss in body weight, muscle mass, and muscle fiber cross-sectional area (CSA) values. Although all three treatments induced significant effects, testosterone-treated animals were generally less protected against Tx-related changes. However, none of the treatments prevented fat tissue loss or muscle fiber type conversion and functional loss generally found in Tx animals. These results provide evidence suggesting that clenbuterol alone or combined with testosterone may constitute better clinically-relevant treatments than testosterone alone to decrease muscle atrophy (mass and fiber CSA) in SCI subjects.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".