Differential Effects Of RAD140 Treatment On Rat Tibialis Anterior And Plantaris Muscle Cross-Sectional Areas
Bibliographic record
Abstract
PURPOSE: Selective Androgen Receptor Modulators (SARMs) are a class of therapeutic compounds that have potent anabolic effects, stimulate skeletal muscle hypertrophy, and may provide benefits in muscle wasting conditions such as sarcopenia or cancer cachexia. Recently, SARM treatment has been increasingly popular with athletes in hopes to improve performance and muscular strength. Of particular interest to strength athletes is testolone (RAD140), a novel SARM known to stimulate androgen-sensitive tissues like skeletal muscle. However, the impact of RAD140 alone, in the absence of any additional androgenic stimulus such as strength training or functional overload using a synergist ablation model, requires further exploration. METHODS: Either RAD140 (1 mg/kg,) or vehicle (5% methylcellulose, w/v) was provided in drinking water for 14 days. Then, rat plantaris (PLA) and tibialis anterior (TA) muscles were collected, trimmed free of connective tissue, weighed, and prepared for histochemical analyses of myofiber areas. Serial sections of each muscle were cut at 14 μM, stained using standard hematoxylin and eosin techniques, and myofiber cross-sectional areas (CSA) were quantified using a light microscope and ImageJ software. RESULTS: Interestingly, RAD140 treatment had differential effects on CSA. For example, average CSA was significantly larger in PLA fibers from RAD140-treated rats compared to vehicles-treated rats (33,935 + 2413 vs. 24,219 + 992 AU, respectively, p < .05). In contrast, average CSA in the TA muscle was unchanged due to RAD140 treatment compared to vehicle treated rats (31,546 + 3229 vs. 28,021 + 4327, respectively). CONCLUSIONS: These data may suggest that muscle recruitment, activation patterns, load challenge, or muscle fiber type composition may also play key roles in the differential response on fiber CSA secondary to RAD140 treatment. AFP Innovation Fund
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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) | 0.000 | 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 | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".