Effects of β-hydroxy-β-methylbutyrate supplementation on muscle mass and strength in onabotulinumtoxin type-A-injected and contralateral quadriceps femoris in rabbits
Bibliographic record
Abstract
OBJECTIVE: To determine the effects of the leucine metabolite β-hydroxy-β-methylbutyrate (HMB) on strength, muscle mass, and contractile material in muscle wasting induced by onabotulinumtoxin type-A (BoNT-A) injection into the quadriceps femoris muscles of New Zealand white rabbits. METHODS: A total of 21, female rabbits were divided into 3 groups (n=7, each). Group 1 (Control) received intramuscular injection of saline. Groups 2 and 3 received intramuscular injection of BoNT-A (3.5 units/kg), with group 3 receiving supplementation with HMB (120 mg/kg-BW/day). Muscle morphology, mass, and strength were assessed 8 weeks later in both injected and non-injected contralateral limbs. RESULTS: Injected muscle strength of group 2 (BoNT-A) and group 3 (BoNT-A+HMB) was reduced by 63% and 60%, respectively, compared with Controls (p<0.0001). Strength in contralateral muscles of group 2 was reduced by 23% vs Controls (p<0.002), while in group 3, strength was similar to Controls. Muscle mass in the injected muscles of the BoNT-A and BoNT-A+HMB groups was significantly reduced, by 46% and 48%, respectively. CONCLUSION: While HMB did not prevent loss of muscle strength and mass in the BoNT-A-injected musculature, it prevented significant loss of contractile material in the injected musculature and strength loss in the contralateral non-injected musculature.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".