Botox Injections in Paraspinal Muscles Result in Low Maximal Specific Force and Shortening Velocity in Fast but Not Slow Skinned Muscle Fibers
Bibliographic record
Abstract
STUDY DESIGN: Basic science, experimental animal study. OBJECTIVE: To determine the effects of Botulinum toxin type A (BTX-A) injections on the mechanical properties of skinned muscle fibers (cells) of rabbit paraspinal muscles. SUMMARY OF BACKGROUND DATA: BTX-A has been widely used in the treatment of disorders of muscle hyperactivity, such as spasticity, dystonia, and back pain. However, BTX-A injection has been shown to cause muscle atrophy, fat infiltration, and decreased force output in target muscles, but its potential effects on the contractile machinery and force production on the cellular level remain unknown. METHODS: Nineteen-month-old, male New Zealand White Rabbits received either saline or BTX-A injections into the paraspinal muscles, equally distributed along the left and right sides of the spine at T12, L1, and L2 at 0, 8, 12, 16, 20, and 24 weeks. Magnetic resonance imaging was used to quantify muscle crosssectional area and structural changes before and at 28 weeks following the initial injection. Skinned fibers isolated from the paraspinal muscles were tested for their active and passive force-length relationships, unloaded shortening velocity, and myosin heavy chain isoforms. RESULTS: BTX-A injections led to significant fat infiltration within the injected muscles and a greater proportion of IIa to IIx fibers. Isolated fast fibers from BTX-A injected animals had lower active force and unloaded shortening velocity compared with fibers from saline-injected control animals. Force and velocity properties were not different between groups for the slow fibers. CONCLUSION: Injection of BTX-A into the paraspinal rabbit muscles leads to significant alterations in the contractile properties of fast, but not slow, fibers.Level of Evidence: N/A.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".