High Intensity Fatigue, Firing Rates And Contractile Properties Of The Human Soleus
Bibliographic record
Abstract
Anatomically and physiologically, the soleus is a unique muscle. It is composed of approximately 90% slow twitch muscle fibres, and exhibits a very narrow rate coding range from 7.3 + 1.0 Hz during posture (Mochizuki et al. 2006) to 10.7 + 2.9 during a voluntary maximal isometric contraction (MVC) (Bellemere et al. 1983). However, in general firing rate data are not extensive and have not been reported over varying contraction intensities. Furthermore, changes in muscle and firing rate properties with fatigue have been limited to one study at low and moderate (<60% MVC) sustained isometric contraction intensities (Kuchinad et al. 2004). PURPOSE: To determine the effects of contraction intensity and high intensity sustained isometric fatigue on the firing rates and contractile properties of the soleus. METHODS: Neuromuscular properties were collected from the soleus and plantar flexors of six young men (20-28 years) during repeated tests (2-4 sessions). Populations of single MU firing rate trains were recorded with tungsten microelectrodes during separate sustained 10-second isometric contractions of varying intensities (25%, 50%, 75% and 100% of MVC), and during a high intensity (75-100%) isometric fatiguing contraction test. RESULTS: Voluntary activation of the plantar flexors, as assessed by the interpolated twitch technique, was near maximal (> 99%) for all subjects. Mean MU firing rates of the soleus were increased progressively from ~8 Hz at 25% MVC to ~17 HZ at MVC. The average duration of the fatigue protocol in which the plantar flexor MVC torque was reduced by 50%, was ~90 seconds. The post-fatigue half-relaxation time (HRT) slowed by 60% compared to pre-fatigue and the firing rates decreased by 40% compared to the commencement of the fatigue protocol. Conversely, peak twitch (Pt) increased by 25% and time to peak twitch (TPT) decreased by 20% post-fatigue compared to pre-fatigue. CONCLUSIONS: Despite the low and narrow rate coding range in mean soleus MU firing rates, these results support previous observations that high intensity fatigue will substantially lower mean maximal MU rates during fatigue that induces substantial torque loss and contractile slowing. Supported by NSERC and OGSST
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.000 |
| 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".