High-intensity Fatigue In The Soleus Of Young And Old Men
Bibliographic record
Abstract
In the soleus, the inherent low maximal motor unit firing rates (MUFRs) are unchanged with age, despite an age-related slowing in the contractile properties of the triceps surae. High intensity fatigue typically induces contractile slowing and lower MUFRs. The question here is in the aged soleus will high intensity fatigue induce further slowing and changes in MUFRs? PURPOSE: Thus, the effects of high-intensity sustained isometric fatigue on the MUFRs and contractile properties of the soleus were compared in 6 young (∼24 years) and 6 old (∼75 years) men. METHODS: Various measures of the contractile function of the soleus and triceps surae were tested during 2-6 sessions via maximal voluntary isometric contractions (MVCs) and tibial nerve stimulation. Populations of MUFR trains were recorded from the soleus with tungsten microelectrodes during a high-intensity (75-100% MVC) sustained isometric fatiguing contraction task, and during MVCs prior to the fatigue task and at 1, 2, 5, and 10 minutes post task failure. RESULTS: Old men had greater fatigue resistance than the young with a time to task failure of ∼138 seconds and ∼100 seconds, respectively. Voluntary activation of the triceps surae, as assessed by the interpolated twitch technique, was near maximal (> 99%) for all subjects. At task failure, voluntary activation decreased to ∼89% for both age groups. Maximal MUFRs in both groups were reduced ∼44% and contraction duration slowed by ∼30% following the fatigue task. Contraction duration recovered equally in both groups within 2 minutes of recovery, but maximal MUFRs did not recover until 5 minutes in the old men compared with one minute for the young. CONCLUSION: There is no relative age-related difference in the reduction of maximal MUFRs in response to high-intensity fatigue. However, recovery of maximal MUFRS was impaired in the old despite the recovery of contractile properties. Supported by NSERC
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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 | 0.001 | 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 | 0.000 |
| 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".