Login / Signup

Fatiguing stimulation increases curvature of the force-velocity relationship in isolated fast-twitch and slow-twitch rat muscles.

Anders Meldgaard KristensenOle Baekgaard NielsenThomas Holm PedersenKristian Overgaard
Published in: The Journal of experimental biology (2019)
In skeletal muscles, the ability to generate power is reduced during fatigue. In isolated muscles, maximal power can be calculated from the force-velocity relationship. This relationship is well described by the Hill equation, which contains three parameters: (1) maximal isometric force, (2) maximum contraction velocity and (3) curvature. Here, we investigated the hypothesis that a fatigue-induced loss of power is associated with changes in curvature of the force-velocity curve in slow-twitch muscles but not in fast-twitch muscles during the development of fatigue. Isolated rat soleus (slow-twitch) and extensor digitorum longus (EDL; fast-twitch) muscles were incubated in Krebs-Ringer solution at 30°C and stimulated electrically at 60 Hz (soleus) and 150 Hz (EDL) to perform a series of concentric contractions to fatigue. Force-velocity data were fitted to the Hill equation, and curvature was determined as the ratio of the curve parameters a/F 0 (inversely related to curvature). At the end of the fatiguing protocol, maximal power decreased by 58±5% in the soleus and 69±4% in the EDL compared with initial values in non-fatigued muscles. At the end of the fatiguing sequence, curvature increased as judged from the decrease in a/F 0 by 81±20% in the soleus and by 31±12% in the EDL. However, during the initial phases of fatiguing stimulation, we observed a small decrease in curvature in the EDL, but not in the soleus, which may be a result of post-activation potentiation. In conclusion, fatigue-induced loss of power is strongly associated with an increased curvature of the force-velocity relationship, particularly in slow-twitch muscles.
Keyphrases
  • single molecule
  • blood flow
  • sleep quality
  • resistance training
  • oxidative stress
  • heart rate
  • diabetic rats
  • blood pressure
  • electronic health record
  • mass spectrometry
  • deep learning
  • high speed