The effects of KATP channels on skeletal muscle fatigue and recovery are dependent on muscle stimulus frequency and fibre type (1102.9)
Bibliographic record
Abstract
ATP sensitive potassium (KATP) channels are hypothesized to protect against ATP depletion during maximal muscle contraction by decreasing force production, however their function in submaximal contractions and recovery is unknown. We sought to test whether KATP channels altered the force of contraction during fatigue and recovery of mouse slow (soleus; SOL) and fast (extensor digitorum longus; EDL) twitch muscle, in vitro, at maximal and submaximal stimulus frequencies. We fatigued SOL (60Hz, 60 contractions per minute (CPM) and 20Hz, 60CPM) and EDL (100Hz, 60CPM and 40Hz, 60CPM) for 5 minutes in the absence or presence of a KATP channel inhibitor (10‐5M glibenclamide; GLIB) or a KATP channel opener (10‐5M pinacidil; PIN) and then observed recovery (SOL: 60Hz 0.6 CPM and 20Hz, 0.6CPM; EDL:100Hz 0.6CPM and 40Hz 0.6 CPM). GLIB had no effect on fatigue or recovery of SOL or EDL at maximum stimulus frequencies but significantly attenuated force in SOL (12.2%) and EDL (6.9%) at submaximal frequencies. GLIB significantly enhanced force during recovery of SOL by 22.5% at submaximal stimulus frequencies. PIN did not affect fatigue of SOL or EDL at maximal stimulus frequencies or the recovery of SOL but significantly attenuated force of EDL during recovery by 8.9%. PIN had no effect on fatigue or recovery during submaximal contractions. Thus, the effect of KATP channels is dependent on both fibre type and stimulus frequency. Grant Funding Source : Supported by NSERC, Canada
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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) | 0.000 | 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) | 0.002 | 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".