Contractile dysfunction in K <sub>ATP</sub> channel deficient muscle during fatigue involves Ca <sup>2+</sup> influx and reactive oxygen species
Bibliographic record
Abstract
A previous study has shown that K ATP channel deficient muscles have faster rate of fatigue than wild type muscles and that the apparent faster fatigue rate was probably because of several contractile dysfunctions that include large increases in resting [Ca 2+ ] i and resting tension as well as poor force recovery after fatigue. The objective of this study was to test the hypothesis that the contractile dysfunctions are due to a Ca 2+ influx thought L‐type Ca 2+ channels and increased production of reactive oxygen species (ROS). To test this hypothesis, FDB muscle bundles from wild type and Kir6.2 −/− mice, which has no K ATP channel activity in the cell membrane, were fatigued with one contraction per sec for 3 min. Lowering [Ca 2+ ] e from 2.4 mM (control) to 0.6 mM or adding 20 μM verapamil, to partially blocked L‐type Ca 2+ channels, reduced the increased in resting tension in Kir6.2 −/− FDB and improved force recovery, while it had no effects in wild type FDB. Exposing Kir6.2 −/− FDB to 10 mM NAC or triron, two ROS scavengers, also reduced resting tension and improve force recovery in Kir6.2 −/− FDB. Furthermore, exposing Kir6.2 −/− FDB to either low [Ca 2+ ] e , verapamil, NAC or triron had no effect on the pre‐fatigue peak tetanic force, but significantly reduced the rate of fatigue whereas it had absolutely not effect in wild type FDB. It is therefore concluded that the contractile dysfunctions in Kir6.2 −/− FDB involved a Ca 2+ influx through L‐type Ca 2+ channels and the production of ROS. Our data also suggest that the contractile dysfunctions are also responsible for the apparent faster rate of fatigue in K ATP channel deficient muscle.
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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 | 0.001 | 0.001 |
| 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".