Verapamil and NAC reduce the excess and damaging increase in myoplasmic calcium concentration during fatigue
Bibliographic record
Abstract
Muscles that lack KATP channel activity generate much greater resting [Ca2+]i and force than normal muscles. The large increase in resting force in KATP channel deficient muscles is completely abolished with 1 μM verapamil, a L-type Ca2+ channel blocker, which suggests that the increase in resting force was due to a Ca2+ influx through L-type Ca2+ channel. However, we recently found that NAC, a ROS scavenger, also reduces resting force. The objective of this study was to test the hypothesis that “the excess increases in resting [Ca2+]i during fatigue in KATP channel deficient muscles starts with an excess Ca2+ influx through L-type Ca2+ channels, followed by excess ROS production that somehow causes a further increase in resting [Ca2+]i”. At 1 μm, verapamil had no effect on tetanic and resting Ca2+ before fatigue, but significantly reduced resting [Ca2+]i in KATP channel deficient fibers. At 1 mM, NAC did not affect contractility and reduced the increase in resting [Ca2+]i in KATP channel deficient fibers. It is therefore suggested that the excess increased in resting [Ca2+]i during fatigue in KATP channel deficient FDB fibers is not completely due to an influx through L-type Ca2+ channels as it may involve excess ROS production acting on proteins that regulate [Ca2+]i (e.g., the Ca2+ ATPase).
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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) | 0.001 | 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) | 0.001 | 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".