Fatigue pre‐conditioning increases fatigue resistance and protects muscle against the deleterious effects of no K <sub>ATP</sub> channel activity
Bibliographic record
Abstract
The objective of this study was to determine how one fatigue bout at 37°C after the kinetics of subsequent fatigue bout in CD‐1 mouse flexor digitorum brevis (FDB). All fatigue bouts were elicited with one tetanic contraction every sec for 3 min. The first fatigue bouts (FAT1) were all elicited under control conditions while the second fatigue bouts (FAT2) were elicited 60 min after FAT1 either under control conditions or in the presence of 10 μM glibenclamide, a K ATP channel blocker. Another group of FDBs were fatigued only once but at the same time as FAT2, defined as delayed FAT1 (delFAT1), also under control conditions or in the presence of glibenclamide. Under both control and glibenclamide conditions, the rate of fatigue, measured from the decrease in tetanic intracellular Ca 2+ ([Ca 2+ ] i ) and tetanic force, was significantly slower during FAT2 than during delFAT1; i.e., fatigue resistance increased after one fatigue bout. The presence of glibenclamide during delFAT1 resulted in several contractile dysfunctions including large increases in resting [Ca 2+ ] i and resting tension, supercontracted single muscle fibers and diminished force recovery. During FAT2, none of these contractile dysfunctions was observed in the presence of glibenclamide. Furthermore, while during delFAT1 the rate of fatigue was faster in the presence than in the absence of glibenclamide, during FAT2 the rate of fatigue was the same for control and glibenclamide conditions. It is concluded that following one fatigue bout at 37°C fatigue resistance increases and the dependency on K ATP channel to prevent contractile dysfunctions decreases. We define this new phenomenon as fatigue preconditioning.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".