Glucose and glycogen affects Ca <sup>2+</sup> <scp>transient during</scp> fatigue to a greater extent in the least than in the most fatigue resistant mouse <scp>FDB</scp> fibers
Bibliographic record
Abstract
Abstract The overall objective was to determine how no extracellular glucose and/or low glycogen content affect fatigue kinetics in mouse flexor digitorum brevis (FDB) single muscle fibers. High glycogen content (Hi GLY), near normal in situ level, was obtained by incubating fibers in culture medium containing glucose and insulin while low glycogen content (Lo GLY), at about 19% of normal in situ level, was achieved by incubating fibers without glucose. Neither Lo GLY nor the absence of extracellular glucose (0GLU) affected tetanic [Ca 2+ ] i prior to fatigue. The number of contracting unfatigued fibers versus stimulus strength relationship of Lo GLY‐0GLU fibers was shifted to higher voltages compared to Hi GLY fibers exposed to 5.5 mM glucose (5GLU). The relationship for Lo GLY‐0GLU fibers was shifted back toward that of Hi GLY‐5GLU fibers when glucose was reintroduced, whereas the removal of glucose from Hi GLY‐5GLU fibers had no effect. Fatigue was elicited with one 200 ms long tetanic contraction every s for 3 min. Both Lo GLY and 0GLU increased the rate at which intracellular tetanic concentration ([Ca 2+ ] i ) declined and unstimulated [Ca 2+ ] i increased during fatigue in the order of the least fatigue resistant > mid fatigue resistant > the most fatigue resistant fibers.
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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".