Distribution of K <sub>ATP</sub> channel among the different fiber types in skeletal muscle.
Bibliographic record
Abstract
The ATP‐sensitive K + channel (K ATP channel) is essential in preventing contractile dysfunctions during exercise and fatigue in skeletal muscle (Cifelli et al. 2007. J. Physiol. 582:843). The extent of the contractile dysfunctions varies in the order of FDB = EDL ≫ soleus. Our studies and others have shown that, in mouse, FDB is composed primarily of type IIA and IIX fibers, the EDL has primarily IIB and IIX while soleus is composed of I and IIA. The objective of this study was to test the hypothesis that the protein content for the K ATP channel is in the order of I < IIA ≪ IIB = IIX. Fiber types were identified using specific antibodies for each of the four myosin isoforms while the KATP channel was quantify using an antibody against the Kir6.2 subunit, which forms the pore of the channel. The data showed large Kir6.2 protein content in type IIA fibers of FDB and soleus, while very low levels were found in these same fibers in EDL. Type IIB (EDL only) and type IIX fibers (EDL & FDB) contained large amount of Kir6.2, while type I (FDB and soleus) had the lowest content. It is concluded that the expression of the Kir6.2 subunit of the KATP channel not only vary among fiber types, but also for type IIA fibers among different muscles. Research supported by an operating grant from the Natural Sciences and Engineering Council of Canada (NSERC).
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".