Fiber type specific PGC‐1α content and its relation to capillarization and mitochondrial content
Bibliographic record
Abstract
PGC‐1α is a central protein in skeletal muscle physiology. In addition of being the master regulator of mitochondrial biology, this coactivator of transcription has been implicated in the regulation of angiogenesis and has been shown to drive the formation of slow‐twitch fibers. The objective of the present study was, for the first time, to determine the relationship between PGC‐1α content, fiber type, capillarization, and mitochondrial content in rat skeletal muscle. To this end, serial sections of plantaris muscle were used to determine (i) fiber type (immunolabeling for MHC I, IIa, IIx, and IIb), (ii) mitochondrial content (succinate dehydrogenase stain), (iii) capillarization (Lead ATPase) and (iv) PGC‐1α content (immunolabeling). Surprisingly, we found that type IIa fibers have the highest PGC‐1α content, whereas no significant differences were observed between other fiber types. Associated with their higher PGC‐1α content, Type IIa fibers also displayed the highest mitochondrial content, followed by type I>;IIx>;IIb. However, type I displayed the highest number of capillaries per fiber perimeter, followed by type IIa>;IIx>;IIb. Although type IIa fibers show the higest PGC‐1α and mitochondrial content, our results suggest that neither basal mitochondrial content nor capillarization are directly related to differences in PGC‐1α content between fiber types in rat skeletal 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 | 0.001 | 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) | 0.001 | 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".