Rapid Determination of Myosin Heavy Chain Expression in Rat, Mouse, and Human Skeletal Muscle Using Multicolor Immunofluorescence Analysis
Bibliographic record
Abstract
Skeletal muscle is a heterogeneous tissue comprised of fibers with different morphological, functional, and metabolic properties. Different muscles contain varying proportions of fiber types; therefore, accurate identification is important. A number of histochemical methods are used to determine muscle fiber type; however, these techniques have several disadvantages. Immunofluorescence analysis is a sensitive method that allows for simultaneous evaluation of multiple MHC isoforms on a large number of fibers on a single cross-section, and offers a more precise means of identifying fiber types. In this investigation we characterized pure and hybrid fiber type distribution in 10 rat and 10 mouse skeletal muscles, as well as human vastus lateralis (VL) using multicolor immunofluorescence analysis. In addition, we determined fiber type-specific cross-sectional area (CSA), succinate dehydrogenase (SDH) activity, and α-glycerophosphate dehydrogenase (GPD) activity. Using this procedure we were able to easily identify pure and hybrid fiber populations in rat, mouse, and human muscle. Hybrid fibers were identified in all species and made up a significant portion of the total population in some rat and mouse muscles. For example, rat mixed gastrocnemius (MG) contained 12.2% hybrid fibers whereas mouse white tibialis anterior (WTA) contained 12.1% hybrid fibers. Collectively, we outline a simple and time-efficient method for determining MHC expression in skeletal muscle of multiple species. In addition, we provide a useful resource of the pure and hybrid fiber type distribution, fiber CSA, and relative fiber type-specific SDH and GPD activity in a number of rat and mouse muscles.
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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 | 0.001 | 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) | 0.002 | 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".