The Importance Of Culture Medium And FDB In The Isolation Of Single Fdb Fibers Using A Collagenase Digestion
Bibliographic record
Abstract
PURPOSE: Most studies on muscle fatigue use whole muscle. The disadvantages of such preparations for the study of muscle fatigue are the development of an anoxic core and the impossibility of studying differences between fiber types. While some studies have used mechanically dissected single muscle fibers, the preparation is time consuming and difficult to use for electrophysiological measurements. Another method is to isolate single fibers in large numbers following a collagenase treatment to allow for dispersal of fibers by trituration. Although variations on the method to obtain such single fiber preparations exist, there is no quantitative data indicating quality of preparation in terms of fiber contractility. The objective of this study was to determine the best protocol for fiber isolation from mouse FDB fibers. RESULTS: Fibers isolated following the collagenase digestion in MEM culture medium with 10% FBS had better morphology and contractility (i.e., number of contracting fibers and threshold values) than those in which DMEM was used as a culture medium. Also, in the absence of FBS in culture medium, all fibers supercontracted during trituration regardless of the culture medium used. The addition of 0.2% FBS in the physiological solution bathing fibers during experiments also improved morphology, contractility and stability.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".