CONTRACTILE PROTEIN NUMBER IN ADJACENT SARCOMERES – IS THERE A DIFFERENCE?
Bibliographic record
Abstract
INTRODUCTION Variation in sarcomere length within an actively contracting skeletal muscle has often been observed yet remains unexplained. Sarcomeres within a myofibril are connected in series and thus they must each produce the same amount of force [1]. We know that the amount of force that a sarcomere can produce is related to the amount of overlap between the actin and myosin filaments within it. As a result, sarcomere length affects the amount of force that a sarcomere can produce [2]. If we assume that sarcomeres within a myofibril contain the same number of contractile proteins, it would be expected that sarcomeres within a myofibril would be of the same or similar length thus producing the same amount of force. However, it has been observed that sarcomere lengths vary within myofibrils [3] causing us to hypothesize that contractile protein number differs between adjacent sarcomeres. The purpose of this study was to identify the number of myosin filaments in serially arranged sarcomeres and determine if this number differed. The hope was to provide a possible explanation for the difference in serially arranged sarcomere lengths within myofibrils. METHODS Bundles of rabbit psoas muscle fibers about 2 mm in diameter were harvested and placed in a modified Karnovsky’s fixative. The fibers were post fixed with 1% osmium tetroxide and then put through a standard dehydration and infiltration process. The muscle samples were teased apart under a dissecting scope to get bundles about 100 μm in diameter and embedded in Embed 812 Resin. Blocks were cut into 100 nm thick slices perpendicularly to the embedded samples to obtain cross sections of the muscle samples. Sectioning was done using an ultracut microtome. The sections were stained with uranyl acetate and lead citrate and viewed under an electron microscope to obtain images of myofibril cross sections. The number of myosin filaments present was then counted manually. RESULTS An electron micrograph of a sarcomere cross section was obtained and is shown in Figure 1. The number of myosin filaments counted was 715 in this sarcomere of approximate cross sectional area 0.7 μm 2 . LIMITATIONS Due to the size of our samples (about 100 μm in diameter) compared to the size of a single myofibril (about 1 μm in diameter) we were not able to follow a single myofibril in multiple micrographs and compare the number of myosin filaments in adjacent sarcomeres. DISCUSSION AND CONCLUSIONS In this study we were successful in obtaining an electron micrograph of a sarcomere in cross section and counting the number of myosin filaments within it. We anticipate that when we are successful in counting the number of myosin filaments in adjacent sarcomeres, we will find a difference, thus providing a possible explanation for the variance in sarcomere length within a myofibril. From this investigation we have identified a viable method for imaging sarcomeres in cross section and counting the number of myosin filaments within them. The next step is to refine the protocol in order to embed smaller samples. With smaller samples it will be easier to locate and track a single myofibril in multiple electron micrographs and compare the number of myosin filaments in adjacent sarcomeres.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".