Assessing Functional Metrics of Skeletal Muscle Health in Human Skeletal Muscle Microtissues
Bibliographic record
Abstract
Three-dimensional (3D) in vitro models of skeletal muscle are a valuable advancement in biomedical research as they afford the opportunity to study skeletal muscle reformation and function in a scalable format that is amenable to experimental manipulations. 3D muscle culture systems are desirable as they enable scientists to study skeletal muscle ex vivo in the context of human cells. 3D in vitro models closely mimic aspects of the native tissue structure of adult skeletal muscle. However, their universal application is limited by the availability of platforms that are simple to fabricate, cost and user-friendly, and yield relatively high quantities of human skeletal muscle tissues. Additionally, since skeletal muscle plays an important functional role that is impaired over time in many disease states, an experimental platform for microtissue studies is most practical when minimally invasive calcium transient and contractile force measurements can be conducted directly within the platform itself. In this protocol, the fabrication of a 96-well platform known as 'MyoTACTIC', and en masse production of 3D human skeletal muscle microtissues (hMMTs) is described. In addition, the methods for a minimally invasive application of electrical stimulation that enables repeated measurements of skeletal muscle force and calcium handling of each microtissue over time are reported.
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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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".