Emerging new tools to study and treat muscle pathologies: genetics and molecular mechanisms underlying skeletal muscle development, regeneration, and disease
Bibliographic record
Abstract
Skeletal muscle is the most abundant tissue in our body, is responsible for generating the force required for movement, and is also an important thermogenic organ. Skeletal muscle is an enigmatic tissue because while on the one hand, skeletal muscle regeneration after injury is arguably one of the best-studied stem cell-dependent regenerative processes, on the other hand, skeletal muscle is still subject to many degenerative disorders with few therapeutic options in the clinic. It is important to develop new regenerative medicine-based therapies for skeletal muscle. Future therapeutic strategies should take advantage of rapidly developing technologies enabling the differentiation of skeletal muscle from human pluripotent stem cells, along with precise genome editing, which will go hand in hand with a steady and focused approach to understanding underlying mechanisms of skeletal muscle development, regeneration, and disease. In this review, I focus on highlighting the recent advances that particularly have relied on developmental and molecular biology approaches to understanding muscle development and stem cell function. Copyright © 2016 Pathological Society of Great Britain and Ireland. Published by John Wiley & Sons, Ltd.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.004 |
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".