Bibliographic record
Abstract
Myostatin, or GDF-8 (growth and differentiation factor-8), was first identified through sequence identity with members of the BMP (bone morphogenetic protein)/TGF-β (transforming growth factor-β) superfamily. The skeletal-muscle-specific expression pattern of myostatin suggested a role in muscle development. Mice with a targeted deletion of the myostatin gene exhibit a hypermuscular phenotype. In addition, inactivating mutations in the myostatin gene have been identified in ‘double muscled’ cattle breeds, such as the Belgian Blue and Piedmontese, as well as in a hypermuscular child. These findings define myostatin as a negative regulator of skeletal-muscle development. Myostatin binds with high affinity to the receptor serine threonine kinase ActRIIB (activin type IIB receptor), which initiates signalling through a smad2/3-dependent pathway. In an effort to validate myostatin as a therapeutic target in a post-embryonic setting, a neutralizing antibody was developed by screening for inhibition of myostatin binding to ActRIIB. Administration of this antimyostatin antibody to adult mice resulted in a significant increase in both muscle mass and functional strength. Importantly, similar results were obtained in a murine model of muscular dystrophy, the mdx mouse. Unlike the myostatin-deficient animals, which exhibit both muscle hypertrophy and hyperplasia, the antibody-treated mice demonstrate increased musculature through a hypertrophic mechanism. These results validate myostatin inhibition as a therapeutic approach to muscle wasting diseases such as muscular dystrophy, sarcopenic frailty of the elderly and amylotrophic lateral sclerosis.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".