Development of skeletal muscles in transforming growth factor-beta 1 (TGF-?1) null-mutant mice
Bibliographic record
Abstract
Fetal transforming growth factor-beta 1 (TGF-beta1) has been postulated to regulate the onset of myotube formation and/or pattern formation in developing skeletal muscles. In apparent contradiction of these hypotheses, the development of the extensor digitorum longus and soleus in TGF-beta1 null-mutant muscle was normal. The onset of secondary myotube formation, the numbers of myotubes formed, the proportion of fast and slow fibers, and the patterns of fiber types and connective tissues were essentially identical in TGF-beta1(+/+) and TGF-beta1(-/-) mice. A portion of the TGFbeta1 in skeletal muscles is derived from the mother, via the placenta. This maternal-derived TGF-beta1 was also not essential for the development of skeletal muscles, as the characteristics of pups born to a TGF-beta1(-/-) mother were normal TGF-beta1(-/-) mice die at weaning due to a generalized autoimmune attack. This postnatal death was circumvented by breeding the TGF-beta1 null mutation into nude mice (Whn(-/-)). Like many other strains of TGF-beta1(-/-) mice, extensive loss of Whn(-/-), TGF-beta1(-/-) embryos occurred in utero. However, a portion of the Whn(-/-), TGF-beta1(-/-) mice survived past weaning, remained healthy, and were fertile. The TGF-beta1(-/-) x Whn(-/-) mouse thus represents a valuable tool for the study of the function of TGF-beta1 in the adult, including its putative role as a pregnancy-related hormone.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".