Presence of neurotrophic factors in skeletal muscle correlates with survival of spinal cord motor neurons
Bibliographic record
Abstract
To determine which combination of skeletal muscle-derived neurotrophic factors may be important for the survival of specific subpopulations of developing spinal cord motor neurons, we used Myf5 and MyoD (myogenic regulatory factors) knockouts, containing differentially committed myogenic precursor cells (MPCc) and immunohistochemistry against several muscle-secreted neurotrophic factors. At the peak of motor neuron cell death, skeletal muscle development is delayed in the back and body wall muscles of Myf5-/- embryos and in the limb muscles of MyoD-/- embryos. We hypothesized that, if the skeletal muscle was indeed an important source of survival factors for motor neurons, the back, the abdominal wall, and the forelimb MPCs of Myf5-/- or MyoD-/- embryos should produce at least some neurotrophic factors necessary for the survival of motor neurons. In this report, we demonstrate that (1) different MPCs lacking Myf5, MyoD, or Myf5/MyoD have different capabilities in providing factors potentially required for the survival of motor neurons and intramuscular nerve branching, (2) MPCs in double-mutant embryos do not contain neurotrophic factors in the absence of myogenic specification, and (3) different subpopulations of MPCs contain different combinations of neurotrophic factors potentially required for the survival of the specific subpopulations of innervating motor neurons.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".