Dissection of the <em>Transversus Abdominis</em> Muscle for Whole-mount Neuromuscular Junction Analysis
Bibliographic record
Abstract
Analysis of neuromuscular junction morphology can give important insight into the physiological status of a given motor neuron. Analysis of thin flat muscles can offer significant advantage over traditionally used thicker muscles, such as those from the hind limb (e.g. gastrocnemius). Thin muscles allow for comprehensive overview of the entire innervation pattern for a given muscle, which in turn permits identification of selectively vulnerable pools of motor neurons. These muscles also allow analysis of parameters such as motor unit size, axonal branching, and terminal/nodal sprouting. A common obstacle in using such muscles is gaining the technical expertise to dissect them. In this video, we detail the protocol for dissecting the transversus abdominis (TVA) muscle from young mice and performing immunofluorescence to visualize axons and neuromuscular junctions (NMJs). We demonstrate that this technique gives a complete overview of the innervation pattern of the TVA muscle and can be used to investigate NMJ pathology in a mouse model of the childhood motor neuron disease, spinal muscular atrophy.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 0.008 |
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".