Modeling the neuromuscular junction <i>in vitro</i>: an approach to study neuromuscular junction disorders
Bibliographic record
Abstract
The neuromuscular junction (NMJ) is a specialized structure that works as an interface to translate the action potential of the presynaptic motor neuron (MN) in the contraction of the postsynaptic myofiber. The design of appropriate experimental models is essential to have efficient and reliable approaches to study NMJ development and function, but also to generate conditions that recapitulate distinct features of diseases. Initial studies relied on the use of tissue slices maintained under the same environment and in which single motor axons were difficult to trace. Later, MNs and muscle cells were obtained from primary cultures or differentiation of progenitors and cocultured as monolayers; however, the tissue architecture was lost. Current approaches include self-assembling 3D structures or the incorporation of biomaterials with cells to generate engineered tissues, although the incorporation of Schwann cells remains a challenge. Thus, numerous investigations have established different NMJ models, some of which are quite complex and challenging. Our review summarizes the in vitro models that have emerged in recent years to coculture MNs and skeletal muscle, trying to mimic the healthy and diseased NMJ. We expect our review may serve as a reference for choosing the appropriate experimental model for the required purposes of investigation.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".