The Neuromuscular Junction: A Shared Vulnerability in Aging and Disease
Bibliographic record
Abstract
The neuromuscular junction (NMJ) is a specialized synapse essential for effective motor neuron-muscle communication and is increasingly recognized as a vulnerable site in aging and neuromuscular disease. While traditionally considered a final common pathway for motor deficits, accumulating evidence demonstrates that NMJ dysfunction is an early and critical driver of disease onset and progression in conditions such as amyotrophic lateral sclerosis and Charcot-Marie-Tooth disease. This review highlights shared and disease-specific mechanisms contributing to NMJ impairment, including presynaptic, postsynaptic, and perisynaptic Schwann cell defects in these diseases. We also discuss age-related changes at the NMJ, emphasizing its role in sarcopenia and muscle weakness in older adults. Furthermore, we explore emerging molecular drivers of NMJ dysfunction uncovered through studies in congenital myasthenic syndromes, autoimmune disorders, and advanced omics approaches. By integrating insights across diseases and aging, we underscore the potential for shared therapeutic strategies aimed at stabilizing NMJ function. Promising interventions targeting presynaptic neurotransmitter release, postsynaptic excitability, and perisynaptic Schwann cells are discussed as avenues to improve neuromuscular transmission and maintain muscle strength. Finally, we discuss the challenges and opportunities in translating these mechanistic insights into clinical therapies and highlight how novel human neuromuscular organoid models and advanced molecular profiling can bridge this gap. Together, these insights establish the NMJ as a critical, modifiable target for preserving motor function across neuromuscular diseases and aging.
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 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".