Improved Human Muscle Biopsy Method To Study Neuromuscular Junction Structure and Functions with Aging
Bibliographic record
Abstract
Reduced mobility and physical independence of elders has emerged as a major clinical and public health priority with extended life expectancy. The impact of the neuromuscular function on muscle activity and properties has emerged as a critical factor influencing the progress and outcome of muscle changes with aging. However, very little is known about the neuromuscular junctions (NMJs) in humans, in part due to technical constraints limiting the access to healthy, fresh neuromuscular tissue. Here, we describe a method, called Biopsy using Electrostimulation for Enhanced NMJ Sampling (BeeNMJs) that improves the outcome of muscle biopsies. We used local cutaneous stimulation to identify the area enriched with NMJs for each participant at the right Vastus lateralis (VL). The needle biopsy was then performed in proximity of that point. The BeeNMJs procedure was safe for the participants. We observed NMJs in 53.3% of biopsies in comparison with only 16.7% using the traditional method. Furthermore, we observed an average of 30.13 NMJs per sample compared to only 2.33 for the traditional method. Importantly, high-quality neuromuscular material was obtained whereby pre-, postsynaptic, and glial elements were routinely labeled, simultaneously with myosin heavy chain type I. The BeeNMJs approach will facilitate studies of NMJs, particularly in human disease or aging process.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".