Neuromuscular remodeling and neuromuscular junction instability in human diabetic neuropathy (1168.1)
Bibliographic record
Abstract
Diabetic polyneuropathy (DPN) is a progressive axonopathy marked by loss of motor fibers, compensatory collateral reinnervation and reduced stability of neuromuscular transmission. Our objective was to assess the degree of reinnervation and motor unit instability in patients with DPN using decomposition‐based quantitative electromyography (DQEMG). Additionally, relationships between motor unit stability and muscle function were examined. The tibialis anterior (TA) muscle was tested in twelve patients with DPN (65 ± 15 yrs) and 12 age‐matched controls (63 ± 15 yrs). DQEMG was used to analyze surface and intramuscular EMG signals recorded from the TA during moderate voluntary dorsiflexion contractions. Individual motor unit action potential (MUP) trains were identified and analyzed for: MUP size (peak to peak amplitude, area), complexity (turns, fiber dispersion) and stability (near fiber jiggle). DPN patients featured larger (+45% MUP area), more complex (+40% fiber dispersion), and less stable (+30% near fiber jiggle) MUPs (p<0.05). No significant relationships were found between MUP stability and muscular denervation, or strength. MUP complexity and instability were positively related in DPN patients (r=0.46; p<0.05) and controls (r=0.37; p<0.05). DPN is associated with neuromuscular remodeling which leads to increasingly impaired neuromuscular transmission that is detectable using DQEMG. Grant Funding Source : NSERC
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".