Decreased muscle endurance associated with diabetic neuropathy may be attributed partially to neuromuscular transmission failure
Bibliographic record
Abstract
Diabetic polyneuropathy (DPN) can cause muscle atrophy, weakness, contractile slowing, and neuromuscular transmission instability. Our objective was to assess the response of the impaired neuromuscular system of DPN in humans when stressed with a sustained maximal voluntary contraction (MVC). Baseline MVC and evoked dorsiflexor contractile properties were assessed in DPN patients (n = 10) and controls (n = 10). Surface electromyography was used to record tibialis anterior evoked maximal compound muscle action potentials (CMAPs) and neuromuscular activity during MVCs. Participants performed a sustained isometric dorsiflexion MVC for which task termination was determined by the inability to sustain ≥60% MVC torque. The fatigue protocol was immediately followed by a maximal twitch, with additional maximal twitches and MVCs assessed at 30 s and 2 min postfatigue. DPN patients fatigued ∼21% more quickly than controls (P < 0.05) and featured less relative electromyographic activity during the first one-third of the fatigue protocol compared with controls (P < 0.05). Immediately following fatigue, maximal twitch torque was reduced similarly (∼20%) in both groups, and concurrently CMAPs were reduced (∼12%) in DPN patients, whereas they were unaffected in controls (P > 0.05). Twitch torque and CMAP amplitude recovered to baseline 30 s postfatigue. Additionally, at 30 s postfatigue, both groups had similar (∼10%) reductions in MVC torque relative to baseline, and MVC strength recovered by 2 min postfatigue. We conclude DPN patients possess less endurance than controls, and neuromuscular transmission failure may contribute to this greater fatigability.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".