Effects of neuromuscular electrical stimulation on cytokines in peripheral blood for healthy participants: a prospective, single‐blinded Study
Bibliographic record
Abstract
Summary Introduction The effect of exercise on cytokines may improve muscle strength. Neuromuscular electrical stimulation (NMES) is a muscle‐preserving therapy that benefits patients unable to participate in active exercise. How NMES alters cytokines is unclear. The aim of this study was to study the effects of 1 NMES session on cytokines associated with protein metabolism during exercise. Methods We evaluated the effects of NMES on IL‐1, IL‐6, IL‐10 and TNF‐α levels in peripheral blood. Participants received NMES to bilateral lower extremity muscles (quadriceps, tibialis anterior, gastrocnemius) for 30 min. Blood samples immediately pre‐ and post‐NMES were drawn at 15‐min intervals to 2‐h follow‐up, and the mean values of pre‐NMES levels were compared to peak and trough post‐NMES levels. For cytokines with significant changes, we conducted a repeated‐measures linear regression analysis. We also measured post‐NMES lactate and creatine kinase levels. Results We enrolled nine eligible participants. There was a significant increase in peak IL‐6 from the mean pre‐NMES value [0·65 (0·89) to 1·04 (0·89) pg ml−1, P = 0·001] and a significant decrease in trough IL‐1 [0·08 (0·07) to 0·02 (0·02) pg ml−1, P = 0·041] and TNF‐α [2·42 (0·54) to 2·16 (0·59) pg ml−1, P = 0·021]. In repeated‐measures regression analysis, we identified significantly higher mean IL‐6 values throughout the full 120 min post‐NMES period, and a significantly higher mean IL‐1 value at 30 min post‐NMES. There were no significant differences in peak IL‐10, trough IL‐6, lactate, or creatine kinase values. Conclusions In nine healthy humans, 30 min of NMES was temporally associated with changes in cytokines similar to the effects of active exercise and may mediate NMES' observed effects on reducing muscle weakness.
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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 | 0.006 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".