Neuromuscular electrical stimulation but not photobiomodulation therapy improves cardiovascular parameters of rats with heart failure
Bibliographic record
Abstract
The aim of the present study was to analyze the effect of neuromuscular electrical stimulation (NMES) and photobiomodulation (PBMT) on the cardiovascular parameters, hemodynamic function, arterial baroreflex sensitivity (BRS), and autonomic balance (ANS) of rats with heart failure (HF). Male Wistar rats (220–290 g) were organized into five groups: Sham (n = 6), Control-HF (n = 5), NMES-HF (n = 6), PBMT-HF (n = 6), and NMES + PBMT-HF (n = 6). Myocardial infarction (MI) was induced by left coronary artery ligation. Animals were subjected to an eight-week NMES and PBMT protocol. Statistical analysis included the General Linear Model (GLM) followed by a Bonferroni post-hoc test. Rats of the NMES-HF group showed a higher MI area than the Control-HF (P = 0.003), PBMT-HF (P = 0.002), and NMES + PBMT-HF (P = 0.012) groups. NMES-HF and NMES + PBMT-HF showed higher pulmonary congestion (P = 0.004 and P = 0.02) and lower systolic pressure (P = 0.019 and P = 0.002) than the Sham group. NMES + PBMT-HF showed lower mean arterial pressure (P = 0.02) than the Sham group. Control-HF showed a higher heart rate than the NMES-HF and NMES + PBMT-HF (P = 0.017 and P = 0.013) groups. There was no difference in the BRS and ANS variables between groups. In conclusion, eight-week NMES isolated or associated with PBMT protocol reduced basal heart rate, systolic and mean arterial pressure, without influence on baroreflex sensibility and autonomic control, and no effect of PBMT was seen in rats with HF.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| 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".