Vagus nerve stimulation improves coagulopathy in hemorrhagic shock: a thromboelastometric animal model study
Bibliographic record
Abstract
INTRODUCTION: Inflammation plays a major role in the multifactorial process of trauma associated coagulopathy. The vagus nerve regulates the cholinergic anti-inflammatory pathway. We hypothesized that efferent vagus nerve stimulation (VNS) can improve coagulopathy by modulating the inflammatory response to hemorrhage. METHODS: Wistar rats (n = 24) were divided in 3 groups: Group (G1) Sham hemorrhagic shock (HS); (G2) HS w/o VNS; (G3) HS followed by division of the vagus nerves and VNS of the distal stumps. Hemorrhage (45% of baseline MAPx15 minutes) was followed by normotensive resuscitation with LR. Vagus nerves were stimulated (3.5 mA, 5 Hz) for 30 sec 7 times. Samples were obtained at baseline and at 60 minutes for thromboelastometry (Rotem®) and cytokine assays (IL-1 and IL-10). ANOVA was used for statistical analysis; significance was set at p < 0.05. RESULTS: Maximum clot firmness (MCF) significantly decreased in G2 after HS (71.5 ± 1.5 vs. 64 ± 1.6) (p < 0.05). MCF significantly increased in G3 compared to baseline (67.3 ± 2.7 vs. 71.5 ± 1.2) (p < 0.05). G3 also showed significant improvement in Alfa angle, and Clot Formation Time (CFT) compared to baseline. IL-1 increased significantly in group 2 and decrease in group 3, while IL-10 increased in group 3 (p < 0.05). CONCLUSIONS: Electrical stimulation of efferent vagus nerves, during resuscitation (G3), decreases inflammatory response to hemorrhage and improves coagulation.
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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 | 0.001 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".