Stimulation of the Efferent Vagus Nerve Mitigates Acute Lung Injury and Ventilator Induced Lung Injury
Bibliographic record
Abstract
Rationale Ventilator induced lung injury (VILI) significantly contributes to the mortality in patients with acute respiratory distress syndrome. Objectives To determine the role of vagus nerve signaling in VILI. Methods To determine whether vagus signaling plays a role in VILI we first demonstrated that disruption of the CAP reflex by bilateral vagotomy results in worsening lung injury in a mouse model of high‐volume‐induced lung injury. In a clinically relevant model of injurious mechanical ventilation following lung injury induced by hemorrhagic shock/resuscitation we then tested the hypothesis that electrical and pharmacological stimulation of the vagus nerve can attenuate lung injury in rats. Results Vagotomy exacerbates lung injury from high volume ventilation in mice as demonstrated by increased wet‐to‐dry ratio, infiltration of neutrophils in bronchoalveolar lavage fluid and lung tissues, and increased tissue levels of interleukin‐6. Vagotomy exacerbated while vagus stimulation attenuates lung injury in rats after ischemia reperfusion injury ventilated with either high or low volume strategies. Treatment of both mice and rats with the vagus mimetic drug, semapimod, resulted in decreased lung injury. Vagotomy also increased pulmonary apoptosis whilst vagus stimulation attenuated VILI‐induced apoptosis. In‐vitro studies suggest that vagus‐dependent effects on inflammation and apoptosis are mediated via the alpha 7 subunit of the acetylcholine nicotinic receptor effect on cyclic stretch‐dependent singling pathways c‐jun N‐terminal kinase (JNK) and Fas (TNF receptor superfamily, member 6). Conclusion Stimulation of the cholinergic anti‐inflammatory reflex may represent a promising alternative for the treatment of VILI.
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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) | 0.000 | 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 | 0.001 |
| 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".