Diaphragm Neurostimulation Mitigates Ventilation-Associated Brain Injury in a Preclinical Acute Respiratory Distress Syndrome Model
Bibliographic record
Abstract
CONTEXT: In a porcine healthy lung model, temporary transvenous diaphragm neurostimulation (TTDN) for 50 hours mitigated hippocampal apoptosis and inflammation associated with mechanical ventilation (MV). HYPOTHESIS: Explore whether TTDN in combination with MV for 12 hours mitigates hippocampal apoptosis and inflammation in an acute respiratory distress syndrome (ARDS) preclinical model. METHODS AND MODELS: Compare hippocampal apoptosis, inflammatory markers, and serum markers of neurologic injury between never ventilated subjects and three groups of mechanically ventilated subjects with injured lungs: MV only (LI-MV), MV plus TTDN every other breath, and MV plus TTDN every breath. MV settings in volume control were tidal volume 8 mL/kg and positive end-expiratory pressure 5 cm H 2 O. Lung injury, equivalent to moderate ARDS, was achieved by infusing oleic acid into the pulmonary artery. RESULTS: Hippocampal apoptosis, microglia, and reactive-astrocyte percentages were similar between the TTDN-every-breath and never ventilated groups. The LI-MV group had a higher percentage of these measures than all other groups ( p < 0.05). Transpulmonary driving pressure at study end was lower in the TTDN-every-breath group than in the LI-MV group; systemic inflammation and lung injury scores were not significantly different. The TTDN-every-breath group had considerably lower serum concentration of homovanillic acid (cerebral dopamine production surrogate) at study end than the LI-MV group ( p < 0.05). Heart rate variability declined in the LI-MV group and increased in both TTDN groups ( p < 0.05). INTERPRETATIONS AND CONCLUSIONS: In a moderate-ARDS porcine model, MV is associated with hippocampal apoptosis and inflammation, and TTDN mitigates that hippocampal apoptosis and inflammation.
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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".