527: MECHANICAL POWER CORRELATES WITH NEUROINFLAMMATION IN HEALTHY PIGS VENTILATED FOR 50 HOURS
Bibliographic record
Abstract
Introduction: Clinical studies show an association between mechanical power (MP) of mechanical ventilation (MV) and morbidity and mortality. Preclinical studies have demonstrated that MV is linked to neuroinflammation. Temporary transvenous diaphragm neurostimulation (TTDN) is a novel hybrid ventilatory strategy, combining positive and negative pressure, that has been shown to mitigate neuroinflammation in healthy pigs undergoing MV for 50 hours. This study investigates the correlation between MP over time, generated by MV and by MV plus TTDN, to hippocampal neuroinflammation at study-end. Methods: Two groups of pigs with healthy lungs received volume control MV (PEEP 5 cmH2O, tidal volume 8 ml/kg) for 50 hours: MV (n=8), and MV with TTDN on every breath (MV+TTDN100%, n=7) delivered as previously published. At study-end, the hippocampus was harvested, and ionized calcium-binding adapter molecule-1 (IBA-1) and glial fibrillary acid protein (GFAP) assays were used to stain microglia and astrocytes, respectively. MP was calculated as MP=0.098*RR*VT*(PIP-½(driving pressure)), and total exposure was calculated as the area under the curve. Spearman’s correlation was used for statistical analyses. Results: Total exposure to MP, and hippocampal microglia percentage and astrocyte percentage (measures of neuroinflammation) were all lower in the MV+TTDN100% group than in the MV group. Total exposure to MP correlated moderately with hippocampal microglia percentage (r=0.67, p=0.0080) and strongly with astrocyte percentage (r=0.86, p< 0.0001). Conclusions: Total exposure to MP is moderately to strongly correlated with neuroinflammation in pigs with healthy lungs mechanically ventilated for 50 hours.
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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) | 0.000 | 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) | 0.003 | 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".