Ventilation with Negative Airway Pressure Induces a Cytokine Response in Isolated Mouse Lung
Bibliographic record
Abstract
UNLABELLED: We tested the hypothesis that, under relatively low tidal volume (VT) mechanical ventilation, continuing lung decruitment induced by negative end-expiratory pressure (NEEP) would increase the lung cytokine response, potentially contributing to lung injury. Mouse lungs were excised and randomly assigned to one of 3 different ventilatory strategies: 1) the zero end-expiratory pressure group served as a control, 2) the NEEP7 group received a NEEP of -7.5 cm H(2)O, and 3) the NEEP15 group received a NEEP of -15 cm H(2)O. In all 3 groups, a VT of 7 mL/kg was used. After 2 h of ventilation, lung lavage fluid was collected for measurements of tumor necrosis factor-alpha, monocyte chemoattractant protein-1, and lactate dehydrogenase. Increases in plateau pressure before and after mechanical ventilation were significantly greater in the NEEP15 group compared with the zero end-expiratory pressure group or NEEP7 group. Lung compliance was decreased in the NEEP15 compared with the other two groups. Concentrations of tumor necrosis factor-alpha, monocyte chemoattractant protein-1, and lactate dehydrogenase in lung lavage were larger in the NEEP15 group than in the other groups. Atelectatic lung during repeated collapse and reopening of lung units accentuates the lung cytokine response that may contribute to lung injury even during relatively low VT mechanical ventilation. IMPLICATIONS: Repeated closing and reopening of lung units induced by negative end-expiratory pressure resulted in lung inflammation and cell injury even under mechanical ventilation using a normal tidal volume. This finding may have clinical relevance in certain patients who are prone to atelectasis during mechanical ventilation.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".