Effect of Mechanical Ventilation on Cytokine Response to Intratracheal Lipopolysaccharide
Bibliographic record
Abstract
BACKGROUND: Mechanical ventilation may cause lung injury through the excitation of an inflammatory response and the release of mediators, such as cytokines. The authors tested the hypothesis that intratracheal lipopolysaccharide amplifies the cytokine response to mechanical ventilation. METHODS: Rat lungs were intratracheally instilled with lipopolysaccharide followed by ex vivo mechanical ventilation for 2 h with low tidal volume of 7 ml/kg with 3 cm H2O positive end-expiratory pressure (PEEP), high tidal volume of 40 ml/kg with zero PEEP, medium tidal volume of 15 ml/kg with 3 cm H2O PEEP, or medium tidal volume and zero PEEP. RESULTS: In the absence of lipopolysaccharide, lung lavage concentrations of tumor necrosis factor and interleukin 1 beta but not macrophage inflammatory protein 2 were significantly higher in lungs ventilated at high tidal volume/zero PEEP than at low tidal volume. There was a marked increase in lavage tumor necrosis factor and macrophage inflammatory protein 2 concentrations in lungs ventilated at low tidal volume after exposure to intratracheal lipopolysaccharide at doses of 100 ng/ml or greater. However, in lungs ventilated at high tidal volume, this response to lipopolysaccharide was markedly reduced. In addition, the number of alveolar macrophages recovered in the lavage was significantly lower in lungs ventilated at high tidal volume. CONCLUSION: Ventilation strategy can modify lung cytokine responses to lipopolysaccharide, likely through an effect on the alveolar macrophage population.
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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) | 0.002 | 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".