The effects of exogenous surfactant administration on ventilation-induced inflammation in mouse models of lung injury
Bibliographic record
Abstract
BACKGROUND: Mechanical ventilation (MV) is an essential supportive therapy for acute lung injury (ALI); however it can also contribute to systemic inflammation. Since pulmonary surfactant has anti-inflammatory properties, the aim of the study was to investigate the effect of exogenous surfactant administration on ventilation-induced systemic inflammation. METHODS: Mice were randomized to receive an intra-tracheal instillation of a natural exogenous surfactant preparation (bLES, 50 mg/kg) or no treatment as a control. MV was then performed using the isolated and perfused mouse lung (IPML) set up. This model allowed for lung perfusion during MV. In experiment 1, mice were exposed to mechanical ventilation only (tidal volume =20 mL/kg, 2 hours). In experiment 2, hydrochloric acid or air was instilled intra-tracheally four hours before applying exogenous surfactant and ventilation (tidal volume =5 mL/kg, 2 hours). RESULTS: For both experiments, exogenous surfactant administration led to increased total and functional surfactant in the treated groups compared to the controls. Exogenous surfactant administration in mice exposed to MV only did not affect peak inspiratory pressure (PIP), lung IL-6 levels and the development of perfusate inflammation compared to non-treated controls. Acid injured mice exposed to conventional MV showed elevated PIP, lung IL-6 and protein levels and greater perfusate inflammation compared to air instilled controls. Instillation of exogenous surfactant did not influence the development of lung injury. Moreover, exogenous surfactant was not effective in reducing the concentration of inflammatory cytokines in the perfusate. CONCLUSIONS: The data indicates that exogenous surfactant did not mitigate ventilation-induced systemic inflammation in our models. Future studies will focus on altering surfactant composition to improve its immuno-modulating activity.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 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 | 0.002 |
| 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".