Effects of ventilation on the surfactant system in sepsis-induced lung injury
Bibliographic record
Abstract
The present study examined the effects of mechanical ventilation, with or without positive end-expiratory pressure (PEEP), on the alveolar surfactant system in an animal model of sepsis-induced lung injury. Septic animals ventilated without PEEP had a significant deterioration in oxygenation compared with preventilated values (arterial PO(2)/inspired O(2) fraction 316 +/- 16 vs. 151 +/- 14 Torr; P < 0.05). This was associated with a significantly lower percentage of the functional large aggregates (59 +/- 3 vs. 72 +/- 4%) along with a significantly reduced function (minimum surface tension 17.7 +/- 1.8 vs. 11.8 +/- 3.8 mN/m) compared with nonventilated septic animals (P < 0.05). Sham animals similarly ventilated without PEEP maintained oxygenation, percent large aggregates and surfactant function. With the addition of PEEP, the deterioration in oxygenation was not observed in the septic animals and was associated with no alterations in the surfactant system. We conclude that animals with sepsis-induced lung injury are more susceptible to the harmful effects of mechanical ventilation, specifically lung collapse and reopening, and that alterations in alveolar surfactant may contribute to the development of lung dysfunction.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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 | 0.001 |
| 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".