Additives in intravenous anesthesia modulates pulmonary inflammation in a model of LPS‐induced respiratory distress
Bibliographic record
Abstract
BACKGROUND: It has been suggested that propofol with ethylenediaminetetraacetic acid (EDTA) can modulate the systemic inflammatory response. Prolonged higher levels of pulmonary inflammation are associated with poor outcome of patients with acute lung injury. In the present study, we hypothesized that pulmonary inflammation could be modulated by propofol with EDTA compared with propofol with sulfite. METHODS: Respiratory distress was induced in rats (n=25) by intratracheal nebulization of lipopolysaccharide (LPS). After 24 h, animals were randomized to either propofol with EDTA (Propofol(EDTA)), propofol with sulfite (Propofol(sulfite)) or ketamine/midazolam (Ket/Mid); control animals received saline (n=30). Animals were ventilated for 4 h and blood gases were measured hourly. Bronchoalveolar lavage (BAL) was performed for cytokine analysis of: tumor necrosis factor (TNF), interleukin (IL)-6 and macrophage inflammatory protein (MIP)-2. RESULTS: LPS led to increased pulmonary inflammation in all groups compared with the control groups. Gas exchange deteriorated over time only in the LPS Propofol(sulfite) group and was significantly lower than the Ket/Mid group. Only IL-6 was significantly higher in the LPS Propofol(sulfite) group compared with both the Ket/Mid group and the Propofol(EDTA) group. CONCLUSION: Pulmonary IL-6 can be modulated by additives in systemic anesthesia. IMPLICATION STATEMENT: This study demonstrates that pulmonary inflammation caused by direct lung injury can be modulated by intravenous anesthesia used in critically ill patients.
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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) | 0.001 | 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".