Systemic Effects Induced by Hyperoxia in a Preclinical Model of Intra-abdominal Sepsis
Bibliographic record
Abstract
Supplemental oxygen is a supportive treatment in patients with sepsis to balance tissue oxygen delivery and demand in the tissues. However, hyperoxia may induce some pathological effects. We sought to assess organ damage associated with hyperoxia and its correlation with the production of reactive oxygen species (ROS) in a preclinical model of intra-abdominal sepsis. For this purpose, sepsis was induced in male, Sprague-Dawley rats by cecal ligation and puncture (CLP). We randomly assigned experimental animals to three groups: control (healthy animals), septic (CLP), and sham-septic (surgical intervention without CLP). At 18 h after CLP, septic ( n = 39 ), sham-septic ( n = 16 ), and healthy ( n = 24 ) animals were placed within a sealed Plexiglas cage and randomly distributed into four groups for continuous treatment with 21%, 40%, 60%, or 100% oxygen for 24 h. At the end of the experimental period, we evaluated serum levels of cytokines, organ damage biomarkers, histological examination of brain and lung tissue, and ROS production in each surviving animal. We found that high oxygen concentrations increased IL-6 and biomarkers of organ damage levels in septic animals, although no relevant histopathological lung or brain damage was observed. Healthy rats had an increase in IL-6 and aspartate aminotransferase at high oxygen concentration. IL-6 levels, but not ROS levels, are correlated with markers of organ damage. In our study, the use of high oxygen concentrations in a clinically relevant model of intra-abdominal sepsis was associated with enhanced inflammation and organ damage. These findings were unrelated to ROS release into circulation. Hyperoxia could exacerbate sepsis-induced inflammation, and it could be by itself detrimental. Our study highlights the need of developing safer thresholds for oxygen therapy.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".