Multiple System Organ Response Induced by Hyperoxia in a Clinically Relevant Animal Model of Sepsis
Bibliographic record
Abstract
Oxygen therapy is currently used as a supportive treatment in septic patients to improve tissue oxygenation. However, oxygen can exert deleterious effects on the inflammatory response triggered by infection. We postulated that the use of high oxygen concentrations may be partially responsible for the worsening of sepsis-induced multiple system organ dysfunction in an experimental clinically relevant model of sepsis. We used Sprague-Dawley rats. Sepsis was induced by cecal ligation and puncture. Sham-septic controls (n = 16) and septic animals (n = 32) were randomly assigned to four groups and placed in a sealed Plexiglas cage continuously flushed for 24 h with medical air (group 1), 40% oxygen (group 2), 60% oxygen (group 3), or 100% oxygen (group 4). We examined the effects of these oxygen concentrations on the spread of infection in blood, urine, peritoneal fluid, bronchoalveolar lavage, and meninges; serum levels of inflammatory biomarkers and reactive oxygen species production; and hematological parameters in all experimental groups. In cecal ligation and puncture animals, the use of higher oxygen concentrations was associated with a greater number of infected biological samples (P < 0.0001), higher serum levels of interleukin-6 (P < 0.0001), interleukin-10 (P = 0.033), and tumor necrosis factor-α (P = 0.034), a marked decrease in platelet counts (P < 0.001), and a marked elevation of reactive oxygen species serum levels (P = 0.0006) after 24 h of oxygen exposure. Oxygen therapy greatly influences the progression and clinical manifestation of multiple system organ dysfunction in experimental sepsis. If these results are extrapolated to humans, they suggest that oxygen therapy should be carefully managed in septic patients to minimize its deleterious effects.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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) | 0.002 | 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".