Anti-Inflammatory Resuscitation Improves Survival in Hemorrhage With Trauma
Bibliographic record
Abstract
BACKGROUND: Hemorrhage is a common cause of death despite the recent advances in resuscitation and critical care. Conventional resuscitation fluids are designed to reestablish tissue perfusion, but they fail to prevent systemic inflammation. Indeed, resuscitation can promote inflammatory responses, which can be more dangerous than the original hemorrhage. This consideration is relevant in critical care where hemorrhage is normally associated with collateral trauma that can exacerbate the inflammatory responses during resuscitation. Here, we analyzed whether ethyl pyruvate could provide a therapeutic anti-inflammatory potential during resuscitation in experimental hemorrhage with trauma. METHODS: Adult male Sprague-Dawley rats were subjected to trauma induced by closed femur fracture. Then, the animals were immediately subjected to lethal hemorrhage during 15 minutes to reach a mean arterial blood pressure of 35 mm Hg to 40 mm Hg and subsequent maintenance of this mean arterial blood pressure for another 15 minutes. Resuscitation was limited to 15 mL/kg Hextend with or without ethyl pyruvate. RESULTS: Resuscitation with conventional fluids reestablished normal tissue perfusion, but still more than 60% of the animals died. Resuscitation with ethyl pyruvate protected all the animals from lethal hemorrhage with trauma. Trauma exacerbated tumor necrosis factor (TNF) levels in the serum, the spleen, and the heart. Ethyl pyruvate blunted TNF levels in the serum and all the organs but particularly in the lung and the liver during resuscitation. TNF levels in the lung, spleen, and the liver of those animals resuscitated with ethyl pyruvate were statistically similar to those in control animals. CONCLUSION: Ethyl pyruvate may attenuate systemic inflammatory responses during resuscitation and improve survival in experimental models of hemorrhage with trauma.
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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 | 0.000 |
| 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".