Stilbazulenyl Nitrone Decreases Oxidative Stress and Reduces Lung Injury After Hemorrhagic Shock/Resuscitation and LPS
Bibliographic record
Abstract
Multiorgan failure is a major cause of late morbidity and mortality after trauma. Reactive oxygen species generated during shock/resuscitation contribute to tissue injury by priming the immune system for an exaggerated response to subsequent inflammatory stimuli such as LPS. Stilbazulenyl nitrone (STAZN) is a novel second-generation azulenyl nitrone that has been shown to have potent antioxidant properties in a rat model of brain ischemia. We hypothesized that STAZN may confer protection against lung injury after shock/resuscitation and LPS by reducing oxidative stress and lowering the production of NF-kappaB-dependent pro-inflammatory cytokines. Sprague-Dawley rats were submitted to a two-hit model of lung injury involving hemorrhagic shock/resuscitation and subsequent intratracheal LPS injection, with and without intraperitoneal injections of STAZN. STAZN reduced overall lung injury in response to LPS alone and also after shock/resuscitation plus LPS. STAZN also reduced plasma levels of 8-isoprostane, a proxy measure of oxidative stress, indicating its antioxidant activity in vivo. The effect of STAZN was, at least in part, related to its effect on nuclear translocation of NF-kappaB and generation of the pro-inflammatory cytokine TNF-alpha. Azulenyl nitrones such as STAZN represent a promising novel class of antioxidants for treating organ injury.
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 | 0.000 |
| 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) | 0.001 | 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".