Stilbazulenyl nitrone (STAZN), a novel azulenyl nitrone antioxidant prevents lung injury following hemorrhagic shock and alters TLR4 distribution in alveolar macrophages
Bibliographic record
Abstract
Introduction: Oxidants (OX) produced during shock/resuscitation (SR) contribute to organ failure by priming alveolar macrophages (AM) for increased responsiveness to subsequent inflammatory stimuli such as LPS. STAZN is a second-generation azulenyl nitrone – a potent antioxidant. Since our initial studies showed that OX/SR induces priming of AM by colocalization of LPS receptor - TLR4 within cell signaling domains – the lipid rafts (LR), we hypothesize that STAZN can protect lungs from injury by preventing macrophage LR recruitment of TLR4. Methods: Rats were bled to MAP of 40mmHg, 1h later resuscitated with blood and Ringer’s Lactate (RL) plus intraperitoneal STAZN (30 mg/kg × 2 doses) or vehicle (dimethyl sulfoxide). 1hr post resuscitation intratracheal LPS (3ug/10gm) or saline (SAL) were administered and 4hrs later bronchoalveolar lavage (BAL) fluid was used for differential cell counts. Alternatively, BAL AM were recovered 1hr after resuscitation, LRs were isolated by sucrose gradient ultracentrifugation and presence of TLR4 or GM-1 (LR marker) were detected by dot blotting. Results: Treatment with STAZN and not vehicle alone significantly attenuated LPS induced lung neutrophilia following RL resuscitation by 67% (n = 3, p<0.05). Shock and RL resuscitation induced TLR4 migration into the AM lipid raft fractions (marked by presence of GM-1), an effect inhibited by STAZN. Conclusions: Therapy with STAZN is effective at attenuating lung injury after shock resuscitation through preventing AM lipid raft recruitment of the LPS receptor. Azulenyl nitrones represent a novel and promising class of potent antioxidants for combating organ failure following 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) | 0.001 | 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".