Ethyl gallate, a scavenger of hydrogen peroxide that inhibits lysozyme-induced hydrogen peroxide signaling in vitro, reverses hypotension in canine septic shock
Bibliographic record
Abstract
Although hydrogen peroxide (H2O2) is a well-described reactive oxygen species that is known for its cytotoxic effects and associated tissue injury, H2O2 has recently been established as an important signaling molecule. We previously demonstrated that lysozyme (Lzm-S), a mediator of sepsis that is released from leukocytes, could produce vasodilation in a phenylephrine-constricted carotid artery preparation by H2O2 signaling. We found that Lzm-S could intrinsically generate H2O2 and that this generation activated H2O2-dependent pathways. In the present study, we used this carotid artery preparation as a bioassay to define those antioxidants that could inhibit Lzm-S's vasodilatory effect. We then determined whether this antioxidant could reverse the hypotension that developed in an Escherichia coli bacteremic model. Of the many antioxidants tested, we found that ethyl gallate (EG), a nonflavonoid phenolic compound, was favorable in inhibiting Lzm-S-induced vasodilation. In our E. coli model, we found that EG reversed the hypotension that developed in this model and attenuated end-organ dysfunction. By fluorometric H2O2 assay and electrochemical probe techniques, we showed that EG could scavenge H2O2 and that it could reduce H2O2 production in model systems. These results show that EG, an antioxidant that was found to scavenge H2O2 in vitro, was able to attenuate cardiovascular dysfunction in a canine in vivo preparation. Antioxidants such as EG may be useful in the treatment of hemodynamic deterioration in septic shock.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".