Lysozyme, a mediator of sepsis that intrinsically generates hydrogen peroxide to cause cardiovascular dysfunction
Bibliographic record
Abstract
In septic shock, cardiovascular collapse is caused by the release of inflammatory mediators. We previously found that lysozyme (Lzm-S), released from leukocytes, contributed to the myocardial depression and arterial vasodilation that develop in canine models of septic shock. To cause vasodilation, Lzm-S generates hydrogen peroxide (H(2)O(2)) that activates the smooth muscle soluble guanylate cyclase (sGC) pathway, although the mechanism of H(2)O(2) generation is not known. To cause myocardial depression, Lzm-S binds to the endocardial endothelium, resulting in the formation of nitric oxide (NO) and subsequent activation of myocardial sGC, although the initial signaling event is not clear. In this study, we examined whether the myocardial depression produced by Lzm-S was also caused by the generation of H(2)O(2) and whether Lzm-S could intrinsically generate H(2)O(2) as has been described for other protein types. In a canine ventricular trabecular preparation, we found that the peroxidizing agent Aspergillus niger catalase, that would breakdown H(2)O(2), prevented Lzm-S- induced decrease in contraction. We also found that compound I, a species of catalase formed during H(2)O(2) metabolism, could contribute to the NO generation caused by Lzm-S. In tissue-free experiments, we used a fluorometric assay (Ultra Amplex red H(2)O(2) assay) and electrochemical sensor techniques, respectively, to measure H(2)O(2) generation. We found that Lzm-S could generate H(2)O(2) and, furthermore, that this generation could be attenuated by the singlet oxygen quencher sodium azide. This study shows that Lzm-S, a mediator of sepsis, is able to intrinsically generate H(2)O(2). Moreover, this generation may activate H(2)O(2)-dependent pathways leading to cardiovascular collapse 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) | 0.000 | 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 | 0.001 | 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".