Mechanisms of systemic vasodilation by lysozyme-c in septic shock
Bibliographic record
Abstract
In septic shock (SS), cardiovascular collapse is caused by the release of inflammatory mediators. We previously found that lysozyme-c (Lzm-S), released from leukocytes, contributed to systemic vasodilation in a canine model of SS. We then delineated the pathway by which this occurs in a canine carotid artery organ bath preparation (CAP). We showed that Lzm-S could intrinsically generate hydrogen peroxide (H(2)O(2)) and that H(2)O(2) subsequently reacted with endogenous catalase to form compound I, an oxidized form of catalase. In turn, compound I led to an increase in cyclic guanosine 3',5'-monophosphate to produce vasodilation. However, it was not clear from previous studies whether it is necessary for Lzm-S to bind to the vasculature to cause vasodilation or, alternatively, whether the generation of H(2)O(2) by Lzm-S in the surrounding medium is all that is required. We examined this question in the present study in which we used multiple preparations. In a partitioned CAP, we found that when we added Lzm-S to a partitioned space in which a semipermeable membrane prevented diffusion of Lzm-S to the carotid artery tissue, vasodilation still occurred because of diffusion of H(2)O(2). On the other hand, we found that Lzm-S could accumulate within the vascular smooth muscle layer (VSML) after 7 h of SS in a canine model. We also determined that when Lzm-S was located in close proximity to vascular smooth muscle cells, it could generate H(2)O(2) to produce lengthening in a human cell culture preparation. We conclude that there are two mechanisms by which Lzm-S can cause vasodilation in SS. In one instance, H(2)O(2) generated by Lzm-S in plasma diffuses to the VSML to cause vasodilation. In a second mechanism, Lzm-S directly binds to the VSML, where it generates H(2)O(2) to produce vasodilation.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".