Lysozyme, a Mediator of Sepsis That Deposits in the Systemic Vasculature and Kidney as a Possible Mechanism of Acute Organ Dysfunction
Bibliographic record
Abstract
In septic shock (SS), dysfunction of many organ systems develops during the course of the illness, although the mechanisms are not clear. In earlier studies, we reported that lysozyme-c (Lzm-S), a protein that is released from leukocytes and macrophages, was a mediator of the myocardial depression and vasodilation that develop in a canine model of Pseudomonas aeruginosa SS. Whereas both of these effects of Lzm-S are dependent on its ability to intrinsically generate hydrogen peroxide, we subsequently showed that Lzm-S can also deposit within the vascular smooth muscle layer of the systemic arteries in this model. In the present study, we extend our previous findings. We used a canine carotid artery organ bath preparation to study the time course and dose dependence of Lzm-S deposition within the vascular smooth muscle layer. We used a human aortic vascular smooth muscle cell preparation to determine whether Lzm-S can persistently inhibit contraction in this preparation. We also used a canine P. aeruginosa model to determine whether Lzm-S deposition might occur in other organs such as the kidney, liver, and small intestine. The results showed that, in the carotid artery organ bath preparation, Lzm-S deposition occurred within minutes of instillation and there was a dose-response effect. In the human aortic vascular smooth muscle cell preparation, Lzm-S inhibited contraction during a 4-day period. In the in vivo model, Lzm-S accumulated in the kidney and the superior mesenteric artery. In a canine renal epithelial preparation, we further showed that Lzm-S can be taken up by the renal tubules to activate inflammatory pathways. We conclude that Lzm-S can deposit in the systemic vasculature and kidneys in SS, where this deposition could lead to acute organ dysfunction.
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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".