N,N′-DIACETYLCHITOBIOSE, AN INHIBITOR OF LYSOZYME, REVERSES MYOCARDIAL DEPRESSION AND LESSENS NOREPINEPHRINE REQUIREMENTS IN ESCHERICHIA COLI SEPSIS IN DOGS
Bibliographic record
Abstract
Cardiovascular dysfunction in septic shock (SS) is ascribed to the release of inflammatory mediators. Norepinephrine (NE) is often administered to treat low MAP in SS. We recently found that lysozyme c (Lzm-S) released from leukocytes was a mediator of myocardial depression in an Escherichia coil model of SS in dogs. This effect can be blocked in an in vitro preparation by chitobiose, a competitive inhibitor of Lzm-S. In the present study, we examined whether chitobiose treatment can reverse myocardial depression and obviate NE requirements in two respective canine E. coli preparations. In a 6-h study, we administered chitobiose after 3.5 h of E. coli bacteremia and compared stroke work (SW) and MAP at 6 h with a sepsis control group. In a 12-h study, we determined whether chitobiose treatment can reduce the need for NE requirements during 12 h of bacteremia. In the latter study, either chitobiose or NE was given when MAP decreased approximately 20% from the presepsis value in respective groups. In anesthetized, mechanically ventilated dogs, we monitored hemodynamic parameters during continuous E. coli infusion. In the 6-h study, chitobiose improved SW and MAP at the 6-h period as compared with the nontreated sepsis group. In the 12-h study, SW and MAP increased after chitobiose without the necessity of NE administration. These results suggest that inhibitors of Lzm-S such as chitobiose may improve myocardial depression and reduce the need for NE requirements in SS.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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 | 0.001 |
| 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".