Accelerated Clearance of <i>Escherichia coli</i> in Experimental Peritonitis of Histamine-Deficient Mice
Bibliographic record
Abstract
We prepared a model of experimental peritonitis by introducing Escherichia coli into the peritoneal cavity of the histamine-deficient mice generated by a disruption of the gene for histidine decarboxylase (HDC), the unique histamine-synthesizing enzyme. When we inoculated E. coli into the peritoneal cavities of the HDC(-/-) (histamine-deficient) mice, they eliminated E. coli more efficiently than did the wild-type mice. Histamine was released efficiently from the peritoneal cells after E. coli inoculation in HDC(+/+) mice, although only trace amounts were detected in the peritoneal cells of HDC(-/-) mice. Two histamine agonists (6-[2-(4-imidazolyl)ethylamino]-N-(4-trifluoromethylphenyl)hepatanecarboxamide (H(1)) and dimaprit (H(2))) impaired the clearance of E. coli from the peritoneal cavity in HDC(-/-) mice, suggesting that the activation of both H(1) and H(2) receptors suppresses the clearance. In contrast, two kinds of H(1) and H(2) receptor antagonists, cimetidine and pyrilamine, promoted the clearance of E. coli in HDC(+/+) mice. Phagocytosis appeared to be enhanced in HDC(-/-) mice, since the number of neutrophils in the peritoneal cavity of HDC(-/-) mice was markedly increased. This enhanced recruitment of neutrophils was suppressed in the presence of the histamine agonists, 6-[2-(4-imidazolyl)ethylamino]-N-(4-trifluoromethylphenyl)hepatanecarboxamide and dimaprit. In this report histamine was first shown to be an important mediator in an E. coli infectious peritonitis model, causing a delay in the elimination of bacteria. This also raised the possibility of the use of antihistamine drugs for bacterial infection.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".