Ceftriaxone pharmacokinetics in interleukin-10-treated murine pneumococcal pneumonia
Bibliographic record
Abstract
OBJECTIVES: Anti-inflammatory therapy with interleukin-10 (IL-10) was previously reported to reduce pulmonary inflammation and to prevent septicaemia in murine pneumococcal pneumonia treated with ceftriaxone. In the present report, we investigated the influence of pulmonary infection and IL-10 administration on the pharmacokinetics of ceftriaxone. METHODS: CD1 mice were infected with 10(7) cfu of Streptococcus pneumoniae. Treatments (intraperitoneal) with IL-10 (1 microg per mouse), ceftriaxone (20 mg/kg) or the combination of IL-10 + ceftriaxone were initiated 18 h after infection. Groups of mice were sacrificed at several time points from 5 min to 24 h after initiation of therapy. Ceftriaxone was quantified in blood and lungs using a microbiological assay. Additional groups of mice received a second dose of IL-10 at 36 h post-infection. Survival rates were recorded over 14 days. RESULTS: The clearance of ceftriaxone was significantly reduced in infected mice compared with that in non-infected animals (P < 0.01), whereas AUC, mean residence time, t(1/2) and AUC(lung)/AUC(serum) were significantly enhanced (P < 0.01, 0.01, 0.05, 0.05). Co-administration of IL-10 with ceftriaxone in infected animals further retained ceftriaxone in the bloodstream and reduced its volume of distribution at steady state and the ratio of AUC(lung)/AUC(serum). IL-10 alone did not modify significantly the pharmacokinetics of ceftriaxone in blood and lungs of non-infected animals. CONCLUSIONS: The results suggest that pulmonary infection, and therapy with IL-10, both affect the pharmacokinetics of ceftriaxone. Indeed, administration of IL-10 + ceftriaxone improved the survival rate of mice (P < 0.001 compared with therapy with ceftriaxone alone). IL-10 should be considered as an adjunctive therapy to antibiotics against severe infections.
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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 | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".