Natural history of Acinetobacter baumannii infection in mice
Bibliographic record
Abstract
In 2017, the WHO identified Acinetobacter baumannii as the top priority for the development of new antibiotics. Despite the need for new antibiotics, there remains a lack of well validated preclinical tools for A. baumannii. Here, we characterize and validate a mouse model for A. baumannii translational research. Antibiotic sensitivity for meropenem, amikacin, and polymyxin b was determined by the broth microdilution MIC assay. LD100 inoculums, in both blood and lung infection models, were determined in male and female C3HeB/FeJ mice that were challenged with various A. baumannii clinical isolates. Blood (blood infection model) or blood and lung tissue (lung infection model) were collected from infected mice at 2 and 18 hours and the bacterial burden was determined by quantitative culture. Blood chemistry was analyzed using the iStat system. Cytokines (IL-1ß, TNF, IL-6, and IL-10) were measured in the blood and lung homogenate by ELISA assay. Lung sections (H&E stains) were scored by a pathologist. In the blood infection model, the cytokines and physiological data indicate that mice become moribund due to sepsis (low blood pH, falling bicarbonate, and a rising base deficit), whereas mice become moribund due to respiratory failure (low blood pH, rising bicarbonate, and a falling base deficit) in the oral aspiration pneumonia model. We also characterized the timing of changes in various clinical and biomarker endpoints, which can serve as a basis for future interventional studies. Susceptibility was generally similar across gender and infection route. However, we did observe that female mice were approximately 2-fold more sensitive to LAC-4 ColR in the blood infection model. We also observed that female mice were more than 10-fold more resistant to VA-AB41 in the oral aspiration pneumonia model. These results establish parameters to follow in order to assess efficacy of novel preventative and therapeutic approaches for these 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".