A multiple comorbidities mouse model to assess atherosclerosis progression following lung infection in <i>ApoE</i> deficient mice
Bibliographic record
Abstract
ABSTRACT Background Inflammation is a risk factor for atherosclerosis progression. Hospitalisation for pneumonia is associated with increased risk of cardiovascular disease. Herein, we describe a multiple comorbidities murine model to study the impact of bacterial pneumonia on atherosclerosis. Methods Firstly, a minimal infectious dose of Streptococcus pneumoniae (TIGR4 strain) to produce clinical pneumonia with a low mortality rate (20%) was established. C57Bl/6 ApoE-/- mice were fed a high-fat diet prior to administering intranasally 10 5 colony forming units of TIGR4 or phosphate buffered saline (PBS). At days 2, 7 and 28 post inoculation (PI), the lungs of mice were imaged by MRI and PET. Mice were euthanised and investigated for changes in systemic inflammation and changes in lung morphology using ELISA, Luminex assay and real-time PCR. Results TIGR4 inoculated mice presented with varying degreess of lung infiltrate, pleural effusion and consolidation on MRI at all timepoints up to 28 days PI. Moreover, PET scans identified significantly higher FDG uptake in the lungs of TIGR4 inoculated mice up to 28 days PI. Majority (90%) TIGR4-inoculated mice developed pneumococcal-specific IgG antibody response at 28 days PI. Consistent with these observations, TIGR4 inoculated mice displayed significantly increased inflammatory gene expression (IL-1β & IL6) in the lungs and significantly increased levels of circulating inflammatory protein (CCL3) at 7- and 28-days PI respectively. Conclusions Our mouse model presents a discovery tool to understand the link between acute infections, including pneumonia, and increased cardiovascular disease risk in humans with inflammation as the mechanistic catalyst. Graphical Abstract
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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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".