Murine Model of Sinusitis Infection for Screening Antimicrobial and Immunomodulatory Therapies
Bibliographic record
Abstract
The very common condition of sinusitis is characterized by persistent inflammation of the nasal cavity, which contributes to chronic rhinosinusitis and morbidity of cystic fibrosis patients. Colonization by opportunistic pathogens such as Staphylococcus aureus and Pseudomonas aeruginosa triggers inflammation that is exacerbated by defects in the innate immune response. Pathophysiological mechanisms underlying initial colonization of the sinuses are not well established. Despite their extensive use, current murine models of acute bacterial rhinosinusitis have not improved the understanding of early disease stages due to analytical limitations. In this study, a model is described that is technically simple, allows non-invasive tracking of bacterial infection, and screening of host-responses to infection and therapies. The model was modified to investigate longer-term infection and disease progression by using a less virulent, epidemic P. aeruginosa cystic fibrosis clinical isolate LESB65. Tracking of luminescent bacteria was possible after intranasal infections, which were sustained for up to 120 h post-infection, without compromising the overall welfare of the host. Production of reactive oxidative species was associated with neutrophil localization to the site of infection in this model. Further, host-defense peptides administered by Respimat ® inhaler or intranasal instillation reduced bacterial burden and impacted disease progression as well as cytokine responses associated with rhinosinusitis. Thus, future studies using this model will improve our understanding of rhinosinusitis etiology and early stage pathogenesis, and can be used to screen for the efficacy of emerging therapies pre-clinically.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".