Lung Immunopathology Following Influenza And Pneumococcus Infection: Mechanisms Of Disease And Therapeutic Approaches
Bibliographic record
Abstract
Influenza is a highly contagious respiratory disease. Yearly epidemics and pandemics account for high morbidity and mortality worldwide. Lung immunopathology is a major factor causing death following influenza. In addition, secondary bacterial superinfections that occur after influenza further complicate the lung immunopathology and contribute to higher morbidity and mortality. The research presented in this thesis addressed important, understudied questions in the complicated field of tissue immunopathogenesis and host defense to influenza and pneumococcal infections. Firstly, in a model of acute respiratory influenza infection, we found that the classically proinflammatory cytokine TNF plays a dual and biphasic role at different times post-infection. While it does have pro-immune roles in the beginning stages, TNF acts as a negative type 1 immune regulator at later points of infection. TNF controls the level of immune activation and has a key role in preventing lung immunopathology and aberrant tissue remodeling. Secondly, to further investigate mechanisms of lung pathology, we elucidated the role of bacterial replication and over activated host immune responses during bacterial superinfection following influenza. In our model of pulmonary Streptococcus pneumoniae infection after influenza, we found that dual infected animals experience rapid weight loss and succumb to infection. Bacterial outgrowth, dysregulated cytokine and chemokine expression, and severe lung neutrophilia and immunopathology are linked to the poor clinical outcome. Combined treatment with both an antibiotic azithromycin and corticosteroid dexamethasone best improves clinical outcome, bacterial clearance, cellular and cytokine responses, and immunopathology. Thirdly, in our continuing interest for improved therapies during pulmonary infections, we tested the transgenic expression of type I IFN as a treatment during S. pneumoniae infection. We found that IFN-a controls bacterial outgrowth and improves clinical outcome. Together, our findings provide novel insights into the mechanisms of lung immunopathology and treatment protocols for pulmonary influenza and pneumococcal 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 | 0.000 |
| 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 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".