Type 1 interferon gene transfer enhances host defense against pulmonary Streptococcus pneumoniae infection via activating innate leukocytes
Bibliographic record
Abstract
Pneumococcal infections are the leading cause of community-acquired pneumonia. Although the type 1 interferon-α (IFN-α) is a well-known antiviral cytokine, the role of IFN-α in antipneumococcal host defense and its therapeutic potential remain poorly understood. We have investigated these issues by using a murine transgene expression model. We found that in control animals, Streptococcus pneumoniae infection caused severe weight loss and excessive lung inflammation, associated with rapid bacterial outgrowth. In contrast, the animals that received a single dose of an adenoviral vector expressing IFN-α prior to pneumococcal infection demonstrated rapid and effective control of bacterial replication and lung inflammation and improved clinical outcome. Enhanced protection by IFN-α was due to increased activation of neutrophils and macrophages with increased release of reactive oxygen and nitrogen species and bacterial killing. Furthermore, we found that raised levels of IFN-α in the lung remained immune protective even when the gene transfer vector was given at a time postpneumococcal infection. Our study thus shows that the classically antiviral type 1 IFN can be exploited for enhancing immunity against pneumococcal infection via its activating effects on innate immune cells. Our findings hold implications for the therapeutic use of IFN-α gene transfer strategies to combat 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 | 0.000 | 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) | 0.001 | 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".