Pre-Exposure With Extracellular Vesicles From Aspergillus fumigatus Attenuates Inflammatory Response and Enhances Fungal Clearance in a Murine Model Pulmonary Aspergillosis
Bibliographic record
Abstract
Aspergillus fumigatus is a ubiquitous and saprophytic filamentous fungus and the main etiologic agent of aspergillosis. Infections caused by A. fumigatus culminate in a strong inflammatory response that can evolve into respiratory failure and may be lethal in immunocompromised individuals. In the last decades, it has been demonstrated that extracellular vesicles (EVs) elicit a notable biological response in immune cells. EVs carry a variety of biomolecules, therefore are considered potential antigen delivery vehicles. The role of EVs as a strategy for modulating an effective response against infections caused by A. fumigatus remains unexplored. Here we investigate the use of EVs derived from A. fumigatus as an immunization tool to induce a more robust immune response to A. fumigatus pulmonary infection. In order to investigate that, male C57BL/6 mice were immunized with two doses of EVs and infected with A. fumigatus. Pre-exposure of mice to EVs was able to induce the production of specific IgG serum for fungal antigens. Besides that, the immunization with EVs reduced the neutrophilic infiltrate into the alveoli, as well as the extravasation of total proteins and the production of proinflammatory mediators IL-1β, IL-6, and CXCL-1. In addition, immunization prevented extensive lung tissue damage and also improved phagocytosis and fungus clearance. Noteworthy, immunization with EVs, associated with subclinical doses of Amphotericin B (AmB) treatment, rescued 50% of mice infected with A. fumigatus from lethal fungal pneumonia. Therefore, the present study shows a new role for A. fumigatus EVs as host inflammatory response modulators, suggesting their use as immunizing agents.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 | 0.002 |
| 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".