Development of an Aerosol Model of Cryptococcus Reveals Humidity as an Important Factor Affecting the Viability of Cryptococcus during Aerosolization
Bibliographic record
Abstract
Cryptococcus is an emerging global health threat that is annually responsible for over 1,000,000 infections and one third of all AIDS patient deaths. There is an ongoing outbreak of cryptococcosis in the western United States and Canada. Cryptococcosis is a disease resulting from the inhalation of the infectious propagules from the environment. The current and most frequently used animal infection models initiate infection via liquid suspension through intranasal instillation or intravenous injection. These models do not replicate the typically dry nature of aerosol exposure and may hinder our ability to decipher the initial events that lead to clearance or the establishment of infection. We have established a standardized aerosol model of murine infection for the human fungal pathogen Cryptococcus. Aerosolized cells were generated utilizing a Collison nebulizer in a whole-body Madison Chamber at different humidity conditions. The aerosols inside the chamber were sampled using a BioSampler to determine viable aerosol concentration and spray factor (ratio of viable aerosol concentration to total inoculum concentration). We have effectively delivered yeast and yeast-spore mixtures to the lungs of mice and observed the establishment of disease. We observed that growth conditions prior to exposure and humidity within the Madison Chamber during exposure can alter Cryptococcus survival and dose retained in mice.
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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".