<i>Pseudomonas aeruginosa</i> Cystic Fibrosis Isolates from Individual Patients Demonstrate a Range of Levels of Lethality in Two <i>Drosophila melanogaster</i> Infection Models
Bibliographic record
Abstract
Recently, two Drosophila melanogaster models of infection, fly feeding and fly nicking, have been developed that allow a determination of pathogenic potential of Pseudomonas aeruginosa isolates. In this study, control strains, isolates from burn wounds, and isolates from the sputa of cystic fibrosis (CF) patients were used to compare the two infection models to determine whether any of the isolates might be better adapted to either of the models. In addition, our goal was to determine the variability of isolates from individual CF patients. Three of four control strains (PAO1, PAK, and PA14) caused significant mortality in the flies in both models of infection. The remaining control strain, PA103, was lethal to flies in the nicking model but lacked significant lethality in the feeding model. The burn wound isolates had a high level of lethality in both models. Interestingly, the CF isolates had the largest diversity of lethality in both models of infection. The range of pathogenic potentials of the CF isolates occurred across a cohort of patients, both at the patient level and down to the level of individual sputum samples. The majority of all isolates had similar levels of lethality in both fly infection models. However, two CF isolates were significantly more lethal in the nicking model, and three CF isolates were significantly more lethal in the feeding model. In conclusion, the two Drosophila infection models were useful for the analysis of the diversity of pathogenic potentials of P. aeruginosa isolates.
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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) | 0.000 | 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".