Live imaging and quantitative analysis of <i>Aspergillus fumigatus</i> growth and morphology during inter-microbial interaction with <i>Pseudomonas aeruginosa</i>
Bibliographic record
Abstract
Pseudomonas aeruginosa (PA) and Aspergillus fumigatus (AF) chronically colonize the airways of patients with cystic fibrosis or chronic immunosuppression and mutually affect each other’s pathogenesis. Here, we evaluated IncuCyte time-lapse imaging and NeuroTrackTM (NT) analysis (Wurster et al., 2019, mBio) as a toolbox to study mycelial expansion and morphogenesis of AF during interaction with PA. Co-incubation of AF with supernatant filtrates of wild-type (WT) PA strains strongly inhibited hyphal growth and branching. Consonant with prior metabolic studies, pyoverdine-deficient PA mutants had significantly attenuated inhibitory capacity. Accordingly, purified PA products pyoverdine and pyocyanin suppressed mycelial expansion of AF in a concentration-dependent way. Using fluorescence-guided tracking of GFP-AF293 mycelia during co-culture with live WT PA cells, we found significant inoculum-dependent mycelial growth inhibition and robust precision of the NT algorithm. Collectively, our experiments position IncuCyte NT as an efficient platform for longitudinal analysis of fungal growth and morphogenesis during bacterial co-infection.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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".