Interactions of <scp><i>Pseudomonas aeruginosa</i></scp> with <scp><i>Acanthamoeba polyphaga</i></scp> Observed by Imaging Flow Cytometry
Bibliographic record
Abstract
Pseudomonas aeruginosa is a Gram-negative bacterium that is abundant in the environment and water systems, with strains that cause serious infections, especially in patients with compromised immune systems. In times of stress or as part of its natural life cycle, P. aeruginosa can adopt a viable but not culturable (VBNC) state, which renders it undetectable by current conventional food and water testing methods and makes it highly resistant to antibiotic treatment. Specific conditions can resuscitate these coccoid VBNC P. aeruginosa cells, which returns them to their active, virulent rod-shaped form. Underreporting the VBNC cells of P. aeruginosa by standard culture-based methods in water distribution systems may therefore pose serious risks to public health. As such, being able to accurately detect and quantify the presence of VBNC P. aeruginosa, especially in a hospital setting, is of critical importance. Herein, we describe a method to analyze VBNC P. aeruginosa using imaging flow cytometry. With this technique, we can accurately distinguish between active and VBNC forms. We also show here that association of VBNC P. aeruginosa with Acanthamoeba polyphaga results in resuscitation of P. aeruginosa to an active form within 2 h. Our approach could provide an alternative, reliable detection method of VBNC P. aeruginosa when coupled with species-specific staining. Most importantly, our experiments demonstrate that the coculture with amoebae can lead to a resuscitation of P. aeruginosa of culturable morphology after only 2 h, indicating that VBNC P. aeruginosa could potentially resuscitate in piped water (healthcare) environments colonized with amoebae. © 2019 International Society for Advancement of Cytometry.
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 | 0.000 | 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".