Using model systems to unravel host– <i>Pseudomonas aeruginosa</i> interactions
Bibliographic record
Abstract
Using model systems in infection biology has led to the discoveries of many pathogen-encoded virulence factors and critical host immune factors to fight pathogenic infections. Studies of the remarkable Pseudomonas aeruginosa bacterium that infects and causes disease in hosts as divergent as humans and plants afford unique opportunities to shed new light on virulence strategies and host defence mechanisms. One of the rationales for using model systems as a discovery tool to characterise bacterial factors driving human infection outcomes is that many P. aeruginosa virulence factors are required for pathogenesis in diverse different hosts. On the other side, many host signalling components, such as the evolutionarily conserved mitogen-activated protein kinases, are involved in immune signalling in a diverse range of hosts. Some model organisms that have less complex immune systems also allow dissection of the direct impacts of innate immunity on host defence without the interference of adaptive immunity. In this review, we start with discussing the occurrence of P. aeruginosa in the environment and the ability of this bacterium to cause disease in various hosts as a natural opportunistic pathogen. We then summarise the use of some model systems to study host defence and P. aeruginosa virulence.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".