Oxidative Stress Response in Pseudomonas aeruginosa
Bibliographic record
Abstract
Pseudomonas aeruginosa is a Gram-negative environmental and human opportunistic pathogen highly adapted to many different environmental conditions. It can cause a wide range of serious infections, including wounds, lungs, the urinary tract, and systemic infections. The high versatility and pathogenicity of this bacterium is attributed to its genomic complexity, the expression of several virulence factors, and its intrinsic resistance to various antimicrobials. However, to thrive and establish infection, P. aeruginosa must overcome several barriers. One of these barriers is the presence of oxidizing agents (e.g., hydrogen peroxide, superoxide, and hypochlorous acid) produced by the host immune system or that are commonly used as disinfectants in a variety of different environments including hospitals. These agents damage several cellular molecules and can cause cell death. Therefore, bacteria adapt to these harsh conditions by altering gene expression and eliciting several stress responses to survive under oxidative stress. Here, we used PubMed to evaluate the current knowledge on the oxidative stress responses adopted by P. aeruginosa. We will describe the genes that are often differently expressed under oxidative stress conditions, the pathways and proteins employed to sense and respond to oxidative stress, and how these changes in gene expression influence pathogenicity and the virulence of P. aeruginosa. Understanding these responses and changes in gene expression is critical to controlling bacterial pathogenicity and developing new therapeutic agents.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".