Bibliographic record
Abstract
Bacteria in natural, industrial and clinical settings predominantly live in biofilms, i.e., sessile structured microbial communities encased in self-produced extracellular matrix material. One of the most important characteristics of microbial biofilms is that the resident bacteria display a remarkable increased tolerance toward antimicrobial attack. Biofilms formed by opportunistic pathogenic bacteria are involved in devastating persistent medical device-associated infections, and chronic infections in individuals who are immune-compromised or otherwise impaired in the host defense. Because the use of conventional antimicrobial compounds in many cases cannot eradicate biofilms, there is an urgent need to develop alternative measures to combat biofilm infections. The present review is focussed on the important opportunistic pathogen and biofilm model organism Pseudomonas aeruginosa. Initially, biofilm infections where P. aeruginosa plays an important role are described. Subsequently, current insights into the molecular mechanisms involved in P. aeruginosa biofilm formation and the associated antimicrobial tolerance are reviewed. And finally, based on our knowledge about molecular biofilm biology, a number of therapeutic strategies for combat of P. aeruginosa biofilm infections are presented.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".