Antibiotic Resistance Detection in <i>Pseudomonas aeruginosa</i>: Recent Strategies, Advances, and Challenges
Bibliographic record
Abstract
Abstract Antibiotic resistance presents a worldwide public health emergency, impeding the effective management of infectious diseases. Pseudomonas aeruginosa plays a substantial role as a bacterial pathogen, particularly in infections among hospitalized individuals, and those with weakened immune systems. Timely and accurate detection of antimicrobial resistance in P. aeruginosa is crucial for initiating tailored antibiotic therapy promptly, thus improving patient outcomes. Nevertheless, this endeavor encounters challenges due to the intricate and varied nature of antibiotic‐resistant strains. Extensive efforts have been invested in developing sensors and instrumentations for assessing antibiotic resistance or susceptibility (AST), aiming to enable personalized patient treatment with appropriate medications. This paper focuses on recent advancements in these methodologies, utilized for evaluating the degree of antibiotic resistance or susceptibility in pathogenic bacteria. Flow cytometry, dual molecular recognition and mass spectrometry are presented as newer phenotypic AST techniques. Genomic methods and the pyocyanin detection are listed as methodologies for the detection of resistance indicators.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".