Express yourself: Quantitative real-time PCR assays for rapid chromosomal antimicrobial resistance detection in <i>Pseudomonas aeruginosa</i>
Bibliographic record
Abstract
Abstract The rise of antimicrobial-resistant (AMR) bacteria is a global health emergency. One critical facet in tackling this epidemic is more rapid AMR diagnosis in serious multi-drug resistant pathogens like Pseudomonas aeruginosa . Here, we designed and then validated two multiplex quantitative real-time PCR (qPCR) assays to simultaneously detect differential expression of the resistance-nodulation-division efflux pumps MexAB-OprM, MexCD-OprJ, MexEF-OprN, MexXY-OprM, the AmpC β-lactamase, and the porin OprD, which are commonly associated with chromosomally-encoded AMR. Next, qPCRs were tested on 15 sputa from 11 participants with P. aeruginosa respiratory infections to determine AMR profiles in vivo . We confirm multiplex qPCR testing feasibility directly on sputa, representing a key advancement in in vivo AMR diagnosis. Notably, comparison of sputa with their derived isolates grown in Luria-Bertani broth (±2.5% NaCl) or a 5-antibiotic cocktail showed marked expression differences, illustrating the difficulty in replicating in vivo expression profiles in vitro . Cystic fibrosis sputa showed significantly reduced mexE and mexY expression when compared with chronic obstructive pulmonary disease sputa, despite harbouring fluoroquinolone- and aminoglycoside-resistant strains, indicating that these loci are not contributing to AMR in vivo. oprD was also significantly downregulated in cystic fibrosis sputa, even in the absence of contemporaneous carbapenem use, suggesting a common adaptive trait in chronic infections that may affect carbapenem efficacy. Sputum ampC expression was highest in participants receiving carbapenems (6.7-15x), some of whom were simultaneously receiving cephalosporins, the latter of which would be rendered ineffective by the upregulated ampC . Our qPCR assays provide valuable insights into the P. aeruginosa resistome, and their use on clinical specimens will permit timely treatment alterations that will improve patient outcomes and antimicrobial stewardship measures.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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".