Adjuvants Based on Hybrid Antibiotics Overcome Resistance in <i>Pseudomonas aeruginosa</i> and Enhance Fluoroquinolone Efficacy
Bibliographic record
Abstract
The use of adjuvants that rescue antibiotics against multidrug-resistant (MDR) pathogens is a promising combination strategy for overcoming bacterial resistance. While the combination of β-lactam antibiotics and β-lactamase inhibitors has been successful in restoring antibacterial efficacy in MDR bacteria, the use of adjuvants to restore fluoroquinolone efficacy in MDR Gram-negative pathogens has been challenging. We describe tobramycin-ciprofloxacin hybrid adjuvants that rescue the activity of fluoroquinolone antibiotics against MDR and extremely drug-resistant Pseudomonas aeruginosa isolates in vitro and enhance fluoroquinolone efficacy in vivo. Structure-activity studies reveal that the presence of both tobramycin and ciprofloxacin, which are separated by a C12 tether, is critical for the function of the adjuvant. Mechanistic studies indicate that the antibacterial modes of ciprofloxacin are retained while the role of tobramycin is limited to destabilization of the outer membrane in the hybrid.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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 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".