Molecular Characteristics of Colistin Resistance in Acinetobacter baumannii and the Activity of Antimicrobial Combination Therapy in a Tertiary Care Medical Center in Lebanon
Bibliographic record
Abstract
(1) Background: Infections with pan-drug-resistant (PDR) bacteria, such as A. baumannii, are becoming increasingly common, especially in healthcare facilities. In this study, we selected 15 colistin-resistant clinical A. baumannii isolates from a hospital in Beirut, Lebanon, to test combination therapies and determine their sequence types (STs) and the mechanism of colistin resistance using whole-genome sequencing (WGS). (2) Methods: Antimicrobial susceptibility testing via broth microdilution against 12 antimicrobials from different classes and growth rate assays were performed. A checkerboard assay was conducted on PDR isolates using six different antimicrobials, each in combination with colistin. Genomic DNA was extracted from all isolates and subjected to WGS. (3) Results: All isolates were resistant to all tested antimicrobials with the one exception that was susceptible to gentamicin. Combining colistin with either meropenem, ceftolozane–tazobactam, or teicoplanin showed synergistic activity. Sequencing data revealed that 67% of the isolates belonged to Pasteur ST2 and 33% to ST187. Furthermore, these isolates harbored a number of resistance genes, including blaOXA-23. Mutations in the pmrC gene were behind colistin resistance. (4) Conclusions: With the rise in antimicrobial resistance and the absence of novel antimicrobial production, alternative treatments must be found. The combination therapy results from this study suggest treatment options for PDR ST2 A. baumannii-infected patients.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".