Mecillinam activity against multidrug-resistant <i>Shigella sonnei</i> and <i>Shigella flexneri</i>
Bibliographic record
Abstract
ABSTRACT We performed phenotypic susceptibility testing and whole-genome sequencing to investigate in vitro mecillinam activity against multidrug-resistant (MDR) Shigella . Ninety-five isolates (53 [56%] Shigella sonnei and 42 [44%] Shigella flexneri ) were included. The MIC 50 and MIC 90 were 1 and 2 µg/mL, respectively, by agar dilution, and all zone diameters were ≥15 mm by disc diffusion testing. No known clinically relevant mecillinam resistance mutations (including cys B) were detected. The favorable MIC profile and absence of resistance markers make pivmecillinam an agent worth considering for the treatment of infections caused by MDR Shigella . Clinically feasible susceptibility testing methods and standardized interpretations for mecillinam and Shigella spp. are needed to guide therapy. IMPORTANCE Rapidly emerging resistance in Shigella species leaves few antibiotic treatment options. The World Health Organization recommends pivmecillinam, a prodrug of mecillinam, for the treatment of Shigella infections. However, little is known about the susceptibility of Shigella spp. to mecillinam in North America. We performed mecillinam susceptibility testing on our collection of resistant Shigella isolates and investigated genetic mechanisms of resistance using whole-genome sequencing. We observed a favorable mecillinam susceptibility profile and a lack of known genetic mechanisms of resistance. However, in the absence of standardized laboratory guidelines for mecillinam susceptibility testing, interpreting susceptibility test results is challenging. We propose that further studies are needed to correlate susceptibility testing data with clinical outcomes, with the aim of establishing standardized clinical breakpoints for Shigella spp. and mecillinam.
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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 | 0.000 | 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".