Evaluation of Susceptibilities to Carbapenems and Faropenem Against Cephalosporin-Resistant <i>Neisseria gonorrhoeae</i> Clinical Isolates with <i>penA</i> Mosaic Alleles
Bibliographic record
Abstract
Neisseria gonorrhoeae is a principal pathogen for sexually transmitted infections, especially for male urethritis. Currently, the prevalence of multidrug resistance is increasing. Carbapenems are broad-spectrum antimicrobials that are widely used in the clinical setting, especially for multidrug-resistant Gram-negative bacteria. However, susceptibility to carbapenems has not been well evaluated for cephalosporin-resistant N. gonorrhoeae isolates. In this study, we determined the susceptibility to a series of carbapenems (meropenem, imipenem, doripenem, and biapenem) and faropenem against cephalosporin-resistant (resistant to cefixime, but susceptible to ceftriaxone) and cephalosporin-susceptible N. gonorrhoeae clinical isolates. The gene mutations associated with β-lactam resistance were evaluated. All cephalosporin-resistant N. gonorrhoeae isolates possessed mosaic mutation alleles in penA (NG-STAR penA-10.001, 27.001, or 108.001). They exhibited a low minimum inhibitory concentration (MIC) (≤0.125 mg/L) for meropenem and markedly high MICs (0.5-2 mg/L) for other carbapenems and faropenem. The strongest association was observed between the mosaic alleles in penA and decreased susceptibility to carbapenems and faropenem compared with mutations in mtrR, porB, and ponA. These results suggest that meropenem may serve as an alternative therapeutic agent for cephalosporin-resistant N. gonorrhoeae with a mosaic allele in penA, whereas other carbapenems and faropenem may be ineffective.
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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) | 0.001 | 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".