Emergence of Quinolone-Resistance and Cephalosporin MIC Creep in Neisseria\n2 gonorrhoeae in a Cohort of Young Men in Kisumu, Kenya: 2002 – 2009
Bibliographic record
Abstract
Introduction: We evaluated antimicrobial resistance in Neisseria gonorrhoeae (NG) isolated from men enrolled in a randomized trial of male circumcision to prevent HIV.\nMethods: Urethral specimens from men with discharge were cultured for NG. Minimum\ninhibitory concentrations (MICs) were determined by agar dilution. Clinical Laboratory Standards Institute criteria defined resistance: MIC≥2.0 μg/ml for penicillin, tetracycline, and\nazithromycin; ciprofloxacin MIC≥1.0 μg/ml; spectinomycin MIC≥128.0 μg/ml. Susceptibility to ceftriaxone and cefixime was MIC<0.25 μg/ml. Additionally, PCR amplification identified mutations in parC and gyrA genes in selected isolates.\nResults: From 2002–2009, 168 NG isolates were obtained from 142 men. Plasmid mediated\npenicillin resistance was found in 65%, plasmid mediated tetracycline resistance in 97%, and 11% were ciprofloxacin resistant (QRNG). QRNG appeared November 2007, increasing from 9.5% in 2007 to 50% in 2009. Resistance was not detected for spectinomycin, cefixime, ceftriaxone, and azithromycin, but MICs of cefixime (p=0.018), ceftriaxone (p<0.001), and azithromycin (p=0.097) increased over time. In a random sample of 51 men gentamicin MIC\nwas: 4 μg/ml (n=1), 8 μg/ml (n=49), 16 μg/ml (n=1).\nDiscussion: QRNG increased rapidly and alternative regimens are required for NG treatment in this area. Amid emerging multi-drug resistant NG, antimicrobial resistance surveillance is essential for effective drug choice. High levels of plasmid mediated resistance and increasing MIC for cephalosporins suggest that selective pressure from antibiotic use is a strong driver of resistance emergence.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".