Emergence of Quinolone Resistance and Cephalosporin MIC Creep in <i>Neisseria gonorrhoeae</i> Isolates from a Cohort of Young Men in Kisumu, Kenya, 2002 to 2009
Bibliographic record
Abstract
We evaluated antimicrobial resistance in Neisseria gonorrhoeae isolated from men enrolled in a randomized trial of male circumcision to prevent HIV. Urethral specimens from men with discharge were cultured for N. gonorrhoeae. MICs were determined by agar dilution. Clinical and Laboratory Standards Institute (CLSI) criteria defined resistance: penicillin, tetracycline, and azithromycin MICs of ≥2.0 μg/ml; a ciprofloxacin MIC of ≥1.0 μg/ml; and a spectinomycin MIC of ≥128.0 μg/ml. Susceptibility to ceftriaxone and cefixime was shown by an MIC of ≤0.25 μg/ml. Additionally, PCR amplification identified mutations in parC and gyrA genes in selected isolates. From 2002 to 2009, 168 N. gonorrhoeae isolates were obtained from 142 men. Plasmid-mediated penicillin resistance was found in 65%, plasmid-mediated tetracycline resistance in 97%, and 11% were ciprofloxacin resistant (quinolone-resistant N. gonorrhoeae [QRNG]). QRNG appeared in November 2007, increasing from 9.5% in 2007 to 50% in 2009. Resistance was not detected for spectinomycin, cefixime, ceftriaxone, or 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 MICs were as follows: 4 μg/ml (n = 1), 8 μg/ml (n = 49), and 16 μg/ml (n = 1). QRNG increased rapidly and alternative regimens are required for N. gonorrhoeae treatment in this area. Amid emerging multidrug-resistant N. gonorrhoeae, antimicrobial resistance surveillance is essential for effective drug choice. High levels of plasmid-mediated resistance and increasing MICs for cephalosporins suggest that selective pressure from antibiotic use is a strong driver of resistance emergence.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".