Molecular epidemiology of Neisseria gonorrhoeae strains circulating in Indonesia using multi-locus variable number tandem repeat analysis (MLVA) and Neisseria gonorrhoeae multi-antigen sequence typing (NG-MAST) techniques
Bibliographic record
Abstract
BACKGROUND: Control of gonorrhea in resource-limited countries, such as Indonesia, is mostly unsuccessful. Examining Neisseria gonorrhoeae (Ng) transmission networks using strain typing might help prioritizing public health interventions. METHODS: In 2014, urogenital Ng strains were isolated from clients of sexually transmitted infection clinics in three Indonesian cities. Strains were typed using Multiple-Locus Variable Number Tandem Repeat (VNTR) Analysis (MLVA) and Ng Multi-Antigen Sequence Typing (NG-MAST) at the Public Health Service, Amsterdam, the Netherlands, and compared to Dutch strains collected from 2012 to 2015. Minimum spanning trees (MSTs) were constructed using MLVA profiles incorporating demographics and NG-MAST genogroups. A cluster was defined as ≥5 strains differing in ≤1 VNTR locus. The concordance between MLVA and NG-MAST was examined with the adjusted Wallace coefficients (AW). RESULTS: We collected a total of 78 Indonesian strains from Yogyakarta (n = 44), Jakarta (n = 25), and Denpasar (n = 9). Seven MLVA clusters and 16 non-clustered strains were identified. No cluster was specific for any geographic area, risk group or age group. Combined with 119 contemporary Dutch strains, 8 MLVA clusters were identified, being four clusters of Indonesian strains, two of Dutch strains, and two of both Indonesian and Dutch strains. Indonesian strains (79.5%) were more often clustered compared to Dutch strains (24.3%). The most prevalent NG-MAST genogroups among Indonesian strains was G1407 (51.3%) and among Dutch strains was G2992 (19.3%). In Indonesian strains, the AW [95% confidence interval] for MLVA to NG-MAST was 0.07 [0.00-0.27] and for NG-MAST to MLVA was 0.03 [0.00-0.12]. CONCLUSION: Indonesian Ng strains are more often clustered than Dutch strains, but show no relation with geographical area, risk group, or age group, suggesting a more clonal Ng epidemic in Indonesia. Some similar Ng strains circulate in both Indonesia and the Netherlands.
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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 | 0.000 | 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) | 0.000 | 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".