Genome-scale approaches to strengthen Neisseria gonorrhoeae epidemiological and antimicrobial resistance surveillance
Bibliographic record
Abstract
Neisseria gonorrhoeae is the human pathogen responsible for the sexually transmitted disease gonorrhoea, whose burden remains a major public health concern. This bacterium has shown an extraordinary ability to develop antimicrobial resistance (AMR) to multiple classes of antimicrobials, with the advent of reaching a “superbug” status. With no available vaccine, managing gonorrhoea infections demands effective preventive measures, antibiotic treatments and epidemiological surveillance. National and international surveillance programmes are increasingly promoting the application of whole-genome sequencing (WGS) data to track N. gonorrhoeae circulation and the emergence and spread of AMR. The major goal of the PhD dissertation was to strengthen N. gonorrhoeae epidemiological and AMR surveillance using WGS. Particularly, we disclose the major AMR trends observed in Portugal throughout 16 years, by reporting data from the National Laboratory Network for Neisseria gonorrhoeae Collection (PTGonoNet), hosted at the Portuguese National Institute of Health (NIH). Using WGS data from across Europe, we report a comprehensive WGS-based genogroup assignment for N. gonorrhoeae. These genogroups represent main circulating lineages and were correlated with other typing techniques and linked to specific AMR signatures. Using a dynamic gene-by-gene approach, we performed the first genome-scale study of N. gonorrhoeae in Portugal, highlighting the genetic diversity of circulating strains, as well as potential transmission chains, which is essential to support epidemiological investigation. Finally, we evaluated a culture-independent strategy to obtain WGS data directly from clinical samples and its suitability for epidemiological surveillance and AMR detection. The findings presented in this dissertation constituted a turning point to consolidate the genomic epidemiology of gonococci in Portugal through the implementation of a WGS-based surveillance methodology in the Portuguese NIH. Ultimately, this work enhances N. gonorrhoeae surveillance by promoting the prospective monitoring of genogroup frequency and geographic spread, towards more oriented Public Health actions to control the spread of N. gonorrhoeae AMR.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".