Development and optimization of methods for molecular detection of sexually transmitted blood-borne infections in wastewater
Bibliographic record
Abstract
Sexually transmitted blood-borne infections (STBBIs) are a group of infectious diseases that spread through sexual contact or blood-to-blood contact. STBBIs are of international concern due to their severe impact on quality of life and morbidity. Surveillance strategies are imperfect, in part due to the frequency of asymptomatic infection. Wastewater-based testing (WBT), the analysis of sewage influent for a pathogen, is one possible solution to supplement current clinical surveillance approaches. WBT has proven to be an effective strategy for monitoring the spread of other pathogens, including SARS-CoV-2 and its variants by allowing for the community-level detection of the infection markers and can act as an early indicator of transmission that precedes clinical indicators. In this work, detectability in wastewater was investigated for six pathogens: Neisseria gonorrhoeae, Chlamydia trachomatis, Hepatitis B Virus (HBV), Hepatitis C Virus (HCV), Human papillomavirus (HPV) types 16 and 18, and Human Immunodeficiency virus (HIV). Neither HIV nor HCV were detected in this study in any Canadian wastewater samples. N. gonorrhoeae, C. trachomatis, HBV, and HPV-16/18 were detected in the solids fraction of wastewater. The methodology was optimized by evaluating multiple extraction kits and input volumes, resulting in a final protocol utilizing 100 mL of wastewater influent extracted with the MagAttract PowerMicrobbiome DNA/RNA Kit. This protocol was employed to survey nine to eleven Canadian correctional facilities and one urban wastewater treatment plant over two six-week testing periods for N. gonorrhoeae, C. trachomatis, HPV-16/18, and HBV. Methods were also developed to evaluate circulating HPV genotypes using Illumina high-throughput sequencing technology. Using direct-to-sequencing primers conjugated to the GP5+/GP6+ primers, two urban wastewater treatment plants, two town sites, and three correctional facilities were surveyed. Ultimately, we detected 26 distinct HPV types, all belonging to the alpha-papillomavirus genus. This included high- and low-risk types, types of unknown risk, and cutaneous and mucosal types. Ultimately, the ability to detect STBBIs in wastewater can strengthen existing public health surveillance systems and allows for targeted allocation of outreach and resources to affected communities.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".