Joint HIV and hepatitis C virus phylogenetic analyses signal network overlap among women engaged in sex work and men who purchase sex
Bibliographic record
Abstract
Background Transmission of HIV and hepatitis C virus (HCV) are heavily influenced by complex interactions within sexual or injecting networks where risk behaviors occur. In Ukraine, women engaged in sex work (WSW) and men who purchase sex (MWPS) are disproportionately affected by both viruses. The aim of our study was to the investigate the influence of underlying networks on transmission of HIV and HCV. Methods A cross-sectional integrated bio-behavioural survey was implemented among 560 WSW and 370 MWPS representative of sex work hotspots in Dnipro, Ukraine (December 2017 to March 2018). A portion of the HIV reverse transcriptase gene ( n = 13; 62% WSW, 38% MWPS) and HCV NS5B gene ( n = 46; 70% WSW, 30% MWPS) were sequenced from dried blood spot specimens. Tip-to-tip distances on phylogenetic trees were used to infer phylogenetic clusters for identifying potential transmission clusters. Results Phylogenetic analyses identified two HIV clusters containing four sequences (50% WSW; 50% MWPS) and 11 HCV clusters containing 31 sequences – the majority comprising infections in WSW (83.9%). Nearly half (45.4%) of HCV clusters contained at least one WSW with a history of injecting drugs. Conclusions Joint analyses of HIV and HCV signal overlap in sex work and injecting networks in Ukraine, suggesting implications for the comprehensive coverage of prevention programs for WSW including harm reduction services. Conducting phylogenetic analyses with HCV may provide a more complete appraisal of underlying transmission networks than HIV alone, particularly in the context of high HIV treatment coverage yielding viral suppression.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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 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".