A2 HIV transmission networks among injection drug users in Pakistan
Bibliographic record
Abstract
Pakistan is currently facing a concentrated HIV epidemic among injection drug users (IDUs). Well-defined transmission networks, based on molecular epidemiology, have the potential to assist in the development of targeted screening and prevention strategies as well as identify hidden epidemic drivers. Here we present molecular transmission networks re-constructed from HIV-1 pol sequences collected from injection drug users located in major urban centres in Pakistan. Dried blood spots (DBS) were collected on Whatman 903 cards from IDUs in Karachi (n = 300), Hyderabad (n = 300), and Peshawar (n = 257). Nucleic acids were extracted using an easyMAG instrument. Protease and part of the reverse transcriptase genes were amplified and sequenced using an in-house HIV genotyping assay. Transmission networks were re-constructed using publicly available bioinformatics software. Most of the HIV pol sequences (75%) from 96 individuals formed connected nodes arranged in 7 clusters, ranging in size from 3 to 33 individuals. Clusters were mainly homogeneous, in which 87 inferred transmission events (90.6%) occurred within a particular city. A small number (9.4%) of transmission events occurred between cities. The highest number of inter-city transmission events (7.3%) occurred between Karachi and Hyderabad while only 2 (2.1%) occurred between Karachi and Peshawar. Transmission events were not observed between Hyderabad and Peshawar. The majority of sequences analyzed formed localized transmission networks partitioned according to city of collection, although some inter-city linkages are apparent. Reconstruction of transmission networks based on sequencing-based approaches has the potential to expand our understanding of the HIV epidemic currently taking place in Pakistan especially if used in combination with epidemiological data. Particular attention maybe warranted for Karachi since transmission linkages were observed with all the other cities included in this study.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".