Complex Subtype Diversity of HIV-1 Among Drug Users in Major Kenyan Cities
Bibliographic record
Abstract
Drug users are increasingly recognized as a key population driving human immunodeficiency virus (HIV) spread in sub-Saharan Africa. To determine HIV-1 subtypes circulating in this population group and explore possible geographic differences, we analyzed HIV-1 sequences among drug users from Nairobi, Mombasa, and Kisumu in Kenya. We sequenced gag and env from 55 drug users. Subtype analysis from 220 gag clonal sequences from 54 of 55 participants (median = 4/participant) showed that 44.4% were A, 16.7% were C, 3.7% were D, and 35.2% were intersubtype recombinants. Of 156 env clonal sequences from 48 of 55 subjects (median = 3/participant), 45.8% were subtype A, 14.6% were C, 6.3% were D, and 33.3% were recombinants. Comparative analysis of both genes showed that 30 (63.8%) participants had concordant subtypes, while 17 (36.2%) were discordant. We identified one genetically linked transmission pair and two cases of dual infection. These data are indicative of extensive HIV-1 intersubtype recombination in Kenya and suggest decline in subtype D prevalence.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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".