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Record W2570006560 · doi:10.1089/aid.2016.0321

Complex Subtype Diversity of HIV-1 Among Drug Users in Major Kenyan Cities

2017· article· en· W2570006560 on OpenAlexaff
Kamini Gounder, Micah Oyaro, Nagavelli Padayachi, Thando Zulu, Túlio de Oliveira, John Wylie, Thumbi Ndung’u

Bibliographic record

VenueAIDS Research and Human Retroviruses · 2017
Typearticle
Languageen
FieldImmunology and Microbiology
TopicHIV Research and Treatment
Canadian institutionsUniversity of Manitoba
FundersWellcome Trust
KeywordsKenyaHuman immunodeficiency virus (HIV)PopulationLentivirusVirologyBiologyTransmission (telecommunications)DemographyVirusImmunologyViral diseaseMedicineEnvironmental healthEcology

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesScience and technology studies
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.003
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.093
GPT teacher head0.349
Teacher spread0.255 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations14
Published2017
Admission routes1
Has abstractyes

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