Genetic Diversity of Recently Acquired and Prevalent HIV, Hepatitis B Virus, and Hepatitis C Virus Infections in US Blood Donors
Bibliographic record
Abstract
BACKGROUND: Genetic variations of human immunodeficiency virus (HIV), hepatitis C virus (HCV), and hepatitis B virus (HBV) can affect diagnostic assays and therapeutic interventions. Recent changes in prevalence of subtypes/genotypes and drug/immune-escape variants were characterized by comparing recently infected vs more remotely infected blood donors. METHODS: Infected donors were identified among approximately 34 million US blood donations, 2006-2009; incident infections were defined as having no or low antiviral antibody titers. Viral genomes were partially sequenced. RESULTS: Of 321 HIV strains (50% incident), 2.5% were non-B HIV subtypes. Protease and reverse transcriptase (RT) inhibitor resistance mutations were found in 2% and 11% of infected donors, respectively. Subtypes in 278 HCV strains (31% incident) yielded 1a>1b>3a>2b>2a>4a>6d, 6e: higher frequencies of 3a in incident cases vs higher frequencies of 1b in prevalent cases were found (P = .04). Twenty subgenotypes among 193 HBV strains (26% incident) yielded higher frequencies of A2 in incident cases and higher frequencies of A1, B2, and B4 in prevalent cases (P = .007). No HBV drug resistance mutations were detected. Six percent of incident vs 26% of prevalent HBV contained antibody neutralization escape mutations (P = .01). CONCLUSIONS: Viral genetic variant distribution in blood donors was similar to that seen in high-risk US populations. Blood-borne viruses detected through large-scale routine screening of blood donors can complement molecular surveillance studies of highly exposed populations.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".