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 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".