Pathogen-focused metagenomic analysis reveals predominance of human rotavirus genotypes G3 and G12 in Zambian pediatric diarrhea cases
Bibliographic record
Abstract
Despite global improvements in water, sanitation, and rotavirus vaccination, rotavirus-associated diarrhea continues to cause significant morbidity and mortality among children in low-to-middle-income countries. Genomic surveillance is essential for evaluating vaccine efficacy and detecting emerging escape variants. In this study, we used VirCapSeq-VERT (VCS) to analyze rotavirus genetic diversity during Zambia’s 2023 diarrhea surveillance. Stool samples from under five children with diarrhea were collected from health facilities across nine provinces. Out of 245 samples, 72 were rotavirus qPCR-positive with C t <33 and underwent targeted viral enrichment and sequencing using VCS on the Illumina NextSeq2000. Bioinformatic analysis showed 70/72 strains had near complete genome constellations being genotyped as 45 Wa-like, 11 DS-like, and 14 reassortant strains. VP7 and VP4 analyses showed diverse genotypes (G1-G3, G8-G9, G12; P[4], P[6], P[8], P[11] clustering with vaccine and wild-type strains. Furthermore, G3 and G12 combined with P[4], P[6], and P[8] were the most predominant genotypes (35/70 and 13/70, respectively). Notably, nine samples had an M5 VP3 genotype with a 91% similarity to a simian rotavirus strain. Antigenic epitope analysis highlighted substitutions in P[6], G2, and G12, associated with immune escape. G3P[8] was the most common in severe cases. Fully vaccinated children showed significantly milder disease ( p = 0.033). This study highlights VCS’s utility in detecting viral diversity, reassortment, zoonotic transmission, and immune escape variants, providing crucial insights for assessing vaccine performance and public health strategies.
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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 | 0.000 |
| 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 | 0.000 |
| 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".