Detection and Clinical Implications of Monovalent Rotavirus Vaccine-Derived Virus Strains in Children with Gastroenteritis in Alberta, Canada
Bibliographic record
Abstract
While rotavirus vaccine programs effectively protect against severe rotavirus gastroenteritis, rotavirus vaccine strains have been identified in the stool of vaccinated children and their close contacts suffering from acute gastroenteritis. The prevalence of vaccine strains, the emergence of vaccine-derived strains, and their role in acute gastroenteritis are not well studied. We developed a locked nucleic acid reverse transcription real-time PCR assay (LNA-RTqPCR) to detect the monovalent rotavirus vaccine (RV1) Rotarix nonstructural protein 2 (NSP2) in children with acute gastroenteritis and healthy controls, and validated it using sequence-confirmed RV1 strains. The association between RV1-derived strains and gastroenteritis was determined using logistic regression. The new assay exhibited 100% (95% CI 91.7%, 100%) diagnostic sensitivity and 99.4% (95% CI 96.2%, 100%) diagnostic specificity, with a detection limit of 9.86 copies/reaction and qPCR efficiency of 99.7%. Using this assay, we identified the presence of RV1-derived NSP2 sequences in 7.7% of rotavirus gastroenteritis cases and 98.6% of rotavirus-positive healthy children (94.4% had previously received the RV1). Among gastroenteritis cases, those whose stool contained RV1-derived strains had milder gastroenteritis symptoms compared to that of natural rotavirus infections. We observed no significant association between RV1-derived strains and gastroenteritis (odds ratio [OR] 0.98; 95% CI 0.60, 1.72). Our study demonstrated that the new assay is suitable for monitoring RV1-derived rotavirus strain circulation and that the RV1-derived strains are not associated with development of gastroenteritis symptoms.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".