Increased detection of rotavirus using a real time reverse transcription‐polymerase chain reaction (RT‐PCR) assay in stool specimens from children with diarrhea
Bibliographic record
Abstract
Six-hundred and twenty-six stool specimens collected from children with diarrhea over a 12-month period were tested for rotavirus using a real time reverse transcription-polymerase chain reaction (RT-PCR) assay, a conventional nested PCR assay and by electron microscopy (EM). A fragment of 87 bp in a highly-conserved region of non-structural protein 3 (NSP3) in rotavirus genome was amplified by a single-step RT-PCR protocol in a closed-tube system. Rotavirus was detected in 123 samples (20%) with the real time RT-PCR assay, 113 samples (18%) with the nested-PCR assay, and 79 samples (13%) with EM. Using serial diluted nucleic acid extract, we compared the sensitivity of real time RT-PCR with conventional RT-PCR and conventional nested PCR assays. Real time RT-PCR was two to four logs more sensitive than the conventional assays. The reaction time required for the RT-PCR assay is about half the time required for the conventional nested-PCR. The real time RT-PCR assay is both simple and rapid with advantages including enhanced sensitivity and a lower risk for cross-contamination making it a useful tool for the detection of rotavirus in various situations including sporadic gastroenteritis, outbreaks, and environmental investigations. G(1) was the predominant type (89%), followed by G(2) (10%), and G(4) (1%). No rotavirus of G(3), G(8), and G(9) types were found. The peak season for rotavirus infection was January to May in northern Alberta.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".