Optimizing RT-qPCR multiplex assays for simultaneous detection of enteric and respiratory viruses in wastewater
Bibliographic record
Abstract
This study presents the successful optimization of enteric RT-qPCR multiplex assays for detecting Norovirus GII, Enterovirus, and Coxsackievirus A6 or Enterovirus D68 in municipal wastewater samples. Additionally, optimization of a respiratory RT-qPCR multiplex assay to detect influenza A, respiratory syncytial virus, and SARS-CoV-2 was attempted. The enteric multiplex assays successfully detected Coxsackievirus A6 in wastewater during community outbreaks of hand-foot-mouth disease. Enterovirus D68 was also successfully detected in wastewater samples (Summer/Fall, 2022), which coincided with provincial public health reports of Enterovirus D68 cases. Attempting to optimize the respiratory multiplex assay resulted in challenges due to oligonucleotide cross-reactivity and cross-talk. Specifically, when Texas Red and FAM probes detected higher abundance targets, they interfered with the Cy5 and HEX fluorophore probes that detected lower-abundance targets. In contrast, selecting probes with Cy5/HEX for high-abundance targets and Texas Red/FAM for lower-abundance targets provided more robust results.
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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".