Maximizing viral nucleic acid yield from passive samplers: Evaluating elution and extraction protocols
Bibliographic record
Abstract
The COVID-19 pandemic has underscored the need for effective viral tracking in aqueous environments, particularly for non-enteric viruses. Despite advances in wastewater monitoring, surveillance of viruses in freshwater remains limited due to traditional sampling challenges. This study refines GAC-based passive sampling protocols by determining optimal extraction and elution methods for enhancing the recovery of viral nucleic acids in freshwater. Three commercially available total nucleic acid (TNA) extraction kits and four elution buffers were assessed for their ability to recover SARS-CoV-2 and bacteriophage MS2 from GAC-based samplers. The Promega Wizard® Enviro Total Nucleic Acid Kit, paired with a Tween®20-based buffer, provided the highest virus recovery efficiency for GAC-based passive sampling. Field-scale applications demonstrated the effectiveness of GAC-based passive samplers in capturing SARS-CoV-2, INFA, RSV, and MeV using the optimized protocols. The combination of the Tween®20 based buffer and the Promega kit led to increased detection frequencies in grab samples, which remained lower in recovery than passive sampling. This study underscores the importance of selecting appropriate TNA extraction kits and elution buffers to maximize virus recovery from passive samples. By optimizing these protocols, we enhance the sensitivity and reliability of viral surveillance in freshwater ecosystems.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".