Concentration of Virus Particles from Environmental Water and Wastewater Samples Using Skimmed Milk Flocculation and Ultrafiltration
Bibliographic record
Abstract
Water and wastewater-based epidemiology have emerged as alternative methods to monitor and predict the course of outbreaks in communities. The recovery of microbial fractions, including viruses, bacteria, and microeukaryotes from wastewater and environmental water samples is one of the challenging steps in these approaches. In this study, we focused on the recovery efficiency of sequential ultrafiltration and skimmed milk flocculation (SMF) methods using Armored RNA as a test virus, which is also used as a control by some other studies. Prefiltration with 0.45 µm and 0.2 µm membrane disc filters were applied to eliminate solid particles before ultrafiltration to prevent the clogging of ultrafiltration devices. Test samples, processed with the sequential ultrafiltration method, were centrifuged at two different speeds. An increased speed resulted in lower recovery and positivity rates of Armored RNA. On the other hand, SMF resulted in relatively consistent recovery and positivity rates of Armored RNA. Additional tests conducted with environmental water samples demonstrated the utility of SMF to concentrate other microbial fractions. The partitioning of viruses into solid particles might have an impact on the overall recovery rates, considering the prefiltration step applied before the ultrafiltration of wastewater samples. SMF with prefiltration performed better when applied to environmental water samples due to lower solid concentrations in the samples and thus lower partitioning rates to solids. In the present study, the idea of using a sequential ultrafiltration method arose from the necessity to decrease the final volume of the viral concentrates during the COVID-19 pandemic, when the supply of the commonly used ultrafiltration devices was limited, and there was a need for the development of alternative viral concentration methods.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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 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".