First detection of SARS-CoV-2 proteins in wastewater samples by mass spectrometry
Bibliographic record
Abstract
Abstract On March 12, 2020, the World Health Organization (WHO) declared COVID-19 as a global pandemic. COVID-19 is produced by a novel β-coronavirus known as Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) [1]. Several studies have detected SARS-CoV-2 RNA in urine, feces, and other biofluids from both symptomatic and asymptomatic people with COVID-19 [2], suggesting that SARS-CoV-2 RNA could be detected in human wastewater [3]. Thus, wastewater-based epidemiology (WBE) is now used as an approach to monitor COVID-19 prevalence in many different places around the world [4-10] . Reverse transcription quantitative polymerase chain reaction (RT-qPCR) is the most common SARS-CoV-2 detection method in WBE, but there are other methods for viral biomolecule detection that could work as well. The aim of this study was to evaluate the presence of SARS-CoV-2 proteins in untreated wastewater (WW) influents collected from six wastewater treatment plants (WWTPs), from Durham Region, Ontario, Canada, using a LC-MS/MS-based proteomics approach. We identified many SARS-CoV-2 proteins in these wastewater samples, with peptides from pp1ab being the most consistently detected and with consistent abundance.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| 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".