1873. Comparative performance of RTqPCR vs RTddPCR for the detection of SARS-CoV-2 in wastewater (WW) collected from a range of sites and scales across the sewer network of Calgary, Alberta
Bibliographic record
Abstract
Abstract Background We sought to compare WW SARS-CoV-2 RNA detection across a range of sites and scales using RTqPCR and RTddPCR. Figure. Methods Composite-24h WW was collected from a WW treatment plant (WTP; n=18), a neighborhood (Nb1; n=12) and three hospitals; H-1, H-2, and H-3 (3-sites; A-C)(n=84). RNA was extracted using the 4S-silica column method. RTqPCR (QuantStudio5, ThermoFisher) and RTddPCR (C1000 Thermal Cycler and QX200 Droplet Reader, BioRad) quantified SARS-CoV-2 RNA nucleocapsid (N2, US CDC) and envelope (E Sarbeco, Corman et al 2020) in triplicate. Fisher’s exact test was used to compare assay sensitivity. Correlations between modalities and RNA - clinically-confirmed COVID-19 cases (defined by postal code of primary residence using 5-day rolling average) was assessed using Persons correlation. Results 114 samples were tested (02/23/2021-04/22/2021). SARS-CoV-2-N2 was identified in 90/114 (79%) by RTqPCR and 89/114 (78%) by ddPCR (p=1). SARS-CoV-2 E was found in 72/114 (63%) by RTqPCR and 90/114 (79%) by ddPCR, p=0.01. Correlations between modalities were strongest for N2 relative to E across all sites (see Table). N2 correlated with clinically diagnosed cases for both modalities greater at the level of the WTP (RTqPCR; r=0.8972, p< 0.0001and ddPCR; 0.933, p< 0.0001) relative to neighborhood (RTqPCR; r=0.6, p=0.04 and ddPCR; 0.60, p=0.04). E correlated to a lesser degree with cases at WTP (RTqPCR; r=0.65, p=0.0035 and ddPCR; 0.88, p=< 0.001) and neighborhoods (RTqPCR; r=0.40, p=0.20 and ddPCR; r=0.43, p=0.16). Conclusion SARS-CoV-2 detection of N2 was similar between RTqPCR and RTddPCR across a range of sites and scales in the sewershed, and this correlated best with clinical cases whereas E detection was superior with ddPCR. Disclosures All Authors: No reported disclosures.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".