Integrating Water Purification with Electrochemical Aptamer Sensing for Detecting SARS-CoV-2 in Wastewater
Bibliographic record
Abstract
Wastewater analysis of pathogens, particularly SARS-CoV-2, is instrumental in tracking and monitoring infectious diseases in a population. This method can be used to generate early warnings regarding the onset of an infectious disease and predict the associated infection trends. Currently, wastewater analysis of SARS-CoV-2 is almost exclusively performed using polymerase chain reaction for the amplification-based detection of viral RNA at centralized laboratories. Despite the development of several biosensing technologies offering point-of-care solutions for analyzing SARS-CoV-2 in clinical samples, these remain elusive for wastewater analysis due to the low levels of the virus and the interference caused by the wastewater matrix. Herein, we integrate an aptamer-based electrochemical chip with a filtration, purification, and extraction (FPE) system for developing an alternate in-field solution for wastewater analysis. The sensing chip employs a dimeric aptamer, which is universally applicable to the wild-type, alpha, delta, and omicron variants of SARS-CoV-2. We demonstrate that the aptamer is stable in the wastewater matrix (diluted to 50%) and its binding affinity is not significantly impacted. The sensing chip demonstrates a limit of detection of 1000 copies/L (1 copy/mL), enabled by the amplification provided by the FPE system. This allows the integrated system to detect trace amounts of the virus in native wastewater and categorize the amount of contamination into trace (<10 copies/mL), medium (10-1000 copies/mL), or high (>1000 copies/mL) levels, providing a viable wastewater analysis solution for in-field use.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 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".