High‐Precision Viral Detection Using Electrochemical Kinetic Profiling of Aptamer‐Antigen Recognition in Clinical Samples and Machine Learning
Bibliographic record
Abstract
High-precision viral detection at point of need with clinical samples plays a pivotal role in the diagnosis of infectious diseases and the control of a global pandemic. However, the complexity of clinical samples that often contain very low viral concentrations makes it a huge challenge to develop simple diagnostic devices that do not require any sample processing and yet are capable of meeting performance metrics such as very high sensitivity and specificity. Herein we describe a new single-pot and single-step electrochemical method that uses real-time kinetic profiling of the interaction between a high-affinity aptamer and an antigen on a viral surface. This method generates many data points per sample, which when combined with machine learning, can deliver highly accurate test results in a short testing time. We demonstrate this concept using both SARS-CoV-2 and Influenza A viruses as model viruses with specifically engineered high-affinity aptamers. Utilizing this technique to diagnose COVID-19 with 37 real human saliva samples results in a sensitivity and specificity of both 100 % (27 true negatives and 10 true positives, with 0 false negative and 0 false positive), which showcases the superb diagnostic precision of this method.
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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 | 0.000 | 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".