Higher Affinity Enables More Accurate Detection of SARS‐CoV‐2 in Human Saliva Using Aptamer‐Based Litmus Test
Bibliographic record
Abstract
Abstract Many aptamers have been generated by systematic evolution of ligands by exponential enrichment (SELEX) to recognize spike proteins of the severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2&ek), some of which have been engineered into dimeric and trimeric versions for enhanced affinity for diagnostic applications. However, no studies have been conducted to compare the utilities of monomeric, dimeric and trimeric aptamers in diagnostic assays with real clinical samples to answer the question of what levels of affinity an aptamer must have for accurate clinical diagnostics. Herein, we carried out a comparative study with two monomeric aptamers MSA1 and MSA5, one dimeric aptamer and two homotrimeric aptamers constructed with MSA1 and MSA5, with affinity varying by 1000‐fold. Using a colorimetric sandwich assay to analyze 48 human saliva samples, we found that the trimeric aptamer assay (Kd≈10 pM) can identify the SARS‐CoV‐2 infection much more accurately than the dimeric aptamer assay (Kd≈100 pM) and monomeric aptamer assay (Kd≈10,000 pM). Based on the experimental data, we theoretically predict the levels of affinity an aptamer needs to possess to achieve 80–100 % sensitivity and 100 % specificity. The findings from this study highlight the need for deriving very high affinity aptamers to enable highly accurate detection of viral infection for future pandemics.
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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) | 0.000 | 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".