(Digital Presentation) Biomimic Nanotemplating Assay Based on Molecularly Imprinted Polymers for the Impedimetric Detection of Sars-Cov-2 and Influenza A Spike Proteins in Untreated Saliva
Bibliographic record
Abstract
With the continuously fluctuating incidence of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), critical gaps in the field of rapid diagnostic testing have been exposed, particularly in the diagnosis of viral respiratory infections. Current gold standard methods rely on real time quantitative polymerase chain reaction (RT-qPCR) for the detection of viral nucleic acids, but these tests are challenged with long turnaround times, costly centralized laboratory equipment and the need for trained personnel to execute the protocols. With the growing number of emerging variants that can evade both immune responses and inoculation, addressing current testing challenges is critical to manage the spread of viral infections. Here, we propose the design of a novel assay based on our previously studied gold nano/micro islands (NMIs) as a core for the fabrication of an ultrathin molecularly imprinted polymer (MIP) for the impedimetric detection of SARS-CoV-2 and Influenza A spike proteins (SPs) in untreated saliva within 10 minutes. The proposed electrofabrication protocol is rapidly adaptable to a diverse repertoire of protein biomarkers; in this work, we demonstrate the impedimetric detection of the SARS-CoV-2 original strain, Alpha B.1.1.7, Delta B.1.617.2 and Omicron B.1.1.529 variant SP, as well as the Influenza A SP within physiologically relevant ranges and at a low limit of detection to enable the diagnosis of acute infections. Validation was performed at two unique test sites with 51 SARS-CoV-2 patient samples to demonstrate an overall 100% sensitivity and 100% specificity of the NMIs/MIPs assay. Robust quantification of the electrochemical assay was confirmed against RT-qPCR, which effectively enabled statistically significant (p < .0005) viral load quantification on a rapid, miniaturized, and ultrasensitive platform. This novel technology presents the development of a quantitative and versatile electrochemical assay with the potential for the rapid detection of current and future viral respiratory infections, which can guide future electrochemical clinical and commercial point-of-care testing platforms.
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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 | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".