Electrochemical molecularly imprinted polymer sensors in viral diagnostics: Innovations, challenges and case studies
Bibliographic record
Abstract
Molecularly imprinted polymers (MIPs) are synthetic equivalent of antibodies and have been widely used in electrochemical sensing as recognition elements. They offer advantages over traditional recognition elements such as antibodies, nucleic acids and aptamers due to their simple synthesis, lower production costs, greater chemical and physical stability, and robust performance in diverse environments. Improved detection techniques and combining MIPs with materials like metal nanoparticles, carbon nanotubes, aptamers, metal organic frameworks, quantum dots, and electrochemically active internal probes show increasing potential. These combinations could become a reliable method for detecting viruses quickly, with performance similar or better than standard techniques. In this review article we provide detailed case studies covering ten different viruses (Bean pod mottle virus, Dengue virus, Zika virus, Foot-and-mouth disease virus, Human papillomavirus, Hepatitis C virus, Human immunodeficiency virus, Influenza A virus, Norovirus, Severe acute respiratory syndrome coronavirus 2) and over forty specific examples. We summarize the recent advances in the development of electrochemical MIP-based sensors for the diagnostics of viral diseases and compare their performance. Additionally, challenges and future perspectives of MIPs as promising recognition elements are discussed.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".