Recent advances in aptamer-based biosensors for viral detection
Bibliographic record
Abstract
Viral pathogens pose a persistent and devastating threat to global health, as starkly demonstrated by recent pandemics. The cornerstone of an effective response is rapid and reliable viral detection. Yet, conventional methods like viral culture, immunoassays, and nucleic acid amplification tests are hampered by limitations in speed, cost, sensitivity, and portability. Aptamers, single-stranded DNA or RNA oligonucleotides selected in vitro, have emerged as powerful molecular recognition elements that rival antibody affinity while offering superior thermal stability, manufacturability, and design flexibility. These attributes make them ideal for integration into next-generation biosensors. This review systematically summarizes recent advancements in aptamer-based biosensors for viral detection. It provides a comprehensive analysis of key methodologies for selecting virus-specific aptamers and a detailed examination of the various signal transduction mechanisms employed, including electrochemical, fluorescent, colorimetric, Surface Plasmon Resonance (SPR), and Surface-Enhanced Raman Scattering (SERS) biosensors. By critically evaluating the integration of high-affinity aptamers with diverse biosensing architectures, this work aims to establish a clear framework for understanding current progress and future directions. The review underscores the potential of these biosensors to deliver the rapid, sensitive, and field-deployable devices urgently needed for pandemic preparedness and effective viral outbreak management.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| 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) | 0.001 | 0.002 |
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".