Aptamer-based biosensors: from SELEX to biomedical diagnostics
Bibliographic record
Abstract
With excellent binding properties, stability, programmable structures, and ease of modification, DNA aptamers are promising for developing biosensors for both point-of-care and continuous monitoring applications. Over the last few years, significant progress has been made in the selection of high-quality aptamers for important target molecules, fundamental understanding of aptamer binding, and biosensor development, especially in the form of portable sensors, continuous in vivo monitoring and wearable devices. For small molecule targets, library-immobilization-based selection has yielded over 100 high-quality short aptamers with well-defined secondary structures. For protein targets, engineering polyvalent aptamers and slow off-rate aptamers can better mimic the binding properties of antibodies allowing extensive washing and binding in complex sample matrices. New methods in cell-SELEX have also provided insights into the isolation of aptamers against rare surface biomarkers. This review aims to capture these developments, which will build a solid foundation for future research and development in aptamer-based biosensors. • New aptamer selection methods reviewed for small molecules, proteins and cells. • Wearable biosensors, in vivo continuous monitoring devices and lateral flow devices based on aptamers described. • Critical comparison of different aptamer selection and sensing strategies.
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".