Aptamer Engineering: Strategies for Discovering Functional Nucleic Acids for Next‐Generation Diagnostics and Biosensing
Bibliographic record
Abstract
Aptamers are highly selective nucleic acid ligands that overcome many challenges of antibodies, including structural instability and high production costs. The implementation of aptamers into routine research and commercial applications has advanced steadily, though it has been tempered by the limitations of existing discovery methods, which remain relatively slow and low-throughput. SELEX, the gold-standard method for aptamer discovery, has played a pivotal role in the field. As the demand for faster and more tailored solutions grows, there is increasing recognition of the value of newer approaches that can expedite the discovery and optimization of aptamers for specific applications. This review discusses recent advances in the methods used for aptamer engineering, including both wet lab methods performed in vitro, and dry lab methods performed in silico. Applications of engineered aptamers described in recent literature and discuss new developments that could further lead to innovative new applications of aptamers for use both within and outside of the laboratory are discussed. Within the laboratory, these applications include (but are not limited to) target-binding assays and integration into analytical nanomaterials, and outside of the laboratory, diagnostics and biosensing, which will be discussed at length.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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 | 0.002 |
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".