Aptamer-based biosensors for biomedical diagnostics
Bibliographic record
Abstract
Aptamers are single-stranded nucleic acids that selectively bind to target molecules. Most aptamers are obtained through a combinatorial biology technique called SELEX. Since aptamers can be isolated to bind to almost any molecule of choice, can be readily modified at arbitrary positions and they possess predictable secondary structures, this platform technology shows great promise in biosensor development. Over the past two decades, more than one thousand papers have been published on aptamer-based biosensors. Given this progress, the application of aptamer technology in biomedical diagnosis is still in a quite preliminary stage. Most previous work involves only a few model aptamers to demonstrate the sensing concept with limited biomedical impact. This Critical Review aims to summarize progress that might enable practical applications of aptamers for biological samples. First, general sensing strategies based on the unique properties of aptamers are summarized. Each strategy can be coupled to various signaling methods. Among these, a few detection methods including fluorescence lifetime, flow cytometry, upconverting nanoparticles, nanoflare technology, magnetic resonance imaging, electronic aptamer-based sensors, and lateral flow devices have been discussed in more detail since they are more likely to work in a complex sample matrix. The current limitations of this field include the lack of high quality aptamers for clinically important targets. In addition, the aptamer technology has to be extensively tested in a clinical sample matrix to establish reliability and accuracy. Future directions are also speculated to overcome these challenges.
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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) | 0.002 | 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 | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.007 |
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".