Aptamer-based biosensors for biomedical diagnostics
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it