Recent Progress in Nucleic Acid Aptamer-Based Biosensors and Bioassays
Bibliographic record
Abstract
As the key constituents of the genetic code, the importance of nucleic acids to life has long been appreciated. Despite being composed of only four structurally similar nucleotides, single-stranded nucleic acids, as in single-stranded DNAs and RNAs, can fold into distinct three-dimensional shapes due to specific intramolecular interactions and carry out functions beyond serving as templates for protein synthesis. These functional nucleic acids (FNAs) can catalyze chemical reactions, regulate gene expression, and recognize target molecules. Aptamers, whose name is derived from the Latin word aptus meaning "to fit", are oligonucleotides that can bind their target ligands with high affinity and specificity. Since aptamers exist in nature but can also be artificially isolated from pools of random nucleic acids through a process called in vitro selection, they can potentially bind a diverse array of compounds. In this review, we will discuss the research that is being done to develop aptamers against various biomolecules, the progress in engineering biosensors by coupling aptamers to signal transducers, and the prospect of employing these sensors for a range of chemical and biological applications. Advances in aptamer technology emphasizes that nucleic acids are not only the fundamental molecules of life, they can also serve as research tools to enhance our understanding of life. The possibility of using aptamer-based tools in drug discovery and the identification of infectious agents can ultimately augment our quality of life.
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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) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.005 |
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".