Aptamer and DNAzyme Based Colorimetric Biosensors for Pathogen Detection
Bibliographic record
Abstract
The detection of pathogens is critical for preventing and controlling health hazards across clinical, environmental, and food safety sectors. Functional nucleic acids (FNAs), such as aptamers and DNAzymes, have emerged as versatile molecular tools for pathogen detection due to their high specificity and affinity. This review focuses on the in vitro selection of FNAs for pathogens, with emphasis on the selection of aptamers for specific biomarkers and intact pathogens, including bacteria and viruses. Additionally, the selection of DNAzymes for bacterial detection is discussed. The integration of these FNAs into colorimetric biosensors has enabled the development of simple, cost-effective diagnostic platforms. Both non-catalytic and catalytic colorimetric biosensors are explored, including those based on gold nanoparticles, polydiacetylenes, protein enzymes, G-quadruplexes, and nanozymes. These biosensors offer visible detection through color changes, making them ideal for point-of-care diagnostics. The review concludes by highlighting current challenges and future perspectives for advancing FNA-based colorimetric biosensing technologies for pathogen detection.
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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 | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.004 |
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".