Bibliographic record
Abstract
Metal ions are essential to many chemical, biological, and environmental processes. In the past two decades, many DNA-based metal sensors have emerged. While the main biological role of DNA is to store genetic information, its chemical structure is ideal for metal binding via both the phosphate backbone and nucleobases. DNA is highly stable, cost-effective, easy to modify, and amenable to combinatorial selection. Two main classes of functional DNA were developed for metal sensing: aptamers and DNAzymes. While a few metal binding aptamers are known, it is generally quite difficult to isolate such aptamers. On the other hand, DNAzymes are powerful tools for metal sensing since they are selected based on catalytic activity, thus bypassing the need for metal immobilization. In the last five years, a new surge of development has been made on isolating new metal-sensing DNA sequences. To date, many important metals can be selectively detected by DNA often down to the low parts-per-billion level. Herein, each metal ion and the known DNA sequences for its sensing are reviewed. We focus on the fundamental aspect of metal binding, emphasizing the distinct chemical property of each metal. Instead of reviewing each published sensor, a high-level summary of signaling methods is made as a separate section. In principle, each signaling strategy can be applied to many DNA sequences for designing sensors. Finally, a few specific applications are highlighted, and future research opportunities are discussed.
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.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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) | 0.004 | 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".