Sensing guanine and its derivatives: From molecular recognition to applications
Bibliographic record
Abstract
Guanine plays an indispensable role in building nucleic acids, and its derivatives take part in various cellular functions such as regulating biological reactions and signal transduction. Monitoring the levels of guanine and its derivatives is critical for understanding their biological roles and related diseases. Aside from traditional chromatography-based methods, majority of the current detections were based on electrochemistry and the oxidation activity of guanine, for which guanine and adenine often had a similar response. Over the last 30 years, various new sensing strategies have been developed. To provide researchers with more options for specific sensing of guanine and its derivatives, herein we review molecular recognition strategies based on nucleic acids, proteins, small organic molecules, molecularly imprinted polymers to nanomaterials. The mechanism of each molecular recognition strategy is discussed. Based on these target recognition molecules, we also critically review representative fluorescent and electrochemical sensors for guanine-related analytes from an application point of view, and provide readers with our perspectives to further grow this direction.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".