Aptamer-based strategies for recognizing adenine, adenosine, ATP and related compounds
Bibliographic record
Abstract
Adenine is a key nucleobase, adenosine is an endogenous regulator of the immune system, while adenosine triphosphate (ATP) is the energy source of many biological reactions. Selective detection of these molecules is useful for understanding biological processes, biochemical reactions and signaling. Since 1993, various aptamers have been reported to bind to adenine and its derivatives. In addition, the adenine riboswitch was later discovered. This review summarizes the efforts for the selection of RNA and DNA aptamers for adenine derivatives, and we pay particular attention to the specificity of binding. In addition, other molecular recognition strategies based on rational sequence design are also introduced. Most of the work in the field was performed on the classic DNA aptamer for adenosine and ATP reported by the Szostak group. Based on this aptamer, some representative applications such as the design of fluorescent, colorimetric and electrochemical biosensors, intracellular imaging, and ATP-responsive materials are also described. In addition, we critically review the limit of the reported aptamers and also important problems in the field, which can give future research opportunities.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".