Incorporation of Boronic Acid into Aptamer-Based Molecularly Imprinted Hydrogels for Highly Specific Recognition of Adenosine
Bibliographic record
Abstract
Molecularly imprinted polymers (MIPs) and aptamers are two types of molecular recognition strategies. We are interested in combining them to further improve specificity. Adenosine is a model analyte for developing aptamer-based biosensors, but the most commonly used DNA aptamer for adenosine can also bind deoxyadenosine with a similar affinity ( K a (adenosine)/ K a (deoxyadenosine) = 1.07). Since adenosine and deoxyadenosine differ by the former having a cis -diol, and boronic acid can bind cis -diol specifically, in this work, an acrydite-modified adenosine aptamer was copolymerized with a boronic acid containing monomer, 3-acrylamidophenylboronic acid (AAPBA) in the presence of adenosine as the template for imprinting. Isothermal titration calorimetry (ITC) and SYBR Green I staining were used to measure its binding. The AAPBA-containing aptamer-MIP exhibited a 115-fold high selectivity for adenosine against deoxyadenosine at pH 6.4, and 230-fold for adenosine against cytidine. We recently found that boronic acid containing hydrogels can nonspecifically adsorb DNA oligonucleotides and inhibit aptamer binding. The ribose in adenosine may interact with the boronic acid unit and decrease its inhibition effect to the aptamer in the MIP. However, for deoxyadenosine, it does not bear a cis -diol and thus cannot rescue the aptamer. This work provides insight into the combination of aptamers with other functional groups, which may further broaden applications in ways that free aptamers cannot achieve alone.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".