A hydrogel film microarray stabilizes very short structure switching aptamer duplexes to achieve enhanced sensing of small molecules
Bibliographic record
Abstract
• Developed hydrogel film microarray is able to physically entrap and thermally stabilize FRET-based structure switching aptamer constructs ( i.e. , duplexed aptamers) assembled with short quencher stems for improved binding affinity of small molecules. • Using an adenosine aptamer reporter, complexed with rationally located 7-bp quencher stems under decreasing aptamer surface density, hydrogel sensors can achieve detection limit between 1.2–3 µ m and binding affinity between 35.5–14.7 µ m , comparable to the native aptamer. The reduction of affinity associated with the modifications required to create FRET- aptamer constructs represents a key challenge hindering the practical adaptation of such systems for small molecule detection. While the use of shorter and rationally positioned quencher stems is desirable to enhance target-induced fluorescence recovery, the weak hybridization potential of such stems results in high levels of background fluorescence and poor sensitivity. Herein, we introduce a hydrogel microarray sensor able to thermally stabilize very short aptamer duplexes ( i.e. , quencher stems ≤10-bp), resulting in improved FRET efficiency and reduced background levels across a broad range of conditions. The optimal hydrogel microarray can physically entrap >75 % of loaded aptamer reporters while maintaining high accessibility to the target molecules, enabling accurate quantification of dose-responsive affinity interactions. Using an ATP aptamer reporter, the hydrogel sensor can achieve a binding affinity as low as 14.7 µ m − comparable to the native aptamer – and very low limits of detection (LOD) of 1.2 µ m in a pure buffer and 4.6 µ m in 50 % serum. Moreover, the developed hydrogel microarray is reusable, amenable to printing fabrication, and provides protection against nuclease-based degradation. This simple approach thus holds promise for advancing microarray aptamer technology in critical biosensing applications.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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) | 0.000 | 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 teacher head, 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".