An Aptamer-Based Hydrogel Sensing Platform Enabling Low Micromolar Detection of Small Molecules Using Low Sample Volumes
Bibliographic record
Abstract
Poor fluorescence recovery at low analyte dosages and slow ligand binding kinetics are critical challenges currently limiting the use of aptamer-functionalized hydrogels for sensing small molecules. In this paper, we report an adenosine-responsive hydrogel sensor that integrates FRET-signaling aptamer switches into in situ-gelling thin-film hydrogels. The hydrogel sensor is able to entrap a high proportion of the sensing probes (>70% following vigorous washing), delay nucleolytic degradation, stabilize weak aptamer complexes to improve hybridization affinity and suppress fluorescence background, and provide high sensitivity in biological fluids (i.e., undiluted human serum). Furthermore, the developed hydrogel sensors were able to achieve low limits of detection (5.3 μM in buffer and 8.8 μM in serum) within 4 min of exposure to the sample, with signal generation requiring only 20 μL/well of analyte sample. The physical nature of the aptamer encapsulation allows this approach to accommodate virtually any small-molecule aptamer, avoiding the need for covalent anchoring and the complex modification of nucleic acid sequences typically required for effective aptamer-based molecular recognition.
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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) | 0.000 | 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 | 0.001 |
| 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".