A nanoparticle-based thermo-dynamic aptasensor for small molecule detection
Bibliographic record
Abstract
Small molecules (MW < 1000 Da) represent a large class of biomarkers of interest. Recently, a new class of biosensors has been emerging thanks to the recognition properties of aptamers, short DNA or RNA single strands, selected against such small molecular targets. Among them, an adenosine-specific aptamer has been largely described and used due to its remarkable affinity to this small target (KD = 6 μM). In this paper, we achieved the proof-of-principle of an aptasensor based on the thermodynamic follow-up of adenosine binding with engineered split-aptamer sequences. The detection is carried out by surface plasmon resonance imaging of split-aptamer micro-arrays, while signal amplification is ensured by gold nanoparticles (AuNPs). This original approach based on DNA sequence engineering and AuNP conjugation enabled us to reach limits of detection (LOD) 200 times lower than the KD measured in solution with the native aptamer (LOD = 30 nM).
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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 | 0.001 | 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".