Lanthanide‐FRET Molecular Beacons for microRNA Biosensing, Logic Operations, and Physical Unclonable Functions
Bibliographic record
Abstract
Abstract Time‐resolved or time‐gated (TG) biosensing and bioimaging with luminescent lanthanide probes and Förster resonance energy transfer (FRET) have significantly advanced bioanalytical chemistry. However, the development of lanthanide‐based molecular beacons (MBs) has been rather limited. Here, we designed DNA stem‐loop MB probes against two different microRNAs (miR‐21 and miR‐27b) using Tb and Eu FRET donors and quenching (BHQ2) and fluorescent (Cy3) FRET acceptors. Limits of detection down to 190 pM and duplexed miR‐21/miR‐27b quantification at low nanomolar concentrations with Tb‐BHQ2 and Eu‐BHQ2 TG‐FRET MBs demonstrated the versatility and high analytical performance of lanthanide‐based MBs. The particular donor‐acceptor distances in the Tb‐Cy3 MB resulted in inverted nucleic acid target concentration‐dependent TG PL intensities in short (e. g., 0 to 40 μs) and long (e. g., 0.1 to 2.1 ms) TG detection windows after pulsed excitation. We showed that this specific feature of our TG‐FRET MBs can be adapted to the design of molecular logic devices (NOR, OR, NAND, AND, XNOR, XOR, IMPLEMENT, and INHIBIT). Moreover, the almost unlimited choice of TG detection windows and the distinct spectral features of Tb and Cy3 over a broad visible spectral range could be exploited to devise biophotonic physical unclonable functions for highly secure authentication and identification. Our study manifests the versatility of lanthanides for advanced biophotonic applications.
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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 | 0.001 | 0.001 |
| 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 | 0.001 | 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".