Unlocking Pb<sup>2+</sup> Sensing Potential in a DNA G-Quadruplex via Loop Modification with Fluorescent Chalcone Surrogates
Bibliographic record
Abstract
The ability of guanine (G)-rich DNA to bind toxic lead (Pb 2+ ) ions within a G-quadruplex (GQ) motif is a leading DNA biosensor strategy. A major analytical hurdle for GQ detection of Pb 2+ is competitive GQ templating by potassium (K + ) ions. We employ the on-strand DNA synthesis of internal fluorescent chalcone surrogates within the 15-mer thrombin binding aptamer (TBA15) to address this challenge. Replacement of thymidine at the 3-position (T3) within TBA15 with an indole-4-hydroxy-indanone (Ind4HI) chalcone strongly decreases K + -GQ stability while enhancing Pb 2+ -GQ stability to increase Pb 2+ binding specificity. The new T3-Ind4HI probe exhibits a 15-fold increase in fluorescence intensity upon binding of Pb 2+ by the modified TBA15 and can detect 6.4 nM Pb 2+ in the presence of 10 mM K + . Thus, replacement of the T3 residue of TBA15 with the new Ind4HI probe modulates metal ion affinity by native TBA15 to solve the analytical challenge posed by K + in real water samples for detecting Pb 2+ to meet regulatory guidelines by using a GQ biosensor.
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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 | 0.000 |
| 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".