A novel colorimetric potassium sensor based on the substitution of lead from G-quadruplex
Bibliographic record
Abstract
Potassium ions play diverse roles in biological processes, and abnormal K(+) levels are the hallmarks of diseases. However, the potential clinical application of the developed DNA-based K(+) sensors remains a challenge due to the presence of Pb(2+) in blood samples. In this contribution, a novel colorimetric potassium sensing assay that functions in the presence of Pb(2+) is reported. This approach is based on conformational switching of a hairpin DNA structure to a G-quadruplex. Specifically, the hairpin DNA containing G-rich aptamer T30695 is exposed to successive amounts of Na(+), Pb(2+) and K(+). These cations induce formation of the corresponding metal-stabilized G-quadruplex, which acts as DNAzyme (with hemin as a cofactor) for the catalytic oxidation of ABTS by H(2)O(2). Importantly, studies presented here show that K(+) replaces Pb(2+) from the G-quadruplex to form K(+)-stabilized G-quadruplex, which differ in the catalytic behavior. With Pb(2+)-stabilized G-quadruplex as a probe, a highly sensitive and selective colorimetric detection of K(+) is achieved in the presence of Pb(2+) and excessive Na(+) (140 mM) with the detection limit of 1.9 nM. This system represents the first known DNAzyme-based colorimetric K(+) sensor, which works in the presence of Pb(2+). Finally, the sensor is successfully applied for K(+) detection in a real human serum sample containing Pb(2+).
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".