Metal and pH-Dependent Aptamer Binding of Tetracyclines Enabling Highly Sensitive Fluorescence Sensing
Bibliographic record
Abstract
Tetracyclines are a widely used group of antibiotics, many of which are currently only used in veterinary medicine and animal husbandry due to their adverse side effects. For the detection of tetracyclines, we previously reported a DNA aptamer named OTC5 that binds to tetracycline, oxytetracycline, and doxycycline with similar KD’s of ~100 nM. Tetracyclines have an intrinsic fluorescence that is enhanced upon binding to OTC5, which can be used as a label-free and dye-free sensor. In this work, the effect of pH and metal ions on the sensor was studied. Mg2+ ions are required for the binding of OTC5 to its target with an optimal concentration of 2 mM. Other metal ions including Ca2+ and Zn2+ can also support aptamer binding. Although Mn2+ barely supported binding, the binding can be rescued by Mg2+. ITC studies confirmed that OTC5 had a KD of 0.2 μM at a pH of 6.0 and 0.03 μM at a pH of 8.3. Lower pH (pH 6) showed better fluorescence enhancement than higher pH (pH 8.3), although a pH of 6.0 had slightly higher KD values. Under optimized sensing conditions, sensors with limit of detections (LODs) of 0.1–0.7 nM were achieved for tetracycline, oxytetracycline, and doxycycline, which are up to 50-fold lower than previously reported. Milk samples were also tested yielding an LOD of 16 nM oxytetracycline at a pH of 6.0.
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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 | 0.001 |
| 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 | 0.000 |
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".