Multiplexed SELEX for Sulfonamide Antibiotics Yielding a Group-Specific DNA Aptamer for Biosensors
Bibliographic record
Abstract
The widespread use of sulfonamide (SA) antibiotics in animal husbandry has led to residues of SAs in the environment, causing adverse effects to the ecosystem and a risk of bacterial resistance, which is a potential threat to public health. Therefore, it is highly desirable to develop simple, high-throughput methods that can detect multiple SAs simultaneously. In this study, we isolated aptamers with different specificities based on a multi-SA systematic evolution of ligands by the exponential enrichment (SELEX) strategy using a mixture of sulfadimethoxine (SDM), sulfaquinoxaline (SQX), and sulfamethoxazole (SMZ). Three aptamers were obtained, and one of them showed a similar binding to all tested SAs, with dissociation constant ( K d ) ranging from 0.22 to 0.63 μM. For the other two aptamers, one is specific for SQX, and the other is specific for SDM and sulfaclozine. A label-free detection method based on the broad-specificity aptamer was developed for the simultaneous detection of six SAs, with detection of limits ranging from 0.14 to 0.71 μM in a lake water sample. The aptasensor has no binding for other broad-spectrum antibiotics such as β-lactam antibiotics, quinolones, tetracyclines, and chloramphenicol. This work provides a promising biosensor for rapid, multiresidue, and high-throughput detection of SAs, as well as a shortcut for the preparation of different specific recognition elements required for the detection of broad-spectrum antibiotics.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".