Selection, Characterization, and Biosensing Application of High Affinity Congener-Specific Microcystin-Targeting Aptamers
Bibliographic record
Abstract
The efficiency of current microcystin detection methods has been hampered by the low detection limits required in drinking water and that routine detection is restricted to a few of the congeners with high degree of undesired cross-reactivity. Here, we report the development of novel microcystin-targeting molecules and their application in microcystin detection. We have selected DNA aptamers from a diverse random library that exhibit high affinity and specificity to microcystin-LR, -YR, and -LA. We obtained aptamers that bind to all chosen congeners with high affinity with K(D) ranging from 28 to 60 nM. More importantly, we also obtained aptamers that are selective among the different congeners, with selectivity from 3-folds difference in binding affinity to total discrimination (K(D) of 50 nM versus nonspecific binding). Electrochemical aptasensors constructed with the selected aptamers were able to achieve sensitive and congener-specific microcystin detection with detection limit as low as 10 pM.
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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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".