Comprehensive Analytical Comparison of Strategies Used for Small Molecule Aptamer Evaluation
Bibliographic record
Abstract
Nucleic acid aptamers are versatile molecular recognition agents that bind to their targets with high selectivity and affinity. The past few years have seen a dramatic increase in aptamer development and interest for diagnostic and therapeutic applications. As the applications for aptamers expand, the need for a more standardized, stringent, and informative characterization and validation methodology increases. Here we performed a comprehensive analysis of a panel of conventional affinity binding assays using a suite of aptamers for the small molecule target ochratoxin A (OTA). Our results highlight inconsistency between conventional affinity assays and the need for multiple characterization strategies. To mitigate some of the challenges revealed in our head-to-head comparison of aptamer binding assays, we further developed and evaluated a set of novel strategies that facilitate efficient screening and characterization of aptamers in solution. Finally, we provide a workflow that permits rapid and robust screening, characterization, and functional verification of aptamers thus improving their development and integration into novel applications.
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 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".