Comprehensive Analytical Comparison of Strategies Used for Small Molecule Aptamer Evaluation
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 it