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
Aptamers are typically selected from libraries of random DNA (or RNA) sequences by SELEX, which involves multiple rounds of alternating steps of partitioning and PCR amplification. Here we report, for the first time, non-SELEX selection of aptamers-a process that involves repetitive steps of partitioning with no amplification between them. A highly efficient affinity method, non-equilibrium capillary electrophoresis of equilibrium mixtures (NECEEM), was used for partitioning. We found that three steps of NECEEM-based partitioning in the non-SELEX approach were sufficient to improve the affinity of a DNA library to a target protein by more than 4 orders of magnitude. The resulting affinity was higher than that of the enriched library obtained in three rounds of NECEEM-based SELEX. Remarkably, NECEEM-based non-SELEX selection took only 1 h in contrast to several days or several weeks required for a typical SELEX procedure by conventional partitioning methods. In addition, NECEEM-based non-SELEX allowed us to accurately measure the abundance of aptamers in the library. Not only does this work introduce an extremely fast and economical method for aptamer selection, but it also suggests that aptamers may be much more abundant than they are thought to be. Finally, this work opens the opportunity for selection of drug candidates from libraries of small molecules, which cannot be PCR-amplified and thus are not approachable by SELEX.
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