Ideal‐Filter Capillary Electrophoresis (IFCE) Facilitates the One‐Step Selection of Aptamers
Bibliographic record
Abstract
Abstract Selection of aptamers from oligonucleotide libraries currently requires multiple rounds of alternating steps of partitioning of binders from nonbinders and enzymatic amplification of all collected oligonucleotides. Herein, we report a highly practical solution for reliable one‐step selection of aptamers. We introduce partitioning by ideal‐filter capillary electrophoresis (IFCE) in which binders and nonbinders move in the opposite directions. The efficiency of IFCE‐based partitioning reaches 109, which is ten million times higher than that of typical solid‐phase partitioning methods. One step of IFCE‐based partitioning is sufficient for the selection of a high‐affinity aptamer pool for a protein target. Partitioning by IFCE promises to become an indispensable tool for fast and robust selection of binders from different types of oligonucleotide libraries.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".