A Sol–Gel-Based Microfluidics System Enhances the Efficiency of RNA Aptamer Selection
Bibliographic record
Abstract
RNA and DNA aptamers that bind to target molecules with high specificity and affinity have been a focus of diagnostics and therapeutic research. These aptamers are obtained by SELEX often requiring many rounds of selection and amplification. Recently, we have shown the efficient binding and elution of RNA aptamers against target proteins using a microfluidic chip that incorporates 5 sol-gel binding droplets within which specific target proteins are imbedded. Here, we demonstrate that our microfluidic chip in a SELEX experiment greatly improved selection efficiency of RNA aptamers to TATA-binding protein, reducing the number of selection cycles needed to produce high affinity aptamers by about 80%. Many aptamers were identical or homologous to those isolated previously by conventional filter-binding SELEX. The microfluidic chip SELEX is readily scalable using a sol-gel microarray-based target multiplexing. Additionally, we show that sol-gel embedded protein arrays can be used as a high-throughput assay for quantifying binding affinities of aptamers.
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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 | 0.000 |
| 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) | 0.001 | 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".