Enhancing the Sensitivity of DNA and Aptamer Probes in the Dextran/PEG Aqueous Two-Phase System
Bibliographic record
Abstract
Increasing the local concentration of DNA-based probes is a convenient way to improve the sensitivity of biosensors. Instead of using organic solvents or ionic liquids that phase-separate with water based on hydrophobic interactions, we herein studied a classic aqueous two-phase system (ATPS) comprising polyethylene glycol (PEG) and dextran. Polymers of higher molecular weights and higher concentrations favored phase separation. DNA oligonucleotides are selectively enriched in the dextran-rich phase unless the pH was increased to 12. A higher volume ratio of PEG-to-dextran and a higher concentration of PEG also enrich more DNA probes in the dextran-rich phase. The partition efficiency of the T 15 DNA was enriched around seven times in the dextran phase when the volume ratio of dextran and PEG reached 1:10. The detection of limit improved by 3.6-fold in a molecular beacon-based DNA detection system with the ATPS. The ATPS also increased the sensitivity for the detection of Hg 2+ and adenosine triphosphate, although these target molecules alone distributed equally in the two phases. This work demonstrates a simple method using water soluble polymers to improve biosensors.
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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) | 0.001 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 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".