A Label-Free, Mix-and-Detect ssDNA-Binding Assay Based on Cationic Conjugated Polymers
Bibliographic record
Abstract
The accurate, simple, and efficient measurement of the concentration of single-stranded DNA (ssDNA) is important for many analytical applications, such as DNA adsorption, biosensor design, and disease diagnosis, but it is still a challenge. Herein, we studied a cationic conjugated polymer (CCP)-based ssDNA assay taking advantage of the obvious fluorescence change of CCPs upon binding ssDNA. Poly(3-(3′-N,N,N-triethylamino-1′-propyloxy)-4-methyl-2,5-thiophene hydrochloride) (PMNT) achieved an apparent dissociation constant (Kd) of 57 ± 4 nM for ssDNA, indicating a very high binding affinity between PMNT and ssDNA. This allowed us to develop a CCP-based ssDNA biosensor with a detection limit of 0.6 nM, similar to the fluorescence-dye-based method using SYBR Green I and SYBR Gold. Our CCP-based biosensor produced smaller differences among ssDNA samples with different base compositions. In addition, the existence of double-stranded DNA (dsDNA) at different concentrations did not interfere with the fluorescence of PMNT, indicating that our CCP-based biosensor was more suitable for the measurement of ssDNA. Compared with fluorescence-intensity-based quantification, our CCP system allowed ratiometric quantification, which made the calibration easier and more robust. We then applied our method to the quantification of ssDNA on AuNPs using both unmodified and thiolated ssDNA, and the accurate quantification of ssDNA was achieved without any fluorophore modification. This method provides an alternative approach for the measurement of ssDNA.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".