Universal Strategy To Engineer Catalytic DNA Hairpin Assemblies for Protein Analysis
Bibliographic record
Abstract
Nucleic acids can be programmed into enzyme-free catalytic DNA circuits (CDCs) to carry out various functions ranging from DNA computing to signal amplifications for biosensing. Catalytic hairpin assembly (CHA), the accelerated hybridization between two DNA hairpins catalyzed by a DNA input, is one of the most widely studied and used CDCs for amplified detection of nucleic acids and small molecules. So far, it is still challenging to expand CHAs to proteins largely due to the lack of a universal strategy to construct protein-responsive CHAs. To address this challenge, we demonstrate that a rationally designed protein-DNA binding complex can be used as an effective catalyst to accelerate CHA reactions. On the basis of this principle, we developed specific CHAs for a number of important protein biomarkers, including human α-thrombin, human prostate specific antigen, and human epidermal growth factor receptor 2. Upon establishing this panel of protein-responsive CHAs, we further explore their potential applications to the detection of specific protein biomarkers from human serum samples and cancer cells.
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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 | 0.000 |
| 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 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".