Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 it