DNAzyme Hybridization, Cleavage, Degradation, and Sensing in Undiluted Human Blood Serum
Bibliographic record
Abstract
RNA-cleaving DNAzymes provide a unique platform for developing biosensors. However, a majority of the work has been performed in clean buffer solutions, while the activity of some important DNAzymes in biological sample matrices is still under debate. Two RNA-cleaving DNAzymes (17E and 10-23) are the most widely used. In this work, we carefully studied a few key aspects of the 17E DNAzyme in human blood serum, including hybridization, cleavage activity, and degradation kinetics. Since direct fluorescence monitoring is difficult due to the opacity of serum, denaturing and nondenaturing gel electrophoresis were combined for studying the interaction between serum proteins and DNAzymes. The 17E DNAzyme retains its activity in 90% human blood serum with a cleavage rate of 0.04 min(-1), which is similar to that in the PBS buffer (0.06 min(-1)) with a similar ionic strength. The activity in serum can be accelerated to 0.3 min(-1) with an additional 10 mM Ca(2+). As compared to 17E, the 10-23 DNAzyme produces negligible cleavage in serum. Degradation of both the substrate and the DNAzyme strand is very slow in serum, especially at room temperature. Degradation occurs mainly at the fluorophore label (linked to DNA via an amide bond) instead of the DNA phosphodiester bonds. Serum proteins can bind more tightly to the 17E DNAzyme complex than to the single-stranded substrate or enzyme. The 17E DNAzyme hybridizes extremely fast in serum. With this understanding, the detection of DNA using the 17E DNAzyme is demonstrated in serum.
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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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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 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".