In Vitro Selection in Serum: RNA-Cleaving DNAzymes for Measuring Ca<sup>2+</sup> and Mg<sup>2+</sup>
Bibliographic record
Abstract
RNA-cleaving DNAzymes have been attempted as in vivo analytical probes and gene silencing reagents over the past two decades. Despite progress already achieved, concerns still exist regarding the activity of DNAzymes in biological fluids. An example is the low activity of the 10–23 DNAzyme in intracellular Mg 2+ concentrations. To obtain DNAzymes that work optimally in biological samples, we herein report the first DNAzyme in vitro selection in undiluted human blood serum. The selection starts with a large DNA library containing 50 random nucleotides, and sequences that can be cleaved in serum were isolated and amplified. After deep sequencing analysis, 80% of the final library are a variant of the 8–17 DNAzyme (named 17EV1). The main difference between 17E and 17EV1 is a single mutation at the N 12 position of the catalytic core. 17EV1 is ∼6-fold faster in serum than 17E, since 17EV1 is preferentially activated by Ca 2+ and serum is rich in Ca 2+ over Mg 2+ . On the other hand, 17E has a similar activity with Ca 2+ or Mg 2+ . With this observation, a method for measuring the Ca 2+ /Mg 2+ ratio was developed by combining the 17E and 17EV1 DNAzymes. This study demonstrates the feasibility of selecting DNAzymes in biological fluids and will facilitate the application of DNAzymes in bioanalytical chemistry and gene therapy.
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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 | 0.000 | 0.001 |
| 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".