Chimeric RNA–DNA Molecular Beacons for Quantification of Nucleic Acids, Single Nucleotide Polymophisms, and Nucleic Acid Damage
Bibliographic record
Abstract
Single nucleotide polymorphisms (SNPs) are the main cause for variations in the human genome. DNA lesions, such as cyclobutane pyrimidine dimers (CPDs), [6-4] pyrimidine-pyrimidinones, dewar pyrimidinones, and photohydrates, can subsequently lead to mutagenesis, carcinogenesis, and cell death. Much effort has focused on methods for detecting DNA, SNPs, or damaged nucleic acids. However, almost all of the proposed methods consist of multistep procedures, are limited to specific types of damage, some of these methods require expensive instruments, and some suffer from a high level of interferences. In this paper, we present a novel, simple, mix-and-read assay for the detection of nucleic acids that is general for all types of SNPs and nucleic acid damage. This method uses a chimeric RNA-DNA molecular beacon (chMB). The calibration curve of the chMB for detecting single base mismatch and ultraviolet (UV)-induced DNA damage shows good linearity (R(2) = 0.981 and 0.996, respectively) and limits of detection of 10.4 ± 2.2 and 8.64 ± 1.2 nM, respectively. The chimeric RNA-DNA MB proves to be a more sensitive and selective tool for the quantification of nucleic acids, DNA mismatches, and UV-induced DNA damage than DNA MBs.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".