<i>In‐vitro</i> Clinical Diagnostics using RNA‐Cleaving DNAzymes
Bibliographic record
Abstract
Over the last three decades, significant advancements have been made in the development of biosensors and bioassays that use RNA-cleaving DNAzymes (RCDs) as molecular recognition elements. While early examples of RCDs were primarily responsive to metal ions, the past decade has seen numerous RCDs reported for more clinically relevant targets such as bacteria, cancer cells, small metabolites, and protein biomarkers. Over the past 5 years several RCD-based biosensors have also been evaluated using either spiked biological matrixes or patient samples, including blood, serum, saliva, nasal mucus, sputum, urine, and faeces, which is a critical step toward regulatory approval and commercialization of such sensors. In this review, an overview of the methods used to generate RCDs and the properties of key RCDs that have been utilized for in vitro testing is first provided. Examples of RCD-based assays and sensors that have been used to test either spiked biological samples or patient samples are then presented, highlighting assay performance in different biological matrixes. A summary of current prospects and challenges for development of in vitro diagnostic tests incorporating RCDs and an overview of future directions of the field is also provided.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.005 |
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".