FUNCTIONAL NUCLEIC ACIDS AS KEY COMPONENTS IN BIOSENSORS
Bibliographic record
Abstract
The functionality of nucleic acids beyond genetics has attracted more attention over the past decades. Functional nucleic acids (FNA), including aptamers and nucleic acid-based enzymes, are well-known for their target binding and reaction catalysis abilities. FNA can be obtained through a technology called in vitro selection, which allows the isolation of customized FNA for various applications. In particular, FNA have received much interest in biosensing application. Their wide range of sensing targets, intrinsic stability, and high specificity have qualified them as the molecular recognition element in biosensors. This thesis explored the utilization of FNA to tackle real-world biosensing challenges, especially for pathogenic bacteria detection. The first project aimed to make the most use of in vitro selection to derive FNA that can meet the requirements of terminal applications. A few feasible approaches were proposed based on lessons from Mother Nature and validated by innovative scientist pioneers. In the second research project, I characterized an RNA-cleaving DNAzyme for Clostridium difficile infection diagnosis. This DNAzyme displayed high sensitivity and specificity for clinical C. difficile strains, making it a competitive candidate for a potential point-of-care diagnostic tool. In the next chapter, I incorporated a Legionella pneumophila-responsive RNA-cleaving DNAzyme into a bead-based assay for practical on-site detection. This assay exhibited a high stability and functionality in the cooling tower water samples, the real-world application environment. The following chapter was to optimize this assay further with a coupled rolling circle amplification strategy. This additional amplification speeded up the detection process, improved the limit of detection, and enabled the colorimetric results that are observable to the naked eye. These research aimed to advance the practical applications of FNA as key components in biosensors. I hope readers find this thesis insightful and inspirational for the development of the FNA field.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.004 |
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".