Developing Platforms for Solid-Phase Bioaffinity Assays Using Nucleic Acid Aptamers
Bibliographic record
Abstract
This thesis focuses on devising strategies for immobilizing nucleic acid aptamers on or in solid supports that improve their stability, availability for binding interactions and compatibility with emerging solid-phase bioanalysis applications. The work begins with the entrapment of labile RNA-based aptamers within a sol-gel derived material for bio-immobilization without the need for extraneous binding molecules or complex conjugation procedures. A hybrid organic-inorganic material provided the best matrix environment not only for RNA functionality but protection from both nuclease attack and chemical degradation, enhancing long-term stability when compared to solution. To expand the application of aptamer/sol-gel biohybrids, rolling circle amplification was used to generate long DNA molecules containing concatemeric aptamer sequences. A high-throughput screen was used to identify materials with macropores that allowed for minimal leaching and high activity and of the entrapped concatemeric DNA aptamers. The optimal concatemer-entrapped material was used to make monolithic columns for flow-based detection of both small molecules and high molecular weight proteins, thus, broadening the range of analytes that can interact with sol-gel entrapped aptamers to include large macromolecules. To further expand the utility of concatemeric aptamers, these large molecules were inkjet-printed as “bioinks” on paper as an alternative means of producing paper-based sensing devices in a simple and inexpensive manner. The large structure of the concatemeric aptamer molecules allowed direct bio-immobilization through strong adsorption on cellulose without the need for surface conjugation or material entrapment. Although these concatemers remained immobilized after liquid elution over the printing area, they retained sufficient segmental motion for target binding and signaling on the paper surface. Patterning letters/symbols to create internally-referenced and multiplexed assays for both qualitative and quantitative detection of small molecules and proteins, demonstrated a generic platform for on-demand printing of aptamer-based solid-phase assays in the emerging field of paper-based sensors for on-site detection applications.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".