Development of an electrochemical surface-enhanced Raman spectroscopy DNA aptamer biosensor for rapid detection of protein biomarkers of disease
Bibliographic record
Abstract
Development of an electrochemical surface-enhanced Raman spectroscopy DNA aptamer biosensor for rapid detection of protein biomarkers of disease By Scott Gerald Harroun Electrochemical surface-enhanced Raman spectroscopy (EC-SERS) is a vibrational spectroscopic method involving detection of a target molecule at a metal nanostructured surface to which a potential has been applied.EC-SERS can lower limits of detection, even relative to SERS, which is itself an improvement over conventional Raman spectroscopy.DNA aptamers are oligonucleotides engineered to bind a target molecule with high specificity and high affinity.Advantages of using aptamers instead of antibodies to bind a target molecule include time and temperature stability, in vitro synthesis, ease of handling and storage, and low cost.Thiolated DNA aptamers are immobilised onto Ag nanoparticles (AgNP) by the formation of a Ag-S bond.The aptamer-modified AgNP electrodes are then used for detection of target proteins: immunoglobulin E (IgE), cytochrome c (cyt c) and catalase-peroxidase (KatG), which are all biomarkers indicative of various health conditions.IgE is produced as a response to various allergens.Cyt c is a biomarker for diseases of the liver and kidney, and KatG is a biomarker for tuberculosis.This project uses EC-SERS of aptamer-modified AgNP electrodes for detection of these biomarker proteins.The overall goal is the eventual development of a low-cost and portable point of care diagnostic device that can be used in developing nation settings, such as South Africa.Frasier for being on my supervisory committee, and Dr. Jan Rainey for being the external examiner.In addition, I would like to acknowledge the past and present Brosseau research group members for their encouragement, friendship and any contributions to my research:
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| 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 | 0.001 | 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".