Development of Electrochemical Sensors for the Analysis of Therapeutic Compounds and Proteases related to Alzheimer's disease
Bibliographic record
Abstract
The development of biosensors has advanced significantly in the past few decades. IUPAC has given the definition of biosensors as a device that employs specific biochemical reactions mediated by enzymes, immunosystems, tissues, organelles or whole cells for the detection of a chemical analyte by electrical, thermal or optical signals. To date, one of the most successful endeavor being the commercialization of the well-known glucose biosensor, now used by many around the world. In 1956, Clark proposed the first oxygen sensor, with the ability to determine oxygen content in a sample independent of the sample composition. In this thesis, we will review the fundamental concepts in electrochemical biosensors with respect to its detection techniques and applications of carbon nanomaterials to enhance signal detection (Chapter 1) in relation Alzheimer's disease (AD) (Chapter 2). Next, we will demonstrate the use of carbon nanotube modified screen-printed electrodes for the detection of disease markers such as homocysteine (Chapter 3). Another prominent disease marker, the Amyloid-β peptide (Aβ), has been implicated in Alzheimer's disease (AD). Under the Amyloid Cascade Hypothesis, the onset and progression of AD has been connected with the formation of toxic Aβ aggregates. However, numerous factors affect this aggregation process and the next few chapters will serve to explore factors that have been shown to increase Aβ aggregation (Chapter iii 4) and potential natural therapeutic compounds that may slow down the progression of Aβ aggregation (Chapter 5 and 6). To showcase the applicability of electrochemical biosensors for the detection of an array of biological processes, we further applied the screen-printed electrodes for the detection of an apoptosis biomarker linked to AD (caspase-3), using a label-free detection platform (Chapter 7) and with assistance from a well-known label, p-nitroaniline, which has a dual function as a fluorescence tag and an electrochemical probe (Chapter 8). The applications presented in this thesis will highlight the utility of electrochemical platforms as a powerful analytical tools, capable of fast detection at a lower cost with future miniaturization and high-throughput analysis capabilities.
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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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".