Development of Nanomaterial-Modified Electrochemical Sensors for the Analysis of Nucleic Acids
Bibliographic record
Abstract
Research in the quest for advanced and innovative diagnostic tools has driven the healthcare and pharmaceutical industry to explore new frontiers in biosensing technologies, with electrochemical sensors showing great promise in detecting biomarkers for DNA damage and oxidative stress. This thesis focuses on the detection of Guanine (G), Inosine (INO), and 8-hydroxyguanine (8-OH-G), biomolecules relevant to understanding the onset and progression of neurodegenerative diseases like Alzheimer’s. The working electrode surface has been modified in various ways, including the creation of multi-walled carbon nanotube iron oxide nanoparticles (MWCNT-Fe3O4 NPs) and glassy carbon electrode 4-thiophenol-gold nanoparticles (GCE-Ph-S-AuNPs), to develop an electrochemical platform for decentralized point-of-care testing. These modifications, along with nanotechnology, enable simultaneous detection of G, INO, and 8-OH-G with high accuracy. This thesis demonstrates the potential of microfabrication for developing simple point-of-care devices that allow timely and accurate diagnoses of neurodegenerative diseases, leading to improved patient outcomes and quality of life.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".