An Optically-Transparent Aptamer-Based Detection System for Colon Cancer Applications Using Gold Nanoparticles Electrodeposited on Indium Tin Oxide
Bibliographic record
Abstract
In this paper, a label-free aptamer based detection system (apta-DS) was investigated for detecting colon cancer cells. For this purpose, we employed an aptamer specific to colon cancer cells like HCT116 expressing carcinoembryonic antigen (CEA) on their surfaces. Capture aptamers were covalently immobilized on the surface of gold nanoparticles (GNPs) through self-assembly monolayer of 11-mercaptoundecanoic acid (11-MUA) activated with EDC (1-Ethyl-3-[3-dimethylaminopropyl]carbodiimide)/N-hydroxysuccinimide (NHS). The cyclic voltammetry (CV) and chronopotentiometry (CP) methods were used for electrodeposition of GNPs on the surface of indium tin oxide (ITO). In this work, the CV method was also used to demonstrate the conjugation of GNPs and aptamers and identify the cancer cell capturing events. Additionally, Field Emission Scanning Electron Microscopy (FE-SEM) confirmed the deposition of GNPs on ITO and the immobilization of aptamer on the apta-DS. The electrodeposited GNPs played the role of nanoprobes for cancer cell targeting without losing the optical transparency of the ITO substrate. A conventional optical microscope also verified the detection of captured cancer cells. Based on this study's results relying on electrochemical and optical microscopic methods, the proposed apta-DS is reliable and high sensitive with a LOD equal to 6 cell/mL for colon cancer detection.
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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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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".