Enhanced Diagnosis of Breast Cancer through Detection of Exosomes using ex-situ, in-situ, and Solid State Dewetted Nanoplasmonic Platforms
Bibliographic record
Abstract
Breast cancer is one of the leading causes of death and the second most common among women in Canada. This thesis aims to develop sensing nanoplasmonic platforms for the early diagnosis of breast cancer through the detection of exosomes. The adsorption of bio or organic molecules on metallic nanoparticles leads to a plasmon band shift towards longer wavelengths; a phenomenon central to Localized Surface Plasmon Resonance (LSPR). This study aims to develop nano-structured arrays of plasmonic platforms for the high-performance detection of sub-micron biological entities such as viruses, exosomes, and other extracellular vesicles (EVs) ranging from 30 - 1000 nm. This thesis particularly focuses on developing and optimizing unique plasmonic platforms for detecting EVs/exosomes derived from breast cancer cell lines. These EVs/exosomes are nano-sized (30 - 100 nm), cargo-bearing vesicles secreted by almost all cell types, consisting of a lipid bilayer encasing a semi-fluid core of cytoplasmic materials, including proteins, nucleic acids, and exhibit significant heterogeneity, reflecting their cellular origin. Despite their potential as biomarkers for diseases like cancer, their heterogeneity and overlapping physicochemical properties with other vesicles pose challenges for effective detection, separation, and purification. In the beginning, two different types of platforms, one fabricated by an ex-situ method and the other by an in-situ method, were investigated to find out which was more convenient in terms of sensitivity for detecting EVs/exosomes. Different fabrication conditions have been explored and the sensitivity of the platforms has been measured. A sensing protocol involving several steps has been developed and optimized for detecting EVs/exosomes. It has been found that the platform prepared by an ex-situ method, that is, by convective assembly of gold colloidal particles, is more sensitive than the silver (Ag)-PDMS and gold (Au)-PDMS nanocomposite platforms fabricated by an in-situ method. In the case of gold (Au)-PDMS, gold is segregated under the surface of the polymer, without direct contact with the environment, and thus not suitable for detection. Additionally, to improve the refractive index sensitivity, novel platforms have been investigated. This thesis is further dedicated to developing and characterizing new plasmonic platforms, based on nano-islands/particles, fabricated from thin gold films e-beam deposited on an adhesion layer. The nano-islands/particles fabricated by the dewetting of the uniform gold films deposited on Chromium (Cr) (Cr-Au) and Indium Tin Oxide (ITO) adhesion layers (ITO-Au), respectively. Subsequently, based on the characterization results, the Cr-Au platform has been identified for the detection of EVs/exosomes. In addition, a direct detection method as well was developed without any surface chemistry modification of the plasmonic platform. The detection results obtained by using this novel platform and method were compared with the results of the ex-situ, and in-situ platforms studied earlier in this thesis work. It was found that, with the novel platform and method, the sensitivity of detection improved around 1.5 times, and the time taken for the detection was reduced by around one-fourth of the time of the earlier method. This work also provided invaluable information, regarding the adhesion layers, and their characteristics, especially stability, in association with very thin gold films. The phenomena of interdiffusion and oxidation during the deposition and heat treatment and their effect on detection have been studied as well. Overall, this work contributes to the development of cost-effective, high-performance nanoplasmonic platforms, for early diagnosis of breast cancer establishing a foundation for their broader application in detecting and analyzing diverse biological and environmental samples.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".