High throughput Detection of Molecular Targets in Cancer Using Nanoparticles: Application in Diagnostics
Bibliographic record
Abstract
In light of cancer as a multi-parameter disease, technological advancements are being developed to obtain automated and high throughput assessment of molecular targets in diagnostics, with the aim to reduce subjective analytical assessment of tumours and/or to integrate molecular data obtained from different tests on given tumour samples. Towards this goal, a multi-disciplinary approach is presented in this dissertation using nanotechnology. First, by combining quantum dot (QD) nanocrystals, tissue microarray, optical spectroscopy, and algorithm design, an automated and high throughput quantitative approach in the assessment of biomarker surrogates in neoplastic tissue is introduced. The validation study was performed using epidermal growth factor (EGFR) in 8 formalin-fixed paraffin-embedded (FFPE) xenografts of lung adenocarcinoma (r2=0.9) (chapter 2). Furthermore, in chapter 3 a novel design of QD-based fluorescent probes, nanobeads, was presented by encapsulation of QDs in polystyrene in an emulsion polymerization reaction. Nanobeads exhibited resistance to fluorescent quenchers such as solutions of extreme pH and colorimetric dyes of hematoxylin & eosin (H&E) and Giemsa, key standard dyes used in cancer diagnostic. Comparative studies with organic dye, phycoerythrin (PE), and QDs demonstrated significant (P<0.001) fluorescent quenching in H&E and Giemsa using Kruskal Wallis test. The design allowed a simultaneous consolidation of fluorescent immunoassaying, using nanobeads, and morphologic and parametric visualization of cellular features, using colorimetric stains, in a given biopsy. Sten-volmer analysis demonstrated a reduction of fluorescence resonance energy transfer among nanobead610nm and nanobead560nm. The application of the QD-based methods bears potential significance in cancer diagnosis and in tumor management, especially human core biopsies of limited quantity. In chapter 4, cellular endocytosis of gold particles was shown to be size dependence, with gold particles of 50 nm demonstrating the highest uptake. The cellular response subsequent to gold uptake was assessed using 10k cDNA microarrays. Significant analysis of microarray (SAM) showed the gene expression of treated and non-treated particles to be 99.65% similar. The remaining 35 genes however, demonstrated down regulation of apoptosis, cell proliferation and cell adhesion responses in treated cells. While promising, future research and development are required to adopt nanoparticles in diagnostics.
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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) | 0.000 | 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 | 0.000 |
| 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".