(<i>Invited</i>) Plasmonic Detection of Exosomes for Early Diagnosis
Bibliographic record
Abstract
Exosomes is a class of extracellular (EV) vesicles (Fig1) which are unique nano-sized cargo-bearing biological vesicles, secreted by almost all normal and cancer cells into the extracellular space. These are the smallest of extracellular vesicles in the range of 30-150 nm present in all body fluids, making it suitable for liquid biopsy. This presentation will cover introduction to exosomes and development of different sensing platforms for the detection using nanoparticle integrated plasmonic platforms along with performance comparison. The presentation will also cover different methods of fabricating nano integrated microfluidic chips along with comparison of various optical sensing methods. The platforms include nanometal-polymer composite films integrated with inorganic nanoparticles dispersed into a polymer matrix. Nanoparticles such as gold and silver are used for their strong Localized Surface Plasmon Resonance in visible spectrum, that originates from the excitation of plasmons by the incident light. This property makes noble metal-polymer nanocomposites particularly adequate for sensing and biosensing applications. Furthermore, association of Au and Ag nanoparticles of various shapes with polydimethyl siloxane (PDMS), allows the use of nanocomposite materials for microfluidic based biosensing as well. In this presentation, we will talk about the in-situ synthesis of nano-PDMS nanocomposites both at the macroscale and inside the channel of a microfluidic chip. The nanocomposite has been successfully used for sensing of different biological entities including exosomes. Detection of breast cancer using exosomes in Lab on Chips is demonstrated in this talk. The talk also includes micromixing integrated lab on chips developed for detection and isolation of exosomes. M. Packirisamy, is a Professor, Gina Cody Research and Innovation Fellow, and Concordia Research Chair. He studies nano integrated microsystems for cancer diagnosis, green energy harvesting, Lab on Chip, direct sound printing and micro-nano integration. He is the recipient of Robert W Angus Medal and I.W.Smith award from Canadian Society of Mechanical Engineering, Gino Cody Research and Innovation Fellow, Distinguished Researcher of the University Award, Gina Cody Distinguished Excellence Researcher, Research Communicator of the Year, Member Royal Society of Canada College, Fellows of National Academy of Inventors (US), Royal Society of Chemistry (UK), Royal Society of Canada, Indian National Academy of Engineering, Engineering Institute of Canada, Canadian Academy of Engineering, American Society of Mechanical Engineers, Institution of Engineers India, Canadian Society for Mechanical Engineering and from Canadian Society for Mechanical Engineering, Concordia University Research Fellow, Petro Canada Young Innovator Award, ENCS Young Research Achievement Award, Distinguished Alumnus of NITT and Distinguished Research Fellow of University. He has 550 articles published, 52 invited talks, 32 inventions, $17Million grants and 185 graduates and PDFs supervised, one book and six book chapters. His invention on energy harvesting from blue green algae and Direct Sound Printing had more than 400 citations around the world. Figure 1
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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) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.136 | 0.049 |
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".