(<i>Invited</i>) Plasmonic Detection of Exosomes for Early Diagnosis
Notice bibliographique
Résumé
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
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,002 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,136 | 0,049 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».