A paper-based low-cost label-free biosensing silver core-gold shell nanostructure for SERS to be applied to breast cancer diagnostics

• Autor(a) principal: Rijo, Catarina Filipa Braz
• Data de Publicação: 2019
• Tipo de documento: Dissertação
• Idioma: eng
• Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
• Texto Completo: http://hdl.handle.net/10362/91955
• topic: SILAR fabrication; bimetallic Ag@AuNPs; SERS; cellulose fibbers; PCA; exosomes; Domínio/Área Científica::Engenharia e Tecnologia::Nanotecnologia

Breast cancer (BCa) is one of the most common and deadly diseases in women worldwide. In 2018, 2.1 million cases were diagnosed with BCa from which 626,679 women died. Therefore, an early diagnosis is imperative for treatment and a cure success rate. Recent studies have concluded that exosomes can be used as biomarkers, since they participate in the communication between cells, carrying genetic information from the mother cells. Common methods of detection of exosomes as Enzyme-Linked Immunosorbent Assays (ELISA) are used, however, large amounts of highly concentrated sample, special preparation and labelling processes are required. As an alternative, Raman Spectroscopy stands out as a low-cost, simple and fast detection method that leads to a less invasive real sample collection and sample preparation. Low sample volumes are needed due to surface enhancement signal (SERS). However commercial SERS substrate for these measures present high cost and low shelf-life. In this work, Ag-core-Au-shell bimetallic nanoparticles were directly synthesized on paper substrates (Whatman and Office paper) through two-stage successive ionic layer absorption and reaction (SILAR) techniques and tested as SERS substrates. Enhancement factors (EF) from 104 to 105 were reached and, for both substrates the Limit of Detection (LOD) was calculated as 10-11 M R6G. Non-tumoral (MCF-10A) and tumoral (MDA-MB-231) exosomes from breast cells were tested on the optimized substrates and Raman spectra were analysed by a statistical method called PCA (Principal Component Analysis). The data was successfully grouped with 95% confidence confirming its potential as a low-cost, label-free point-of-care test chip for the early diagnosis of breast cancer diseases.