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Sensible label-free bio-sensing: Towards in situ biopsy
Linköping University, Department of Clinical and Experimental Medicine, Division of Cell Biology. Linköping University, Faculty of Medicine and Health Sciences. Univ Cambridge, England; Univ Cambridge, England.
2018 (English)In: 2018 IEEE SENSORS, IEEE , 2018, p. 1721-1724Conference paper, Published paper (Refereed)
Abstract [en]

Integrative in situ bio-sensing approaches became ominous to advance early stage detection of complex and heterogeneous disease like cancer, chronic lung, inflammation, coronary syndromes etc. Particularly in cancer, we can save million lives with existing therapeutic strategies if we can detect them at their very early stage. Unfortunately, cancer offers very rare distinctly identifiable cues during its onset to identify them and posing the technological challenges to spot them at their very early stage. We have developed an aptamer based integrated switchable rare event sensing platform using variable angle spectroscopic ellipsometry to spot in situ, readout and monitor rare events in real-time in complex heterogeneous cell population. The developed aptasensors is being tested with spotting prostate cancer cell PC3 in a blood mimicking population of human peripheral blood mononuclear cells (PBMC). The proof-of-concept data showed that the developed platform can detect in situ up to 10 target cells/ml with 10 background cells. The integrated sensor is designed with switchable surface to safely capture and release the target cells for further clinical validation and analysis for advanced diagnosis. We envision this alternate integrated bio-sensors strategy will be an important step towards the development of label-free in situ biopsy technique for rare event detection and early stage detection of cancer.

Place, publisher, year, edition, pages
IEEE , 2018. p. 1721-1724
Series
IEEE Sensors, ISSN 1930-0395
Keywords [en]
Label-free; Whole cell Biosensor; Aptasensor; on/off switchable surface; early stage detection; Ellipsometry
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:liu:diva-158601DOI: 10.1109/ICSENS.2018.8589723ISI: 000468199300443ISBN: 978-1-5386-4707-3 (electronic)OAI: oai:DiVA.org:liu-158601DiVA, id: diva2:1334837
Conference
17th IEEE SENSORS Conference
Available from: 2019-07-03 Created: 2019-07-03 Last updated: 2019-08-16

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Total: 11 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • oxford
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf