Modelling Bacterial Growth for Applying Photodynamic Therapy with Indocyanine Green

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dc.contributor Aalto-yliopisto fi
dc.contributor Aalto University en
dc.contributor.advisor Alander, Jarmo
dc.contributor.author Nguyen, Minh
dc.date.accessioned 2018-04-03T13:27:54Z
dc.date.available 2018-04-03T13:27:54Z
dc.date.issued 2018-03-26
dc.identifier.uri https://aaltodoc.aalto.fi/handle/123456789/30548
dc.description.abstract Antimicrobial approaches using photodynamic therapy (PDT) have become popular in medication. However, to the best of our knowledge, no model has been developed for estimating the dose of light and photosensitiser with respect to bacterial inhibition. This thesis aims to model the growth of Escherichia coli which can be utilised in developing the aforementioned model when using PDT with near-infrared (NIR) light and indocyanine green (ICG). The project applied a spectroscopic method to measure the spectra of bacteria and chemometric methods to analyse the spectral data. The project consists of two main phases. The first phase conducted two phantoms to develop a measurement system and identify the possibility of utilising the system in controlled conditions. The first phantom analysed the spectra of LED lights. The second phantom determined the concentrations of different colour liquids. In the second phase, bacterial suspension was subjected to spectral analysis using the developed system. As a result of the whole project, a model has been established in order to monitor and estimate the concentrations of bacteria in liquid samples. en
dc.format.extent 56+10
dc.format.mimetype application/pdf en
dc.language.iso en en
dc.title Modelling Bacterial Growth for Applying Photodynamic Therapy with Indocyanine Green en
dc.type G2 Pro gradu, diplomityö fi
dc.contributor.school Sähkötekniikan korkeakoulu fi
dc.subject.keyword modelling en
dc.subject.keyword bacteria en
dc.subject.keyword principal component analysis en
dc.subject.keyword partial least squared regression en
dc.identifier.urn URN:NBN:fi:aalto-201804032012
dc.programme.major Control, Robotics and Autonomous Systems fi
dc.programme.mcode ELEC3025 fi
dc.type.ontasot Master's thesis en
dc.type.ontasot Diplomityö fi
dc.contributor.supervisor Zenger, Kai
dc.programme AEE - Master’s Programme in Automation and Electrical Engineering (TS2013) fi
dc.ethesisid Aalto 9803
dc.location P1 fi


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