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1.
15th International Conference on Developments in eSystems Engineering, DeSE 2023 ; 2023-January:398-403, 2023.
Article in English | Scopus | ID: covidwho-2327017

ABSTRACT

COVID-19 is a novel coronavirus first emerging in Wuhan, China in December 2019 and has since spread rapidly across the globe escalating into a worldwide pandemic causing millions of fatalities. Emergency response to the pandemic included social distancing and isolation measures as well as the escalation of vaccination programmes. The most popular COVID-19 vaccines are nucleic acid-based. The vast spread and struggles in containment of the virus has allowed a gap in the market to emerge for counterfeit vaccines. This study investigates the use of handheld Raman spectroscopy as a method for nucleic acid-based vaccine authentication and utilises machine learning analytics to assess the efficacy of the method. Conventional Raman spectroscopy requires a large workspace, is cumbersome and energy consuming, and handheld Raman systems show limitations with regards to sensitivity and sample detection. Surface Enhanced Raman spectroscopy (SERS) however, shows potential as an authentication technique for vaccines, allowing identification of characteristic nucleic acid bands in spectra. SERS showed strong identification potential through Correlation in Wavelength Space (CWS) with all vaccine samples obtaining an r value of approximately 1 when plotted against themselves. Variance was observed between some excipients and a selected number of DNA-based vaccines, possibly attributed to the stability of the SERS colloid where the colloid-vaccine complex had been measured over different time intervals. Further development of the technique would include optimisation of the SERS method, stability studies and more comprehensive analysis and interpretation of a greater sample size. © 2023 IEEE.

2.
17th International Conference on Intelligent Computing, ICIC 2021 ; 12838 LNAI:132-147, 2021.
Article in English | Scopus | ID: covidwho-1391784

ABSTRACT

The development of a wearable-based system for detecting difficulties in the daily lives of people with dementia would be highly useful in the day-to-day management of the disease. To develop such a system, it would be necessary to identify physiological indicators of the difficulties, which can be identified by analyzing physiological datasets from people with dementia. However, there is no such data available to researchers. As such, it is vital that data is collected and made available in future. In this paper we perform a review of past physiological data collection experiments conducted with people with dementia and evaluate the methods used at each stage of the experiment. Consideration is also given to the impacts and limitations imposed by the COVID-19 pandemic and lockdowns both on the people with dementia- such people being one of the most at risk and affected groups- and on the efficacy and safety of each of the methods. It is concluded that the choice of method to be utilized in future data collection experiments is heavily dependent on the type and severity of the dementia the participants are experiencing, and that the choice of remote or COVID-secure methods should be used during the COVID-19 pandemic;many of the methods reviewed could allow for the spread of the virus if utilized during a pandemic. © 2021, Springer Nature Switzerland AG.

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