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.
ABSTRACT
Covid-19 pandemic created a global shift in the way how consumers purchase. Restrictions to movements of individuals and commodities created a big challenge on day today life. Due to isolation, social media usage has increased substantially, and these platforms created significant impact carrying news and sentiments instantaneously. These sentiments impacted the purchase behavior of consumers and online retailers witnessed variations in their sales. Retailers used various customer behavior prediction models such as Recommendation systems to influence consumers and increasing their sales. Due to Covid-19 pandemic, these models may not perform the same way due to changes in consumer behavior. By integrating consumer sentiments from online social media platform as another feature in the prediction machine learning models such as recommendation systems, retailers can understand consumer behavior better and create Recommendations appropriately. This provides the consumers with appropriate choice of products in essential and non-essential categories based on pandemic condition restrictions. This also helps retailers to plan their operations and inventory appropriately. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
ABSTRACT
INTRODUCTION: Communication with ventilated patients in the Intensive care unit (ICU) is challenging. This may lead to anxiety and frustration, potentially contributing to the development of delirium. Various technologies, such as eye-tracking devices, have been employed to facilitate communication with varying grades of success. The EyeControl-Med device is a novel technology that delivers audio content and allows patients to interact by eye movements and could potentially allow for better communication in this setting. METHOD(S): A single-arm pilot study of patients in a mixed ICU. Patients underwent at least 3 sessions with the EyeControl-Med device administered by a speech-language pathologist. Communication and consciousness were assessed using the Lowenstein communication scale (LCS) and delirium was assessed by a computerized version of the CAM-ICU during the first and last device usage sessions. RESULT(S): 15 patients were included, 40% of whom were diagnosed with COVID-19. All patients completed three to seven usage sessions. The mean LCS score improved by 19.3 points (p < 0.0001), with each of its five components showing significant improvements as well. The mean number of errors on the CAM-ICU tool decreased from 6.5 to 2.5 (p=0.0006), indicating lower rates of delirium. No adverse effects were observed. CONCLUSION(S): The EyeControl-Med device may help enhance communication and re-orientation in this patient population while reducing the helplessness and anxiety associated with lack of communication. It may reduce the manifestations and duration of delirium in ventilated critically ill patients. Controlled studies are required to establish this effect.