Search results for: Keshavan Varadarajan

Authors: Masni Mat Yusoff, Michael H. Gordon, Keshavan Niranjan

Abstract:

The study reports on the effect of aqueous enzymatic extraction (AEE) parameters on the Moringa oleifera (MO) oil yield and the formation of emulsion at the end of the process. A mixture of protease and cellulase enzymes was used at 3:1 (w/w) ratio. The highest oil yield of 19% (g oil/g sample) was recovered with the use of a mixture of pH 6, 1:4 material/moisture ratio, and incubation temperature, time, and shaking speed of 50 ⁰C, 12.5 hr, and 300 stroke/min, respectively. The use of pH 6 and 8 resulted in grain emulsions, while solid-intact emulsion was observed at pH 4. Upon fixing certain parameters, higher oil yield was extracted with the use of lower material/moisture ratio and higher shaking speed. Longer incubation time of 24 hr resulted in significantly (p < 0.05) similar oil yield with that of 12.5 hr, and an incubation temperature of 50 ⁰C resulted in significantly (p < 0.05) higher oil yield than that of 60 ⁰C. In overall, each AEE parameter showed significant effects on both the MO oil yields and the emulsions formed. One of the major disadvantages of an AEE process is the formation of emulsions which require further de-emulsification step for higher oil recovery. Therefore, critical studies on the effect of each AEE parameter may assist in minimizing the amount of emulsions formed whilst extracting highest total MO oil yield possible.

Keywords: enzyme, emulsion, Moringa oleifera, oil yield

Procedia PDF Downloads 397

Authors: Edward Ridyard, Vinary Varadarajan

Abstract:

Background and Aims: The first year as a doctor in any country represents a major career transition in any physician's life. During this period, many physicians concentrate on obtaining clinical skills but may not obtain the important skills necessary to cope with stress. In this study we elucidate stress levels amongst FY1 doctors regarding the transitioning into specialty career choices, working in the NHS and anxiety about future career success. Methods: A prospective single blinded analysis of Foundation Year one (FY1) trainees using a non-mandatory online questionnaire was distributed. No exclusion criteria were applied. The only inclusion criteria was the doctor was in a full-time FY1 post and this was their first job in the UK. A total of n= 22 doctors were included in the study. After data collection, statistical analysis using chi-squared testing was applied. Results: The large majority of FY1 doctors (72.7%) already knew what specialty they wished to pursue (p=0.0001). With regards to their future careers 45.5% of FY1 doctors stated "above average" stress levels. The majority of FY1 doctors (64.3%) stated their stress levels working in the NHS were either "above average" or "high". Finally, 81.8% of respondents know colleagues who have been put off from pursuing specialties due to the stress of competition. Conclusions: A large majority of FY1 doctors already know at this early stage what area they would like to specialise in. With this in mind, a large proportion have above "average" levels of stress with regards to securing this future career path. The most worrying finding is that 64.3% of FY1s stated they had "above average" or "high" stress levels working in the NHS. We therefore recommend formal stress management education to be incorporated into the foundation programme curriculum.

Keywords: stress, anxiety, junior doctor, education

Procedia PDF Downloads 345

Authors: Md. Abu Sufian, Robiqul Islam, Imam Hossain Shajid, Mahesh Hanumanthu, Jarasree Varadarajan, Md. Sipon Miah, Mingbo Niu

Abstract:

Coronary Heart Disease (CHD) remains a principal cause of global morbidity and mortality, characterized by atherosclerosis—the build-up of fatty deposits inside the arteries. The study introduces an innovative methodology that leverages cloud-based platforms like AWS Live Streaming and Artificial Intelligence (AI) to early detect and prevent CHD symptoms in web applications. By employing novel simulation processes and AI algorithms, this research aims to significantly mitigate the health and societal impacts of CHD. Methodology: This study introduces a novel simulation process alongside a multi-phased model development strategy. Initially, health-related data, including heart rate variability, blood pressure, lipid profiles, and ECG readings, were collected through user interactions with web-based applications as well as API Integration. The novel simulation process involved creating synthetic datasets that mimic early-stage CHD symptoms, allowing for the refinement and training of AI algorithms under controlled conditions without compromising patient privacy. AWS Live Streaming was utilized to capture real-time health data, which was then processed and analysed using advanced AI techniques. The novel aspect of our methodology lies in the simulation of CHD symptom progression, which provides a dynamic training environment for our AI models enhancing their predictive accuracy and robustness. Model Development: it developed a machine learning model trained on both real and simulated datasets. Incorporating a variety of algorithms including neural networks and ensemble learning model to identify early signs of CHD. The model's continuous learning mechanism allows it to evolve adapting to new data inputs and improving its predictive performance over time. Results and Findings: The deployment of our model yielded promising results. In the validation phase, it achieved an accuracy of 92% in predicting early CHD symptoms surpassing existing models. The precision and recall metrics stood at 89% and 91% respectively, indicating a high level of reliability in identifying at-risk individuals. These results underscore the effectiveness of combining live data streaming with AI in the early detection of CHD. Societal Implications: The implementation of cloud-based AI for CHD symptom detection represents a significant step forward in preventive healthcare. By facilitating early intervention, this approach has the potential to reduce the incidence of CHD-related complications, decrease healthcare costs, and improve patient outcomes. Moreover, the accessibility and scalability of cloud-based solutions democratize advanced health monitoring, making it available to a broader population. This study illustrates the transformative potential of integrating technology and healthcare, setting a new standard for the early detection and management of chronic diseases.

Keywords: coronary heart disease, cloud-based ai, machine learning, novel simulation techniques, early detection, preventive healthcare

Procedia PDF Downloads 18