ABSTRACT
Corona VIrus Disease abbreviated as COVID-19 is a novel virus which is initially identified in Wuhan of China in December of 2019 and now this deadly disease has spread all over the world. According to World Health Organization (WHO), a total of 3,124,905 people died from 2019 to 2021, April. In this case, many methods, AI base techniques, and machine learning algorithms have been researched and are being used to save people from this pandemic. The SARS-CoV and the 2019-nCoV, SARS-CoV-2 virus invade our bodies, causing some differences in the structure of cell proteins. Protein-protein interaction (PPI) is an essential process in our cells and plays a very important role in the development of medicines and gives ideas about the disease. In this study, we performed clustering on PPI networks generated from 92 genes of the Covi-19 dataset. We have used three graph-based clustering algorithms to give intuition to the analysis of clusters.
Subject(s)
COVID-19ABSTRACT
Introduction and Aim: Previously tension-type headache (TTH) was found to be highly prevalent among the general population worldwide, but the current data available were limited. Due to the COVID-19 pandemic, many life changes occurred to adapt to the situation, students started e-learning from home and their sleep quality (SQ) might be influenced. Physiotherapy and nursing students were studied as they are rarely being studied by researchers, information about them was very limited. This study aimed to determine the prevalence of TTH, SQ and the type of correlation between the two during the COVID-19 pandemic. Method(s): A cross-sectional study was conducted by sharing the online questionnaires composed of 2 main components: (i) Questionnaire formulated from diagnosing criteria for TTH of ICHD-3 (ii) Pittsburgh Sleep Quality Index (PSQI), to PS and NS students from higher education institutions in Klang Valley, Malaysia. Result(s): A total of 259 respondents were recruited in the study. The prevalence of TTH was 76.8% and SQ had a mean score of 5.12, which indicated poor SQ among PS and NS students, during the COVID-19 pandemic. Correlation between TTH and SQ was proved to be significant in this study (p=0.032, rs =0.133). Conclusion(s): High prevalence of TTH and poor SQ among PS and NS students during the COVID-19 pandemic was determined. There is a weak positive correlation between TTH and SQ during COVID-19 pandemic.Copyright © 2023, Indian Association of Biomedical Scientists. All rights reserved.
ABSTRACT
[This retracts the article DOI: 10.1007/s00500-021-05643-2.].
ABSTRACT
The Worldwide spread of the Omicron lineage variants has now been confirmed. It is crucial to understand the process of cellular life and to discover new drugs need to identify the important proteins in a protein interaction network (PPIN). PPINs are often represented by graphs in bioinformatics, which describe cell processes. There are some proteins that have significant influences on these tissues, and which play a crucial role in regulating them. The discovery of new drugs is aided by the study of significant proteins. These significant proteins can be found by reducing the graph and using graph analysis. Studies examining protein interactions in the Omicron lineage (B.1.1.529) and its variants (BA.5, BA.4, BA.3, BA.2, BA.1.1, BA.1) are not yet available. Studying Omicron has been intended to find a significant protein. 68 nodes represent 68 proteins and 52 edges represent the relationship among the protein in the network. A few centrality measures are computed namely page rank centrality (PRC), degree centrality (DC), closeness centrality (CC), and betweenness centrality (BC) together with node degree and Local clustering coefficient (LCC). We also discover 18 network clusters using Markov clustering. 8 significant proteins (candidate gene of Omicron lineage variants) were detected among the 68 proteins, including AHSG, KCNK1, KCNQ1, MAPT, NR1H4, PSMC2, PTPN11 and, UBE21 which scored the highest among the Omicron proteins. It is found that in the variant of Omicron protein-protein interaction networks, the MAPT protein's impact is the most significant.
ABSTRACT
Background: The COVID-19 pandemic forcing the students to stay at home to curb the spread of the coronavirus, which inevitably affects their mental and physical health. Thus, the evaluation of mental health (MH), physical activity (PA) and Sedentary Behaviour (SB) of Health Science students during COVID-19 is a need. Objective: To evaluate the physical activity level, mental health and sedentary behaviour of Health Science students in UTAR during COVID-19 and find the correlation among them. Method: 258 health science students were participated in this study via social media, like Facebook and WhatsApp, The Depression, anxiety, stress scale-21 (DASS-21) was used to assess mental health and the International Physical Activity Questionnaire (IPAQ) was used to assess physical activity levels and sedentary behaviour. Result: There were 34.89%, 55.04% and 25.58% of Health Science students were suffering moderate to extremely severe level of depression, anxiety and stress, respectively. Females had a higher prevalence in anxiety (F:55.49%, M: 53.95%) and stress (F:26.37%, M:23.69%), while depression more prevalent in males (M:42.81%, F: 31.87%). The Chinese Medicine students had the poorest mental health and this followed by Physiotherapy, M.B.B.S and Nursing students. Besides, the prevalence of physical inactivity was 48.99%, which a higher prevalence in females (51.43%) than males (43.10%). Besides, 39.53% of Chinese Medicine Students, 62% of M.B.B.S students, 55.56% of Nursing students and 44.83% of Physiotherapy students were categorized as physical inactivity. The prevalence of sedentary behaviour was 48.10% in Health Science students. Besides, no significant correlation found between physical activity and mental health, and sedentary behaviour and mental health. A weak negative correlation was found between physical activity and sedentary behaviour. Conclusion: The prevalence of Depression, Anxiety, Stress, Physical Inactivity and Sedentary Behaviour during the pandemic was very alarming. From government to institution, adequate and regular surveillance, policy monitoring and further research should be taken.
ABSTRACT
COrona VIrus Disease abbreviated as COVID-19 is a novel virus which is initially identified in Wuhan of China in December of 2019, and now, this deadly disease has spread all over the world. According to World Health Organization (WHO), a total of 3,124,905 people died from 2019 to 2021, April. In this case, many methods, AI-based techniques, and machine learning algorithms, have been researched and are being used to save people from this pandemic. The SARS-CoV and the 2019-nCoV, SARS-CoV-2 virus invade our bodies, causing some differences in the structure of cell proteins. Protein–protein interaction (PPI) is an essential process in our cells and plays a very important role in the development of medicines and gives ideas about the disease. In this study, we performed clustering on PPI networks generated from 92 genes of the COVID-19 dataset. We have used three graph-based clustering algorithms to give intuition to the analysis of clusters. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
ABSTRACT
The World Health Organization (WHO) on December 31, 2019, was informed of several cases of respiratory diseases of unknown origin in the city of Wuhan in the Chinese Province of Hubei, the clinical manifestations of which were similar to those of viral pneumonia and manifested as fever, cough, and shortness of breath. And, the disease caused by the virus is named the new coronavirus disease 2019 and it will be abbreviated as 2019-nCoV and COVID-19. As of January 30, 2020, the WHO classified this epidemic as a global health emergency (Chung et al. in Radiology 295(1):202-207, 2020). It is an international real-life problem. Due to deaths, globally everyone is under fear. Now, it is the responsibility of researchers to give hope to the people. In this article, we aim to better protect people and general pandemic preparedness by predicting the lifetime of the disease-causing virus using three mathematical models. This article deals with a complex real-life problem people face all over the world, an international real-life problem. The main focus is on the USA due to large infection and death due to coronavirus and thereby the life of every individual is uncertain. The death counts of the USA from February 29 to April 22, 2020, are used in this article as a data set. The death counts of the USA are fitted by the solutions of three mathematical models and a solution to an international problem is achieved. Based on the death rate, the lifetime of the coronavirus COVID-19 is predicted as 1464.76 days from February 29, 2020. That is, after March 2024 there will be no death in the USA due to COVID-19 if everyone follows the guidelines of WHO and the advice of healthcare workers. People and government can get prepared for this situation and many lives can be saved. It is the contribution of soft computing. Finally, this article suggests several steps to control the spread and severity of the disease. The research work, the lifetime prediction presented in this article is entirely new and differs from all other articles in the literature.