China 
Africa 
Explore chapters and articles related to this topic
Illuminating the cycle of life
Published in Raquel Seruca, Jasjit S. Suri, João M. Sanches, Fluorescence Imaging and Biological Quantification, 2017
Anabela Ferro, Patrícia Carneiro, Maria Sofia Fernandes, Tânia Mestre, Ivan Sahumbaiev, João M. Sanches, Raquel Seruca
Metabolic disorders associated to elevated body weight and obesity has reached epidemic proportions in industrialized countries, instigating research to identify novel biomarkers and potential molecular targets for efficient drug development. Metabolic disorders are a set of pathophysiological conditions displaying energy and redox imbalance due to disruption of normal metabolic processes. Several studies have provided evidence that this is closely associated with ROS-mediated oxidative stress signals that induce adipogenesis when terminally differentiated preadipocytes reenter the cell cycle and proliferate [102].
Inflammatory Biomarkers: An Important Tool for Herbal Drug Discovery
Published in Mahfoozur Rahman, Sarwar Beg, Mazin A. Zamzami, Hani Choudhry, Aftab Ahmad, Khalid S. Alharbi, Biomarkers as Targeted Herbal Drug Discovery, 2022
Mahfoozur Rahman, Ankit Sahoo, Mohammad Atif, Sarwar Beg
It is a chronic metabolic disorder of carbohydrate, protein, and fat, which is a lifelong condition affecting the person’s blood glucose and insulin level in the body. In diabetic condition, pancreases either do not produce enough insulin or the body does not use it properly. It is estimated that the worldwide prevalence of diabetes will increase from 4% in 1995 to 5.4% by 2025. According to WHO prediction, the major burden will occur in developing countries?
A Secured Healthcare Management and Service Retrieval for Society Over Apache Spark Hadoop Environment
Published in IETE Journal of Research, 2023
It refers to diabetes mellitus, which describe the Metabolic Disorder due to Chronic Hyperglycemia, Carbohydrate, Fat, and Protein Metabolism and defects by Insulin Secretion, Insulin Action, or Both. The causes of diabetes mellitus are as follows: Long-Term Damage, Dysfunction, and Failure of various organs. The failure in blood glucose monitoring prevents all health disorders and helps the patients to give suggestions on their habits, meditation schedules, and meals. We tested our proposed model by developing the Healthcare Prototype System to predict the most dangerous patients. There are two frameworks used for this case study: Apache Spark and MongoDB. For the detection of diabetes patients, spark reads the Metadata information from MongoDB. Jobs are executed in a regular sampling interval that is specified by the user. In apache spark, the emergency case is determined and then sends a warning message to the user from the entries of MongoDB. We have used Spark MLLib and a deep reinforcement learning algorithm to predict the current state of the patient whether the emergency is required or not, which is a deep learning classification.