The Role of Genetic Data Analysis for Precision Therapy in Cancer

Cancer has been known as a devastating disease that takes thousands of lives every year. And since this is a heterogenous disease, standard treatments, like chemotherapy, radiation, and chemo-radio therapy, are effective in specific patient population subset only. Genetic differences play a very crucial role in defining cancer susceptibility and also in determining the drug's efficacy by affecting regulation, expression, and activity of drug metabolizing enzymes, drug transporters, and drug receptors. This genetic variability of the disease lends itself to the emerging field of precision or personalized medicine. There are some specific ways of acquiring data for precision or personalized medicine approach like genome wide association scan (GWAS). This is basically identification and scanning of biomarkers throughout the complete DNA/genome of several individuals to study any type of genetic variations which are linked with any form of cancer.

Android-Based Skin Cancer Recognition System Using Convolutional Neural Network

Skin cancer, which is one of the most common types of cancer in the world, is a malignant growth seen on the skin due to various reasons. There was an increase in the number of the cases of skin cancer nearly 200% between 2004-2009. Since the ozone layer is depleting, harmful rays reflected from the sun cannot be filtered. In this case, the likelihood of skin cancer will increase over the years and pose more risks for human beings. Early diagnosis is very significant as in all types of cancers. In this study, a mobile application is developed in order to detect whether the skin spots photographed by using the machine learning technique for early diagnosis have a suspicion of skin cancer. Thus, an auxiliary decision support system is developed that can be used both by the clinicians and individuals. For cases that are predicted to have a risk higher than a certain rate by the machine learning algorithm, early diagnosis could be initiated for the patients by consulting a physician when the case is considered to have a higher risk by machine learning algorithm.

Internet of Things (IoT) in Healthcare

The recent decade has seen considerable changes in the way the technology interacts with human lives and almost all the aspects of life be it personal or professional has been touched by technology. Many smart devices have also started playing a vital role in many fields and domains and the internet of things (IoT) has been the harbinger of the advent of IoT devices. IoT devices have proven to be monumental in imparting ‘smartness' in the otherwise static machines. The ability of the devices to interact and transfer the data to the internet and ultimately to the end-user has revolutionized the technological world and has brought many seemingly disparate fields in the technological purview. Out of the many fields where IoT has started gaining momentum, one of the most important ones is the healthcare sector. Many wearable smart devices have been developed over time capable to transmit real-time data to hospitals and doctors. It is essential for tracking the progress of the critically ill patients and has opened the horizon for attending patients remotely using these smart devices.

Public Health Surveillance System

To guard people against some grave infectious disease, the surveillance system is a key performance measure of global public health threats and vulnerability. The diseases surveillance system helps in public health monitor, control, and prevent infectious diseases. Infectious diseases remain major causes of death. It's important to monitor and surveillance worldwide for developing a framework for risk assessment and health regulation. Surveillance systems help us in understanding the factors driving infectious disease and developing new technological aptitudes with modeling, pathogen determination, characterization, diagnostics, and communications. This chapter discussed surveillance system working, progress toward global public healthy society considering perspectives for the future and improvement of infectious disease surveillance without limited and fragmented capabilities, and making even global coverage.

The Role of E-Health Interventions in Improving Clinical Outcomes and Overall Health for Prostate Cancer Patients

Globally, prostate cancer is a major healthcare problem. It is among the most frequently diagnosed malignancies and is the primary cancer in males in North America and the Caribbean, Europe, and some parts of Africa. Mobile health interventions afford prostate cancer patients in following prostate specific antigen results including trends, getting a better understanding of the severity of their disease and evaluate carefully the benefits and risks of the available treatment options. This review will examine the use of mobile health applications in prostate cancer research particularly in (1) clinical decision of selecting best treatment option or active surveillance, (2) monitoring disease- and treatment-related symptoms, (3) oncological and supportive care, (4) treatment decisions, and (5) health literacy and promotion of physical exercise. The benefits of telemedicine are discussed. Challenges will be examined and recommendations given for the development and efficient use of mobile health applications by prostate cancer patients and healthcare providers.

IoT on Healthcare Using Clinical Decision Support System

Most of the developing countries face major problems in providing quality healthcare. It is very essential to move the health stream to a higher level with more effective. Though medical care is improving, due to the enormous amount of data, making the decisions is more complex. The technology already links patients, providers, and customers in many ways that are converting the patient experience and delivery of care. This chapter reveals the importance of healthcare by using CDSS along with IoT. By combining connected devices with CDSS will help the clinicians to take decisions immediately for any disease. It provides an efficient, effective quality measurement and enhancement because of its ability to get the data of any patient at any time anywhere.

Applications of Nanoparticles in Various Fields

Nanoparticles (NPs) are tiny particles having dimensions ranging from 1 nm to 100 nm. Nanoparticles are field of profound scientific interest, on account of diverse conceivable applications in various fields such as electronic, optical, agriculture, biomedical, etc. Many of the interesting properties of nanoparticles are intimately linked on shape and size of nanomaterials. In nanoparticles, percentage of surface atoms are high; nanoparticles show properties dependent on shape which are utilized in catalysis, optics, data storage, etc. Further, the physical properties of nanoparticles such as melting point, density, optical properties, electrical conductivity, chemical stability, etc. make them suitable candidates to be utilized in several fields. Many of the nanoparticles have been widely studied and many applications explored for example gold and silver nanoparticles, while research is being carried out to investigate the probable applications in several other fields. This review provides the readers a summary of the applications of various nanoparticles.

Incautious Usage of Social Media

The purpose of this chapter is to examine the incautious usage of social media and its impact on emotional intelligence and health. After a brief introduction to the emotional intelligence and the conceptualisation and evaluation of this construct, this chapter discusses a variety of studies that shed light on the social media, emotional intelligence and health relationships. The idea of emotional intelligence (EI) is of unmatched enthusiasm for both the literature and inside scholarly world. This chapter discusses emotional intelligence and focuses on the evolution of EI by examining the different models. This chapter lists some applications of emotional intelligence in our daily life. The chapter also discusses how the abilities correlate with emotional intelligence and helps individuals cope with unsettling emotions effectively and encourage pleasurable emotions to facilitate personal development and well-being.

Detection of Diabetic Retinopathy With Mobile Application Using Deep Learning

High glucose level disrupts the structure of the retinal layer in the eyes and causes diabetic retinopathy which is characterized with new pathologic blood vessels in the eyes. Although diabetic retinopathy is not clear at the beginning of the disease, it is the most common problem in people who have diabetes and causes blindness or cloudy vision if it is not diagnosed at the beginning of the disease. For early diagnosis of diabetic retinopathy, regular fundus controls and examination of the edema in the vessels of the retina are made periodically by ophthalmologists. With in the scope of this study, it is made possible to provide the early diagnosis and the level of diabetic retinopathy by using deep learning, image processing methods, and convolutional neural networks of the retina. In order to provide ease and rapid of diagnosis of the diabetic retinopathy in daily life, the diagnosis protocol has been turned into a mobile application. With the mobile application, both the diagnosis and more regular results of the diabetic retinopathy can be obtained easily and practically.

Epileptic Seizure Detection Using Machine Learning Techniques

Epilepsy is a brain disorder that can be defined as a short-time and temporary occurrence of symptoms because of abnormal extreme or synchronous neuronal activity of the brain. Almost one percent of the world's population is struggling with epilepsy illness. The detection of epileptic seizures is mainly realized with reading the electroencephalogram (EEG) recordings by medical doctors due to the unpredictable and complex nature of the disease. This process takes much time and depends on the expert's experience. For this reason, automatic seizure detection using EEG recordings is necessary and of great importance for the comfort of medical doctors and patients. While detecting epileptic seizure automatically, machine learning techniques are used in the field of computer science. This chapter deals with the methods, approaches, models, and techniques which are utilized to detect epileptic seizures.