The Internet of Medical Things (IoMT)

Healthcare Transformation

R. J. Hemalatha, D. Akila, D. Balaganesh, Anand Paul, R. J. Hemalatha, D. Akila, D. Balaganesh, Anand Paul

• Publisher: Wiley-Scrivener
• Year: 2022
• ISBN:
  9781119769187
• Edition: 1
• Topic: Computer Science
• Subtopic: Artificial Intelligence (AI) & Semantics
• Index: Computer Science

INTERNET OF MEDICAL THINGS (IOMT)

Providing an essential addition to the reference material available in the field of IoMT, this timely publication covers a range of applied research on healthcare, biomedical data mining, and the security and privacy of health records.

With their ability to collect, analyze and transmit health data, IoMT tools are rapidly changing healthcare delivery. For patients and clinicians, these applications are playing a central part in tracking and preventing chronic illnesses — and they are poised to evolve the future of care.

In this book, the authors explore the potential applications of a wave of sensor-based tools—including wearables and stand-alone devices for remote patient monitoring—and the marriage of internet-connected medical devices with patient information that ultimately sets the IoMT ecosystem apart.

This book demonstrates the connectivity between medical devices and sensors is streamlining clinical workflow management and leading to an overall improvement in patient care, both inside care facilities and in remote locations.

1
In Silico Molecular Modeling and Docking Analysis in Lung Cancer Cell Proteins

Manisha Sritharan¹ and Asita Elengoe^2*

¹Department of Science and Biotechnology, Faculty of Engineering and Life Sciences, University of Selangor, Bestari Jaya, Selangor, Malaysia

²Department of Biotechnology, Faculty of Science, Lincoln University College, Petaling Jaya, Selangor, Malaysia

Abstract

In this study, the three-dimensional (3D) models of lung cancer cell line proteins [epidermal growth factor (EGFR), K-ras oncogene protein, and tumor suppressor (TP53)] were generated and their binding affinities with curcumin, ellagic acid, and quercetin through local docking were assessed. Firstly, Swiss model was used to build lung cancer cell line proteins and then visualized by the PyMol software. Next, ExPASy ProtParam Proteomics server was used to evaluate the physical and chemical parameters of the protein structures. Furthermore, the protein models were validated using PROCHECK, ProQ, ERRAT, and Verify3D programs. Lastly, the protein models were docked with curcumin, ellagic acid, and quercetin by using BSP-Slim server. All three protein models were adequate and in exceptional standard. The curcumin showed binding energy with EGFR, K-ras oncogene protein, and TP53 at 5.320, 2.730, and 1.633, kcal/mol, respectively. Besides that, the ellagic acid showed binding energy of EGFR, K-ras oncogene protein, and TP53 at –2.892, 0.921, and 0.054 kcal/mol, respectively. Moreover, the quercetin showed binding energy of EGFR, K-ras oncogene protein, and TP53 at –1.249, –1.154, and –0.809 kcal/mol, respectively. The EGFR had the strongest bond with ellagic acid while K-ras oncogene protein and TP53 had the strongest interaction with quercetin. In order to identify the appropriate function, all these potential drug candidates can be further assessed through laboratory experiments.

Keywords: EGFR, K-ras, TP53, curcumin, ellagic acid, quercetin, docking

1.1 Introduction

Lung cancer is known to be the number one cause of cancer deaths among all the cancer in both men and women in worldwide. According to a World Health Organization (WHO) survey, lung cancer caused 19.1 deaths per 100,000 in Malaysia, or 4,088 deaths per year (3.22% of all deaths) [1]. Moreover, there was a record of 1.69 million of deaths worldwide in 2015 due to lung cancer. Furthermore, a research in UK estimated that there will be 23.6 million of new cases of cancer worldwide each year by 2030 [1]. The main cause of lung cancer deaths is smoking. Almost 8% of people died because of it. Furthermore, the second reason is exposure to secondhand smoke. Thus, it is very clear that smoking is the leading risk factor for lung cancer. However, not everyone who got lung cancer is smokers as many people with lung cancer are former smokers while many others never smoked at all. Moreover, radiation exposure, unhealthy lifestyle, secondhand smoke, pollution of air, genetic markers, prolongs inhalation of asbestos, and chemicals as well as other factors can cause lung cancer non-smokers [2].

Furthermore, it seems that most lung cancer signs do not appear until the cancer has spread, although some people with early lung cancer do have symptoms. Generally, the symptoms of lung cancer are a cough that does not go away and instead gets worse, shortness of breath, chest...

Table of contents

• Cover
• Table of Contents
• Title Page
• Copyright
• Preface
• 1 In Silico Molecular Modeling and Docking Analysis in Lung Cancer Cell Proteins
• 2 Medical Data Classification in Cloud Computing Using Soft Computing With Voting Classifier: A Review
• 3 Research Challenges in Pre-Copy Virtual Machine Migration in Cloud Environment
• 4 Estimation and Analysis of Prediction Rate of Pre-Trained Deep Learning Network in Classification of Brain Tumor MRI Images
• 5 An Intelligent Healthcare Monitoring System for Coma Patients
• 6 Deep Learning Interpretation of Biomedical Data
• 7 Evolution of Electronic Health Records
• 8 Architecture of IoMT in Healthcare
• 9 Performance Assessment of IoMT Services and Protocols
• 10 Performance Evaluation of Wearable IoT-Enabled Mesh Network for Rural Health Monitoring
• 11 Management of Diabetes Mellitus (DM) for Children and Adults Based on Internet of Things (IoT)
• 12 Wearable Health Monitoring Systems Using IoMT
• 13 Future of Healthcare: Biomedical Big Data Analysis and IoMT
• 14 Medical Data Security Using Blockchain With Soft Computing Techniques: A Review
• 15 Electronic Health Records: A Transitional View
• Index
• End User License Agreement