Machine Learning-Based Multimodel Computing for Medical Imaging for Classification and Detection of Alzheimer Disease.

Alzheimer is a disease that causes the brain to deteriorate over time. It starts off mild, but over the course of time, it becomes increasingly more severe. Alzheimer's disease causes damage to brain cells as well as the death of those cells. Memory in humans is especially susceptible to this. Memory loss is the first indication of Alzheimer's disease, but as the disease progresses and more brain cells die, additional symptoms arise. Medical image processing entails developing a visual portrayal of the inside of a body using a range of imaging technologies in order to discover and cure problems. This paper presents machine learning-based multimodel computing for medical imaging for classification and detection of Alzheimer disease. Images are acquired first. MRI images contain noise and contrast problem. Images are preprocessed using CLAHE algorithm. It improves image quality. CLAHE is better to other methods in its capacity to enhance the look of mammography in minute places. A white background makes the lesions more obvious to the naked eye. In spite of the fact that this method makes it simpler to differentiate between signal and noise, the images still include a significant amount of graininess. Images are segmented using the k-means algorithm. This results in the segmentation of images and identification of region of interest. Useful features are extracted using PCA algorithm. Finally, images are classified using machine learning algorithms.

Investigating healthcare practitioners' attitudes towards the COVID-19 outbreak in Saudi Arabia: A general qualitative framework for managing the pandemic.

BACKGROUND: Some previous studies have investigated the attitudes of healthcare professionals towards certain aspects of the COVID-19 outbreak. In addition, some general frameworks have been proposed to manage the pandemic. OBJECTIVE: The purpose of this article was to analyze the attitudes of healthcare practitioners in Saudi Arabia towards the treatment of patients with COVID-19, work planning of practitioners, leadership approaches to manage the pandemic, sharing information strategies, medical errors, compliance with procedures, and challenges faced by the practitioners. Furthermore, another objective was to propose a general framework for managing the COVID-19 outbreak in Saudi Arabia. METHODS: To achieve these purposes, a survey was designed based on an online questionnaire that was initially sent via WhatsApp, Twitter, Facebook, and email to 336 healthcare practitioners working in 7 hospitals in Saudi Arabia. The response rate was 30.4%. RESULTS: The outcomes indicated that healthcare practitioners in Saudi Arabia had positive attitudes towards effective communication and interaction between health professionals and patients, leadership and maintenance of team coordination, work planning, communication and cooperation between team members, training and skills development of healthcare professionals, implementing strict procedures to avoid errors and control the spread of the COVID-19 pandemic, maintaining an adequate supply of medicines and medical equipment, and obtaining the support of the government, the community, and the people. CONCLUSION: Based on the findings, it was possible to suggest that the management of health care operations related to the COVID-19 outbreak in Saudi Arabia requires effective collaboration and information sharing among various stakeholders. In this sense, communication, effective leadership, coordination and work planning, adequate treatment for patients, strict compliance with hospital rules and procedures, preventive and regulatory measures, and training and support for health professionals, were parameters considered in the general qualitative framework suggested in this study for managing the COVID-19 pandemic in Saudi Arabia. The propositions presented in this study can help the Saudi Arabian government implement an effective plan to control the spread of the COVID-19 pandemic in this country.

A comparative study of strategies for containing the COVID-19 pandemic in Gulf Cooperation Council countries and the European Union.

BACKGROUND: The COVID-19 pandemic has impacted every aspect of human lives including health, businesses, and lifestyles. In spite of governments implementing various strategies across the globe, the pandemic is still expanding with increasing numbers of positive cases. In addition, countries are reopening and easing lockdown restrictions in order to get their economies back on track, and this has led to an increase in the transmission of novel coronavirus. Therefore, it is essential to regularly review the containment strategies employed in different regions in order to understand the characteristics of COVID-19 transmission and to formulate a future course of actions. OBJECTIVE: The objective of this study is to review the COVID-19 transmission statistics in Gulf Cooperation Council (GCC) and European Union (EU) countries, and to compare these data with the various containment strategies implemented for containing the spread of the virus. METHODS: A review method was adopted along with different statistical methods for comparing and analyzing COVID-19 data and containment strategies. Transmission types and the Case Fatality Rate (CFR) in the countries in both regions are used to present the current state of the pandemic. In addition, changes in the number of COVID-19 cases are compared with the mitigation and suppression strategies implemented in both regions and their impact is analyzed. RESULTS: Countries in the EU were slow in reacting to the pandemic, as delays are observed in the implementation of mitigation strategies. However, suppression strategies were implemented soon after mitigation strategies. GCC countries, on the other hand, were quick to react, and they implemented both mitigation and suppression strategies simultaneously, as soon as the pandemic emerged. The CFR was found to be low among GCC countries compared to EU countries. In addition, a second wave of transmission was observed in the EU, whereas in GCC countries there has been no second wave, although a gradual increase in the number of cases is observed. Community transmission was observed among the majority of countries in both GCC and EU countries. CONCLUSIONS: With the reopening of markets, the focus of governments should be on developing integrated user-centric preventive strategies, with a blend of awareness creation, motivation, and support.

A Comparative Study on the Strategies Adopted by the United Kingdom, India, China, Italy, and Saudi Arabia to Contain the Spread of the COVID-19 Pandemic.

PURPOSE: The objective of this study was to compare the strategies adopted by the United Kingdom, Italy, China, India, and Saudi Arabia to contain the spread of the COVID-19 pandemic. MATERIALS AND METHODS: A review of the literature was carried out to collect data on the strategies used by China, Italy, India, the United Kingdom, and Saudi Arabia to contain the spread of the COVID-19 virus. The global analysis of 65 published literature references allowed observing the effectiveness and efficiency of the strategies used by these countries to control the spread of the COVID-19 virus. RESULTS: Both mitigation and suppression strategies were adopted by the United Kingdom, India, Italy, China, and Saudi Arabia to control the spread of the COVID-19 pandemic. It was observed that China has achieved a greater success in flattening the curve compared to the other countries. In China, few new daily cases have occurred since March, and it has been the only country that has managed to keep the COVID-19 pandemic under control. On the other hand, reductions in the number of daily cases (since May 2020) were detected in the United Kingdom, Italy, and Saudi Arabia (since July 2020). Also, during the last 3 months (June, July and August) India has shown the highest growth in the total number of confirmed cases and in the number of new daily cases, compared to the mentioned countries. CONCLUSION: The review of the strategies adopted by China, India, the United Kingdom, Italy and Saudi Arabia to combat the COVID-19 pandemic can guide countries in the design and development of mitigation and suppression approaches to control the spread of the COVID-19 virus. Containment strategies such as lockdowns cannot continue in the long term. Therefore, countries must adopt mitigation and prevention strategies to protect people from infection and learn to live with the virus.

Incorporating Protein Dynamics Through Ensemble Docking in Machine Learning Models to Predict Drug Binding.

Drug discovery is an expensive, lengthy, and sometimes dangerous process. The ability to make accurate computational predictions of drug binding would greatly improve the cost-effectiveness and safety of drug discovery and development. This study incorporates ensemble docking, the use of multiple protein conformations extracted from a molecular dynamics trajectory to perform docking calculations, with additional biomedical data sources and machine learning algorithms to improve the prediction of drug binding. We found that we can greatly increase the classification accuracy of an active vs a decoy compound using these methods over docking scores alone. The best results seen here come from having an individual protein conformation that produces binding features that correlate well with the active vs. decoy classification, in which case we achieve over 99% accuracy. The ability to confidently make accurate predictions on drug binding would allow for computational polypharamacological networks with insights into side-effect prediction, drug-repurposing, and drug efficacy.

Polypharmacology Within the Full Kinome: a Machine Learning Approach.

Protein kinases generate nearly a thousand different protein products and regulate the majority of cellular pathways and signal transduction. It is therefore not surprising that the deregulation of kinases has been implicated in many disease states. In fact, kinase inhibitors are the largest class of new cancer therapies. Understanding polypharmacology within the full kinome, how drugs interact with many different kinases, would allow for the development of safer and more efficacious cancer therapies. A full understanding of these interactions is not experimentally feasible making highly accurate computational predictions extremely useful and important. This work aims at making a machine learning model useful for investigating the full kinome. We evaluate many feature sets for our model and get better performance over molecular docking with all of them. We demonstrate that you can achieve a nearly 60% increase in success rate at identifying binding compounds using our model over molecular docking scores.