Following the rapid spread and evolution of the novel Corona virus starting in December 2019, the lack of a vaccine or a medication that proved to be effective for Covid-19 was addressed as a major concern by the World Health Organization (WHO), the Center for Disease Control and Prevention (CDC), and the U.S. Food and Drug Administration (FDA) [1]. Accordingly, physicians from countries like China and Korea rushed to provide some potential treatment for Covid-19 from their experience in treating patients of the novel Coronavirus -they used antiviral medications like lopinavir, ritonavir, chloroquine, hydroxychloroquine, ribavirin, interferon, remdesivir, sofosbuvir, nitazoxanide, favipiravir, ivermectin, etc. [1]-[3]. These drugs showed improvement in conditions of Covid-19 patients when used individually, or sometimes using a combination of multiple of them. This does not mean that any combinations of these drugs could be beneficial. Some combinations can be lethal and may lead to increasing health risks or mortality. The drugs are being used in vitro (i.e., on cells in a laboratory for experiments) and vivo (i.e., on humans or animals as clinical trials). In vitro analysis, the chemical structure of the drug and the disease are analyzed to generate a hypothesis on the performance of the drug, then the hypothesis is tested in vivo to measure the actual performance of the drug on a living creature. Although these drugs showed promising results with proper dosage, overdose and incorrect combination with other drugs sometimes proved to be lethal. The effectiveness and side-effects of some of these drugs as reported by recent researchers and trials are described in this paper. We address some related research questions concerning the side effects of the covered drugs and their interaction with other drugs based on some well tested results extracted from approved websites of drug-drug interactions. The findings are interesting and confirmed favipiravir as the most effective and safe compared to the others, and this coincides with and supports the announcement by Turkish Ministry of Health where favipiravir has been used in treating COVID-19 patients since the early days.
Brain cancers caused by malignant brain tumors are one of the most fatal cancer types with a low survival rate mostly due to the difficulties in early detection. Medical professionals therefore use various invasive and non-invasive methods for detecting and treating brain tumors at the earlier stages thus enabling early treatment. The main non-invasive methods for brain tumor diagnosis and assessment are brain imaging like computed tomography (CT), positron emission tomography (PET) and magnetic resonance imaging (MRI) scans. In this paper, the focus is on detection and segmentation of brain tumors from 2D and 3D brain MRIs. For this purpose, a complete automated system with a web application user interface is described which detects and segments brain tumors with more than 90% accuracy and Dice scores. The user can upload brain MRIs or can access brain images from hospital databases to check presence or absence of brain tumor, to check the existence of brain tumor from brain MRI features and to extract the tumor region precisely from the brain MRI using deep neural networks like CNN, U-Net and U-Net++. The web application also provides an option for entering feedbacks on the results of the detection and segmentation to allow healthcare professionals to add more precise information on the results that can be used to train the model for better future predictions and segmentations.
Artificial Intelligence and its sub-branches like Machine Learning (ML) and Deep Learning (DL) applications have the potential to have positive effects that can directly affect human life. Medical imaging is briefly making the internal structure of the human body visible with various methods. With deep learning models, cancer detection, which is one of the most lethal diseases in the world, can be made possible with high accuracy. Pancreatic Tumor detection, which is one of the cancer types with the highest fatality rate, is one of the main targets of this project, together with the data set of Computed Tomography images, which is one of the medical imaging techniques and has an effective structure in Pancreatic Cancer imaging. In the field of image classification, which is a computer vision task, the transfer learning technique, which has gained popularity in recent years, has been applied quite frequently. Using pre-trained models were previously trained on a fairly large dataset and using them on medical images is common nowadays. The main objective of this article is to use this method, which is very popular in the medical imaging field, in the detection of PDAC, one of the deadliest types of pancreatic cancer, and to investigate how it per- forms compared to the custom model created and trained from scratch. The pre-trained models which are used in this project are VGG-16 and ResNet, which are popular Convolutional Neutral Network models, for Pancreatic Tumor Detection task. With the use of these models, early diagnosis of pancreatic cancer, which progresses insidiously and therefore does not spread to neighboring tissues and organs when the treatment process is started, may be possible. Due to the abundance of medical images reviewed by medical professionals, which is one of the main causes for heavy workload of healthcare systems, this application can assist radiologists and other specialists in Pancreatic Tumor detection by providing faster and more accurate method
No abstract
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.