A Review of Drug Side Effect Identification Methods

2020 ◽  
Vol 26 (26) ◽  
pp. 3096-3104 ◽  
Author(s):  
Shuai Deng ◽  
Yige Sun ◽  
Tianyi Zhao ◽  
Yang Hu ◽  
Tianyi Zang
Keyword(s):  
Side Effects ◽  
Future Research ◽  
Drug Side Effects ◽  
Important Indicator ◽  
Advantages And Disadvantages ◽  
Key Points ◽  
Network Methods ◽  
Future Research Directions ◽  
The One ◽  
Approved Drugs

Drug side effects have become an important indicator for evaluating the safety of drugs. There are two main factors in the frequent occurrence of drug safety problems; on the one hand, the clinical understanding of drug side effects is insufficient, leading to frequent adverse drug reactions, while on the other hand, due to the long-term period and complexity of clinical trials, side effects of approved drugs on the market cannot be reported in a timely manner. Therefore, many researchers have focused on developing methods to identify drug side effects. In this review, we summarize the methods of identifying drug side effects and common databases in this field. We classified methods of identifying side effects into four categories: biological experimental, machine learning, text mining and network methods. We point out the key points of each kind of method. In addition, we also explain the advantages and disadvantages of each method. Finally, we propose future research directions.

Deepfake videos: synthesis and detection techniques –a survey

2021 ◽  
pp. 1-21
Author(s):  
Shahela Saif ◽  
Samabia Tehseen
Keyword(s):  
Social Systems ◽  
Generative Models ◽  
Detection Methods ◽  
Future Research ◽  
Fake News ◽  
Research Directions ◽  
Detection Techniques ◽  
Future Research Directions ◽  
Video Creation ◽  
The One

Deep learning has been used in computer vision to accomplish many tasks that were previously considered too complex or resource-intensive to be feasible. One remarkable application is the creation of deepfakes. Deepfake images change or manipulate a person’s face to give a different expression or identity by using generative models. Deepfakes applied to videos can change the facial expressions in a manner to associate a different speech with a person than the one originally given. Deepfake videos pose a serious threat to legal, political, and social systems as they can destroy the integrity of a person. Research solutions are being designed for the detection of such deepfake content to preserve privacy and combat fake news. This study details the existing deepfake video creation techniques and provides an overview of the deepfake datasets that are publicly available. More importantly, we provide an overview of the deepfake detection methods, along with a discussion on the issues, challenges, and future research directions. The study aims to present an all-inclusive overview of deepfakes by providing insights into the deepfake creation techniques and the latest detection methods, facilitating the development of a robust and effective deepfake detection solution.

scholarly journals Celastrol: A Review of Useful Strategies Overcoming its Limitation in Anticancer Application

2020 ◽  
Vol 11 ◽  
Author(s):  
Jinfeng Shi ◽  
Jiaxin Li ◽  
Ziyi Xu ◽  
Liang Chen ◽  
Ruifeng Luo ◽  
...  
Keyword(s):  
Side Effects ◽  
Therapeutic Window ◽  
Future Research ◽  
Anticancer Activities ◽  
Research Directions ◽  
Anticancer Efficacy ◽  
Cancer Breast ◽  
Future Research Directions ◽  
Micro Systems ◽  
Anticancer Compound

Celastrol, a natural bioactive ingredient derived from Tripterygium wilfordii Hook F, exhibits significant broad-spectrum anticancer activities for the treatment of a variety of cancers including liver cancer, breast cancer, prostate tumor, multiple myeloma, glioma, etc. However, the poor water stability, low bioavailability, narrow therapeutic window, and undesired side effects greatly limit its clinical application. To address this issue, some strategies were employed to improve the anticancer efficacy and reduce the side-effects of celastrol. The present review comprehensively focuses on the various challenges associated with the anticancer efficiency and drug delivery of celastrol, and the useful approaches including combination therapy, structural derivatives and nano/micro-systems development. The specific advantages for the use of celastrol mediated by these strategies are presented. Moreover, the challenges and future research directions are also discussed. Based on this review, it would provide a reference to develop a natural anticancer compound for cancer treatment.

The Oxford Handbook of Prehistoric Oceania

2014 ◽  
Keyword(s):  
New Zealand ◽  
New Caledonia ◽  
New Guinea ◽  
Future Research ◽  
Research Directions ◽  
Settlement Systems ◽  
Coastline Evolution ◽  
Aotearoa New Zealand ◽  
Key Points ◽  
Future Research Directions

The prehistory of Oceania begins with the occupation of New Guinea over 50,000 years ago, up to the settlement of Aotearoa/New Zealand in the last 700 years. The Oxford Handbook of Prehistoric Oceania presents this history in regional overviews and debates through 21 chapters by leading archaeologists and scholars of allied fields. Chapters present the latest findings and future research directions on the New Guinea region and archipelagos from Vanuatu, New Caledonia, Fiji, Tonga, and Samoa in the western Pacific. Micronesia, East Polynesia, Hawaii, Aotearoa/New Zealand, and Easter Island are also discussed in individual chapters. Chapters on wider disciplinary issues summarize key points of method and theory in Oceanic archaeology, including the generation of explanations, building chronologies, linguistic prehistory, coastline evolution, settlement systems, and maritime migration.

Present Situation of Research on Coal Mining Subsidence under Highway in China

2013 ◽  
Vol 664 ◽  
pp. 954-959
Author(s):  
Wei Nan Deng ◽  
Hua Xing Zhang
Keyword(s):  
Coal Mining ◽  
Present Situation ◽  
Mining Subsidence ◽  
Future Research ◽  
Research Directions ◽  
Mining Areas ◽  
Key Points ◽  
Safety Design ◽  
Future Research Directions ◽  
Coal Mining Subsidence

Research on coal mining subsidence under highway can be divided into two fields: research on the problems caused by coal mining under highway and research on the problems caused by highway construction above mined-out area of coal mine.The issues about safety，design and engineering are the key points restricting the safety and the construction of highway in coal mining areas. The paper completely summarized and analysed the present situation of research on coal mining subsidence under highway and the special characteristics comparing with the general building and the railway. In order to ensure the safety of existing highways and highways in planning in coal mining areas, according to the deficiency of current research, this paper put forward the future research directions of coal mining subsidence under highway.

scholarly journals Divergent Thinking Abilities in Frontotemporal Dementia: A Mini-Review

2021 ◽  
Vol 12 ◽  
Author(s):  
Giulia Fusi ◽  
Maura Crepaldi ◽  
Laura Colautti ◽  
Massimiliano Palmiero ◽  
Alessandro Antonietti ◽  
...  
Keyword(s):  
Frontotemporal Dementia ◽  
Creative Thinking ◽  
Divergent Thinking ◽  
Single Case ◽  
Case Series ◽  
Thinking Skills ◽  
Future Research ◽  
Functional Changes ◽  
Future Research Directions ◽  
The One

A large number of studies, including single case and case series studies, have shown that patients with different types of frontotemporal dementia (FTD) are characterized by the emergence of artistic abilities. This led to the hypothesis of enhanced creative thinking skills as a function of these pathological conditions. However, in the last years, it has been argued that these brain pathologies lead only to an augmented “drive to produce” rather than to the emergence of creativity. Moreover, only a few studies analyzed specific creative skills, such as divergent thinking (DT), by standardized tests. This Mini-Review aimed to examine the extent to which DT abilities are preserved in patients affected by FTD. Results showed that DT abilities (both verbal and figural) are altered in different ways according to the specific anatomical and functional changes associated with the diverse forms of FTD. On the one hand, patients affected by the behavioral form of FTD can produce many ideas because of unimpaired access to memory stores (i.e., episodic and semantic), but are not able to recombine flexibly the information to produce original ideas because of damages in the pre-frontal cortex. On the other hand, patients affected by the semantic variant are impaired also in terms of fluency because of the degradation of their semantic memory store. Potential implications, limitations, and future research directions are discussed.

scholarly journals A Review of the State of the Art in Non-Contact Sensing for COVID-19

Sensors ◽  
2020 ◽  
Vol 20 (19) ◽  
pp. 5665
Author(s):  
William Taylor ◽  
Qammer H. Abbasi ◽  
Kia Dashtipour ◽  
Shuja Ansari ◽  
Syed Aziz Shah ◽  
...  
Keyword(s):  
Healthcare Workers ◽  
State Of The Art ◽  
Future Research ◽  
Research Directions ◽  
Advantages And Disadvantages ◽  
Non Invasive ◽  
Current State ◽  
Global Pandemic ◽  
Future Research Directions ◽  
Contact Sensing

COVID-19, caused by SARS-CoV-2, has resulted in a global pandemic recently. With no approved vaccination or treatment, governments around the world have issued guidance to their citizens to remain at home in efforts to control the spread of the disease. The goal of controlling the spread of the virus is to prevent strain on hospitals. In this paper, we focus on how non-invasive methods are being used to detect COVID-19 and assist healthcare workers in caring for COVID-19 patients. Early detection of COVID-19 can allow for early isolation to prevent further spread. This study outlines the advantages and disadvantages and a breakdown of the methods applied in the current state-of-the-art approaches. In addition, the paper highlights some future research directions, which need to be explored further to produce innovative technologies to control this pandemic.

In Defence of Mobile Technologies Exploring the Socio-Technological Dimensions of M-Learning

2011 ◽  
pp. 67-78 ◽  
Author(s):  
Ayse Kok
Keyword(s):  
Learning Experience ◽  
Research Area ◽  
Mobile Technologies ◽  
Educational Services ◽  
Future Research ◽  
Current State ◽  
The Poor ◽  
Key Points ◽  
Mobile Phone Applications ◽  
Future Research Directions

There has been a rapid growth in the research concerning mobile phones and the delivery of the learning experience in developing countries in recent years. The aim of this chapter is to improve understanding of this expanding research area and in so doing consider the potential for mobile phone applications for the delivery of educational services for the poor. The current state of knowledge is assessed by reviewing the existing research articles drawn from both peer-reviewed academic journals and non-peer reviewed studies and other practitioner-orientated sources. Issues relating to educational needs and the measurement of impacts have been comparatively neglected, whilst application design and adoption have received greater attention. Emphasis tends to be on devices and new ways to deliver services, but ignores the broader context of educational services for the poor and tends to be technology-led (Duncombe, 2006). In order to correct this imbalance in research, the paper identifies key points relating to concepts, methodologies, issues addressed and evidence presented and provides pointers to future research directions.

Study on distributed privacy preserving data mining

2014 ◽  
Vol 11 (2) ◽  
pp. 163-170
Author(s):  
Binli Wang ◽  
Yanguang Shen
Keyword(s):  
Data Mining ◽  
Data Privacy ◽  
Rapid Development ◽  
Privacy Preserving ◽  
Future Research ◽  
Distributed Data ◽  
Distributed Environment ◽  
Privacy Preserving Data Mining ◽  
Advantages And Disadvantages ◽  
Future Research Directions

Recently, with the rapid development of network, communications and computer technology, privacy preserving data mining (PPDM) has become an increasingly important research in the field of data mining. In distributed environment, how to protect data privacy while doing data mining jobs from a large number of distributed data is more far-researching. This paper describes current research of PPDM at home and abroad. Then it puts emphasis on classifying the typical uses and algorithms of PPDM in distributed environment, and summarizing their advantages and disadvantages. Furthermore, it points out the future research directions in the field.

scholarly journals A Review of Data Envelopment Analysis in Airline Efficiency: State of the Art and Prospects

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Qiang Cui ◽  
Li-Ting Yu
Keyword(s):  
Data Envelopment Analysis ◽  
Rapid Development ◽  
Aviation Industry ◽  
Future Research ◽  
Data Envelopment ◽  
Social Concern ◽  
Network Dea ◽  
Advantages And Disadvantages ◽  
Dea Models ◽  
Future Research Directions

The rapid development of the aviation industry has brought about the deterioration of the climate, which makes airline efficiency become a hot issue of social concern. As an important nonparametric method, Data Envelopment Analysis (DEA), has been widely applied in efficiency evaluation. This paper examines 130 papers published in the period of 1993–2020 to summarize the literature involving the special application of DEA models in airline efficiency. The paper begins with an overall review of the existing literature, and then the radial DEA, nonradial DEA, network DEA, dynamic DEA, and DEA models with undesirable outputs applied in airline efficiency are introduced. The main advantages and disadvantages of the above models are summarized, and the drivers of airline efficiency are analyzed. Finally, the literature review ends up with future research directions and conclusions.

Predicting Drug Side Effects with Compact Integration of Heterogeneous Networks

2019 ◽  
Vol 14 (8) ◽  
pp. 709-720 ◽  
Author(s):  
Xian Zhao ◽  
Lei Chen ◽  
Zi-Han Guo ◽  
Tao Liu
Keyword(s):  
Side Effects ◽  
Network Model ◽  
Heterogeneous Networks ◽  
Side Effect ◽  
Correlation Coefficients ◽  
Machine Learning Algorithms ◽  
Drug Side Effects ◽  
Essential Information ◽  
Approved Drugs ◽  
Feature Dimension

Background: The side effects of drugs are not only harmful to humans but also the major reasons for withdrawing approved drugs, bringing greater risks for pharmaceutical companies. However, detecting the side effects for a given drug via traditional experiments is time- consuming and expensive. In recent years, several computational methods have been proposed to predict the side effects of drugs. However, most of the methods cannot effectively integrate the heterogeneous properties of drugs. Methods: In this study, we adopted a network embedding method, Mashup, to extract essential and informative drug features from several drug heterogeneous networks, representing different properties of drugs. For side effects, a network was also built, from where side effect features were extracted. These features can capture essential information about drugs and side effects in a network level. Drug and side effect features were combined together to represent each pair of drug and side effect, which was deemed as a sample in this study. Furthermore, they were fed into a random forest (RF) algorithm to construct the prediction model, called the RF network model. Results: The RF network model was evaluated by several tests. The average of Matthews correlation coefficients on the balanced and unbalanced datasets was 0.640 and 0.641, respectively. Conclusion: The RF network model was superior to the models incorporating other machine learning algorithms and one previous model. Finally, we also investigated the influence of two feature dimension parameters on the RF network model and found that our model was not very sensitive to these parameters.

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