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Jain S, Vimal N, Angmo N, Sengupta M, Thangaraj S. Dengue Vaccination: Towards a New Dawn of Curbing Dengue Infection. Immunol Invest 2023; 52:1096-1149. [PMID: 37962036 DOI: 10.1080/08820139.2023.2280698] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2023]
Abstract
Dengue is an infectious disease caused by dengue virus (DENV) and is a serious global burden. Antibody-dependent enhancement and the ability of DENV to infect immune cells, along with other factors, lead to fatal Dengue Haemorrhagic Fever and Dengue Shock Syndrome. This necessitates the development of a robust and efficient vaccine but vaccine development faces a number of hurdles. In this review, we look at the epidemiology, genome structure and cellular targets of DENV and elaborate upon the immune responses generated by human immune system against DENV infection. The review further sheds light on various challenges in development of a potent vaccine against DENV which is followed by presenting a current account of different vaccines which are being developed or have been licensed.
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Affiliation(s)
- Sidhant Jain
- Independent Researcher, Institute for Globally Distributed Open Research and Education (IGDORE), Rewari, India
| | - Neha Vimal
- Bhaskaracharya College of Applied Sciences, University of Delhi, Delhi, India
| | - Nilza Angmo
- Maitreyi College, University of Delhi, Delhi, India
| | - Madhumita Sengupta
- Janki Devi Bajaj Government Girls College, University of Kota, Kota, India
| | - Suraj Thangaraj
- Swami Ramanand Teerth Rural Government Medical College, Maharashtra University of Health Sciences, Ambajogai, India
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2
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DDPM: A Dengue Disease Prediction and Diagnosis Model Using Sentiment Analysis and Machine Learning Algorithms. Diagnostics (Basel) 2023; 13:diagnostics13061093. [PMID: 36980401 PMCID: PMC10047105 DOI: 10.3390/diagnostics13061093] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2023] [Revised: 02/28/2023] [Accepted: 03/11/2023] [Indexed: 03/17/2023] Open
Abstract
The aedes mosquito-borne dengue viruses cause dengue fever, an arboviral disease (DENVs). In 2019, the World Health Organization forecasts a yearly occurrence of infections from 100 million to 400 million, the maximum number of dengue cases ever testified worldwide, prompting WHO to label the virus one of the world’s top ten public health risks. Dengue hemorrhagic fever can progress into dengue shock syndrome, which can be fatal. Dengue hemorrhagic fever can also advance into dengue shock syndrome. To provide accessible and timely supportive care and therapy, it is necessary to have indispensable practical instruments that accurately differentiate Dengue and its subcategories in the early stages of illness development. Dengue fever can be predicted in advance, saving one’s life by warning them to seek proper diagnosis and treatment. Predicting infectious diseases such as dengue is difficult, and most forecast systems are still in their primary stages. In developing dengue predictive models, data from microarrays and RNA-Seq have been used significantly. Bayesian inferences and support vector machine algorithms are two examples of statistical methods that can mine opinions and analyze sentiment from text. In general, these methods are not very strong semantically, and they only work effectively when the text passage inputs are at the level of the page or the paragraph; they are poor miners of sentiment at the level of the sentence or the phrase. In this research, we propose to construct a machine learning method to forecast dengue fever.
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Abubakar H, Idris M. Artificial Neural Network Logic-Based Reverse Analysis with Application to COVID-19 Surveillance Dataset. ARTIF INTELL 2023. [DOI: 10.5772/intechopen.106210] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/26/2023]
Abstract
The Boolean Satisfiability Problem (BSAT) is one of the crucial decision problems in the fields of computing science, operation research, and mathematical logic that is resolved by deciding whether or not a solution to a Boolean formula exists. When there is a Boolean variable allocation that induces the Boolean formula to yield TRUE, then the SAT instance is satisfiable. The main purpose of this chapter is to utilize the optimization capacity of the Lyapunov energy function of Hopfield neural network (HNN) for optimal representation of the Random Satistibaility for COVID-19 Surveillance Data Set (CSDS) classification with the aim of extracting the relationship of dominant attributes that contribute to COVID-19 detections based on the COVID-19 Surveillance Data Set (CSDS). The logical mining task was carried based on the data mining technique of the energy minimization technique of HNN. The computational simulations have been carried using the different number of clauses in validating the efficiency of the proposed model in the training of COVID-19 Surveillance Data Set (CSDS) for classification. The findings reveals the effectiveness and robustness of k satisfiability reverse analysis with Hopfield neural network in extracting the dominant attributes toward COVID-19 Surveillance Data Set (CSDS) logic.
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Khan MAR, Akter J, Ahammad I, Ejaz S, Jaman Khan T. Dengue outbreaks prediction in Bangladesh perspective using distinct multilayer perceptron NN and decision tree. Health Inf Sci Syst 2022; 10:32. [PMID: 36387748 PMCID: PMC9649590 DOI: 10.1007/s13755-022-00202-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2022] [Accepted: 10/24/2022] [Indexed: 11/11/2022] Open
Abstract
Dengue fever is a disease that has been outbreak worldwide in the last few years. Dengue is a fatal disease; sometimes, it may cause life-threatening complications and even death. Dengue is considered to be one of the critical diseases which is spreading in more than 110 countries. Nearly 45,000 case reports have been found around Bangladesh in the last year. Dengue fever has become a major health hazard in Bangladesh. Hence, early detection would mitigate major casualties of Dengue disease. Distinct studies have been performed concerning Dengue disease; however, no effective study, particularly from Bangladesh's perspective, it seemed that reveals Dengue outbreaks prediction method. In this scenario, this research work aims to analyse the Dengue disease and build an apposite model to predict dengue outbreaks. This paper also aims to find the best technique to early predicts Dengue disease. The real-time data of the patients admitted to different hospitals in Bangladesh is accumulated to achieve the goal of the current research. Then different multilayer perceptron neural networks and a Decision tree are used for Dengue outbreaks prediction. Twenty-five parameters are analysed to find these parameters' infection rates in this work. A comparative study of the developed models' performances is also accomplished to obtain a better Dengue outbreaks prediction model. The results evidence that the Levenberg-Marquardt is the best technique with 97.3% accuracy and 2.7% error in Dengue disease prediction. On the other hand, the Decision tree may have the second choice to assess Dengue disease.
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Affiliation(s)
- Md. Ashikur Rahman Khan
- Department of Information and Communication Engineering, Noakhali Science and Technology University, Noakhali, 3814 Bangladesh
| | - Jony Akter
- Department of Information and Communication Engineering, Noakhali Science and Technology University, Noakhali, 3814 Bangladesh
| | - Ishtiaq Ahammad
- Department of Computer Science and Engineering, Prime University, Dhaka, Bangladesh
| | - Sabbir Ejaz
- Department of Information and Communication Engineering, Noakhali Science and Technology University, Noakhali, 3814 Bangladesh
| | - Tanvir Jaman Khan
- Department of Information and Communication Engineering, Noakhali Science and Technology University, Noakhali, 3814 Bangladesh
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Ismail S, Fildes R, Ahmad R, Wan Mohamad Ali WN, Omar T. The practicality of Malaysia dengue outbreak forecasting model as an early warning system. Infect Dis Model 2022; 7:510-525. [PMID: 36091345 PMCID: PMC9418377 DOI: 10.1016/j.idm.2022.07.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2022] [Revised: 07/07/2022] [Accepted: 07/30/2022] [Indexed: 11/26/2022] Open
Abstract
Dengue is a harmful tropical disease that causes death to many people. Currently, the dengue vaccine development is still at an early stage, and only intervention methods exist after dengue cases increase. Thus, previously, two scientific experimental field studies were conducted in producing a dengue outbreak forecasting model as an early warning system. Successfully, an Autoregressive Distributed Lag (ADL) Model was developed using three factors: the epidemiological, entomological, and environmental with an accuracy of 85%; but a higher percentage is required in minimizing the error for the model to be useful. Hence, this study aimed to develop a practical and cost-effective dengue outbreak forecasting model with at least 90% accuracy to be embedded in an early warning computer system using the Internet of Things (IoT) approach. Eighty-one weeks of time series data of the three factors were used in six forecasting models, which were Autoregressive Distributed Lag (ADL), Hierarchical Forecasting (Bottom-up and Optimal combination) and three Machine Learning methods: (Artificial Neural Network (ANN), Support Vector Machine (SVM) and Random Forest). Five error measures were used to evaluate the consistency performance of the models in order to ensure model performance. The findings indicated Random Forest outperformed the other models with an accuracy of 95% when including all three factors. But practically, collecting mosquito related data (the entomological factor) was very costly and time consuming. Thus, it was removed from the model, and the accuracy dropped to 92% but still high enough to be of practical use, i.e., beyond 90%. However, the practical ground operationalization of the early warning system also requires several rain gauges to be located at the dengue hot spots due to localized rainfall. Hence, further analysis was conducted in determining the location of the rain gauges. This has led to the recommendation that the rain gauges should be located about 3–4 km apart at the dengue hot spots to ensure the accuracy of the rainfall data to be included in the dengue outbreak forecasting model so that it can be embedded in the early warning system. Therefore, this early warning system can save lives, and prevention is better than cure.
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Ibrahim S, Abdul Wahab N. Improved Artificial Neural Network Training Based on Response Surface Methodology for Membrane Flux Prediction. MEMBRANES 2022; 12:membranes12080726. [PMID: 35893444 PMCID: PMC9394359 DOI: 10.3390/membranes12080726] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/29/2022] [Revised: 07/18/2022] [Accepted: 07/20/2022] [Indexed: 11/16/2022]
Abstract
This paper presents an improved artificial neural network (ANN) training using response surface methodology (RSM) optimization for membrane flux prediction. The improved ANN utilizes the design of experiment (DoE) technique to determine the neural network parameters. The technique has the advantage of training performance, with a reduced training time and number of repetitions in achieving good model prediction for the permeate flux of palm oil mill effluent. The conventional training process is performed by the trial-and-error method, which is time consuming. In this work, Levenberg–Marquardt (lm) and gradient descent with momentum (gdm) training functions are used, the feed-forward neural network (FFNN) structure is applied to predict the permeate flux, and airflow and transmembrane pressure are the input variables. The network parameters include the number of neurons, the learning rate, the momentum, the epoch, and the training functions. To realize the effectiveness of the DoE strategy, central composite design is incorporated into neural network methodology to achieve both good model accuracy and improved training performance. The simulation results show an improvement of more than 50% of training performance, with less repetition of the training process for the RSM-based FFNN (FFNN-RSM) compared with the conventional-based FFNN (FFNN-lm and FFNN-gdm). In addition, a good accuracy of the models is achieved, with a smaller generalization error.
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7
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de Lima CL, da Silva ACG, Moreno GMM, Cordeiro da Silva C, Musah A, Aldosery A, Dutra L, Ambrizzi T, Borges IVG, Tunali M, Basibuyuk S, Yenigün O, Massoni TL, Browning E, Jones K, Campos L, Kostkova P, da Silva Filho AG, dos Santos WP. Temporal and Spatiotemporal Arboviruses Forecasting by Machine Learning: A Systematic Review. Front Public Health 2022; 10:900077. [PMID: 35719644 PMCID: PMC9204152 DOI: 10.3389/fpubh.2022.900077] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2022] [Accepted: 05/03/2022] [Indexed: 11/13/2022] Open
Abstract
Arboviruses are a group of diseases that are transmitted by an arthropod vector. Since they are part of the Neglected Tropical Diseases that pose several public health challenges for countries around the world. The arboviruses' dynamics are governed by a combination of climatic, environmental, and human mobility factors. Arboviruses prediction models can be a support tool for decision-making by public health agents. In this study, we propose a systematic literature review to identify arboviruses prediction models, as well as models for their transmitter vector dynamics. To carry out this review, we searched reputable scientific bases such as IEE Xplore, PubMed, Science Direct, Springer Link, and Scopus. We search for studies published between the years 2015 and 2020, using a search string. A total of 429 articles were returned, however, after filtering by exclusion and inclusion criteria, 139 were included. Through this systematic review, it was possible to identify the challenges present in the construction of arboviruses prediction models, as well as the existing gap in the construction of spatiotemporal models.
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Affiliation(s)
- Clarisse Lins de Lima
- Nucleus for Computer Engineering, Polytechnique School of the University of Pernambuco, Poli-UPE, Recife, Brazil
| | - Ana Clara Gomes da Silva
- Nucleus for Computer Engineering, Polytechnique School of the University of Pernambuco, Poli-UPE, Recife, Brazil
| | | | | | - Anwar Musah
- Centre for Digital Public Health and Emergencies, Institute for Risk and Disaster Reduction, University College London, London, United Kingdom
| | - Aisha Aldosery
- Centre for Digital Public Health and Emergencies, Institute for Risk and Disaster Reduction, University College London, London, United Kingdom
| | - Livia Dutra
- Department of Atmospheric Sciences, IAG-USP, University of São Paulo, São Paulo, Brazil
| | - Tercio Ambrizzi
- Department of Atmospheric Sciences, IAG-USP, University of São Paulo, São Paulo, Brazil
| | - Iuri V. G. Borges
- Department of Atmospheric Sciences, IAG-USP, University of São Paulo, São Paulo, Brazil
| | - Merve Tunali
- Boǧaziçi University, Institute of Environmental Sciences, Istanbul, Turkey
| | - Selma Basibuyuk
- Boǧaziçi University, Institute of Environmental Sciences, Istanbul, Turkey
| | - Orhan Yenigün
- Boǧaziçi University, Institute of Environmental Sciences, Istanbul, Turkey
| | - Tiago Lima Massoni
- Department of Systems and Computing, Federal University of Campina Grande, Campina Grande, Brazil
| | - Ella Browning
- Centre for Biodiversity and Environment Research, Department of Genetics, Evolution and Environment, University College London, London, United Kingdom
| | - Kate Jones
- Centre for Biodiversity and Environment Research, Department of Genetics, Evolution and Environment, University College London, London, United Kingdom
| | - Luiza Campos
- Department of Civil Environmental and Geomatic Engineering, University College London, London, United Kingdom
| | - Patty Kostkova
- Centre for Digital Public Health and Emergencies, Institute for Risk and Disaster Reduction, University College London, London, United Kingdom
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Horst A, Smakaj E, Natali EN, Tosoni D, Babrak LM, Meier P, Miho E. Machine Learning Detects Anti-DENV Signatures in Antibody Repertoire Sequences. Front Artif Intell 2021; 4:715462. [PMID: 34708197 PMCID: PMC8542978 DOI: 10.3389/frai.2021.715462] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2021] [Accepted: 07/30/2021] [Indexed: 11/13/2022] Open
Abstract
Dengue infection is a global threat. As of today, there is no universal dengue fever treatment or vaccines unreservedly recommended by the World Health Organization. The investigation of the specific immune response to dengue virus would support antibody discovery as therapeutics for passive immunization and vaccine design. High-throughput sequencing enables the identification of the multitude of antibodies elicited in response to dengue infection at the sequence level. Artificial intelligence can mine the complex data generated and has the potential to uncover patterns in entire antibody repertoires and detect signatures distinctive of single virus-binding antibodies. However, these machine learning have not been harnessed to determine the immune response to dengue virus. In order to enable the application of machine learning, we have benchmarked existing methods for encoding biological and chemical knowledge as inputs and have investigated novel encoding techniques. We have applied different machine learning methods such as neural networks, random forests, and support vector machines and have investigated the parameter space to determine best performing algorithms for the detection and prediction of antibody patterns at the repertoire and antibody sequence levels in dengue-infected individuals. Our results show that immune response signatures to dengue are detectable both at the antibody repertoire and at the antibody sequence levels. By combining machine learning with phylogenies and network analysis, we generated novel sequences that present dengue-binding specific signatures. These results might aid further antibody discovery and support vaccine design.
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Affiliation(s)
- Alexander Horst
- School of Life Sciences, Institute of Medical Engineering and Medical Informatics, University of Applied Sciences and Arts Northwestern Switzerland FHNW, Muttenz, Switzerland
| | - Erand Smakaj
- School of Life Sciences, Institute of Medical Engineering and Medical Informatics, University of Applied Sciences and Arts Northwestern Switzerland FHNW, Muttenz, Switzerland
| | - Eriberto Noel Natali
- School of Life Sciences, Institute of Medical Engineering and Medical Informatics, University of Applied Sciences and Arts Northwestern Switzerland FHNW, Muttenz, Switzerland
| | - Deniz Tosoni
- School of Life Sciences, Institute of Medical Engineering and Medical Informatics, University of Applied Sciences and Arts Northwestern Switzerland FHNW, Muttenz, Switzerland
| | - Lmar Marie Babrak
- School of Life Sciences, Institute of Medical Engineering and Medical Informatics, University of Applied Sciences and Arts Northwestern Switzerland FHNW, Muttenz, Switzerland
| | - Patrick Meier
- School of Life Sciences, Institute of Medical Engineering and Medical Informatics, University of Applied Sciences and Arts Northwestern Switzerland FHNW, Muttenz, Switzerland
| | - Enkelejda Miho
- School of Life Sciences, Institute of Medical Engineering and Medical Informatics, University of Applied Sciences and Arts Northwestern Switzerland FHNW, Muttenz, Switzerland.,SIB Swiss Institute of Bioinformatics, Lausanne, Switzerland.,aiNET GmbH, Basel, Switzerland
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9
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Mello-Román JD, Mello-Román JC, Gómez-Guerrero S, García-Torres M. Predictive Models for the Medical Diagnosis of Dengue: A Case Study in Paraguay. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2019; 2019:7307803. [PMID: 31485259 PMCID: PMC6702853 DOI: 10.1155/2019/7307803] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/30/2018] [Accepted: 05/26/2019] [Indexed: 01/22/2023]
Abstract
Early diagnosis of dengue continues to be a concern for public health in countries with a high incidence of this disease. In this work, we compared two machine learning techniques: artificial neural networks (ANN) and support vector machines (SVM) as assistance tools for medical diagnosis. The performance of classification models was evaluated in a real dataset of patients with a previous diagnosis of dengue extracted from the public health system of Paraguay during the period 2012-2016. The ANN multilayer perceptron achieved better results with an average of 96% accuracy, 96% sensitivity, and 97% specificity, with low variation in thirty different partitions of the dataset. In comparison, SVM polynomial obtained results above 90% for accuracy, sensitivity, and specificity.
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Khalil SF, Mohktar MS, Ibrahim F. Bioimpedance Vector Analysis in Diagnosing Severe and Non-Severe Dengue Patients. SENSORS 2016; 16:s16060911. [PMID: 27322285 PMCID: PMC4934337 DOI: 10.3390/s16060911] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/06/2016] [Revised: 05/17/2016] [Accepted: 05/18/2016] [Indexed: 12/31/2022]
Abstract
Real-time monitoring and precise diagnosis of the severity of Dengue infection is needed for better decisions in disease management. The aim of this study is to use the Bioimpedance Vector Analysis (BIVA) method to differentiate between healthy subjects and severe and non-severe Dengue-infected patients. Bioimpedance was measured using a 50 KHz single-frequency bioimpedance analyzer. Data from 299 healthy subjects (124 males and 175 females) and 205 serologically confirmed Dengue patients (123 males and 82 females) were analyzed in this study. The obtained results show that the BIVA method was able to assess and classify the body fluid and cell mass condition between the healthy subjects and the Dengue-infected patients. The bioimpedance mean vectors (95% confidence ellipse) for healthy subjects, severe and non-severe Dengue-infected patients were illustrated. The vector is significantly shortened from healthy subjects to Dengue patients; for both genders the p-value is less than 0.0001. The mean vector of severe Dengue patients is significantly shortened compare to non-severe patients with a p-value of 0.0037 and 0.0023 for males and females, respectively. This study confirms that the BIVA method is a valid method in differentiating the healthy, severe and non-severe Dengue-infected subjects. All tests performed had a significance level with a p-value less than 0.05.
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Affiliation(s)
- Sami F Khalil
- Department of Biomedical Engineering, Faculty of Engineering, University of Malaya, 50603 Kuala Lumpur, Malaysia.
- Centre for Innovation in Medical Engineering (CIME), Faculty of Engineering, University of Malaya, 50603 Kuala Lumpur, Malaysia.
- Department of Biomedical Engineering, College of Engineering, Sudan University of Science and Technology, 407 Khartoum, Sudan.
| | - Mas S Mohktar
- Department of Biomedical Engineering, Faculty of Engineering, University of Malaya, 50603 Kuala Lumpur, Malaysia.
- Centre for Innovation in Medical Engineering (CIME), Faculty of Engineering, University of Malaya, 50603 Kuala Lumpur, Malaysia.
| | - Fatimah Ibrahim
- Department of Biomedical Engineering, Faculty of Engineering, University of Malaya, 50603 Kuala Lumpur, Malaysia.
- Centre for Innovation in Medical Engineering (CIME), Faculty of Engineering, University of Malaya, 50603 Kuala Lumpur, Malaysia.
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12
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Ibrahim F, Thio THG, Faisal T, Neuman M. The application of biomedical engineering techniques to the diagnosis and management of tropical diseases: a review. SENSORS 2015; 15:6947-95. [PMID: 25806872 PMCID: PMC4435123 DOI: 10.3390/s150306947] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/07/2014] [Revised: 12/05/2014] [Accepted: 01/07/2015] [Indexed: 11/18/2022]
Abstract
This paper reviews a number of biomedical engineering approaches to help aid in the detection and treatment of tropical diseases such as dengue, malaria, cholera, schistosomiasis, lymphatic filariasis, ebola, leprosy, leishmaniasis, and American trypanosomiasis (Chagas). Many different forms of non-invasive approaches such as ultrasound, echocardiography and electrocardiography, bioelectrical impedance, optical detection, simplified and rapid serological tests such as lab-on-chip and micro-/nano-fluidic platforms and medical support systems such as artificial intelligence clinical support systems are discussed. The paper also reviewed the novel clinical diagnosis and management systems using artificial intelligence and bioelectrical impedance techniques for dengue clinical applications.
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Affiliation(s)
- Fatimah Ibrahim
- Department of Biomedical Engineering, Faculty of Engineering, University of Malaya, 50603 Kuala Lumpur, Malaysia.
- Centre for Innovation in Medical Engineering (CIME), Faculty of Engineering, University of Malaya, 50603 Kuala Lumpur, Malaysia.
| | - Tzer Hwai Gilbert Thio
- Department of Biomedical Engineering, Faculty of Engineering, University of Malaya, 50603 Kuala Lumpur, Malaysia.
- Centre for Innovation in Medical Engineering (CIME), Faculty of Engineering, University of Malaya, 50603 Kuala Lumpur, Malaysia.
- Faculty of Science, Technology, Engineering and Mathematics, INTI International University, 71800 Nilai, Negeri Sembilan, Malaysia.
| | - Tarig Faisal
- Department of Biomedical Engineering, Faculty of Engineering, University of Malaya, 50603 Kuala Lumpur, Malaysia.
- Centre for Innovation in Medical Engineering (CIME), Faculty of Engineering, University of Malaya, 50603 Kuala Lumpur, Malaysia.
- Faculty-Electronics Engineering, Ruwais College, Higher Colleges of Technology, Ruwais, P.O Box 12389, UAE.
| | - Michael Neuman
- Department of Biomedical Engineering, Michigan Technological University, Houghton, MI 49931, USA.
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13
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Jahidin AH, Megat Ali MSA, Taib MN, Tahir NM, Yassin IM, Lias S. Classification of intelligence quotient via brainwave sub-band power ratio features and artificial neural network. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2014; 114:50-59. [PMID: 24560277 DOI: 10.1016/j.cmpb.2014.01.016] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/06/2013] [Revised: 01/21/2014] [Accepted: 01/23/2014] [Indexed: 06/03/2023]
Abstract
This paper elaborates on the novel intelligence assessment method using the brainwave sub-band power ratio features. The study focuses only on the left hemisphere brainwave in its relaxed state. Distinct intelligence quotient groups have been established earlier from the score of the Raven Progressive Matrices. Sub-band power ratios are calculated from energy spectral density of theta, alpha and beta frequency bands. Synthetic data have been generated to increase dataset from 50 to 120. The features are used as input to the artificial neural network. Subsequently, the brain behaviour model has been developed using an artificial neural network that is trained with optimized learning rate, momentum constant and hidden nodes. Findings indicate that the distinct intelligence quotient groups can be classified from the brainwave sub-band power ratios with 100% training and 88.89% testing accuracies.
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Affiliation(s)
- A H Jahidin
- Faculty of Electrical Engineering, Universiti Teknologi MARA, 40450 Shah Alam, Selangor, Malaysia.
| | - M S A Megat Ali
- Faculty of Electrical Engineering, Universiti Teknologi MARA, 40450 Shah Alam, Selangor, Malaysia
| | - M N Taib
- Faculty of Electrical Engineering, Universiti Teknologi MARA, 40450 Shah Alam, Selangor, Malaysia
| | - N Md Tahir
- Faculty of Electrical Engineering, Universiti Teknologi MARA, 40450 Shah Alam, Selangor, Malaysia
| | - I M Yassin
- Faculty of Electrical Engineering, Universiti Teknologi MARA, 40450 Shah Alam, Selangor, Malaysia
| | - S Lias
- Faculty of Electrical Engineering, Universiti Teknologi MARA, 40450 Shah Alam, Selangor, Malaysia
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14
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García-Garaluz E, Atencia M, Joya G, García-Lagos F, Sandoval F. Hopfield networks for identification of delay differential equations with an application to dengue fever epidemics in Cuba. Neurocomputing 2011. [DOI: 10.1016/j.neucom.2011.03.022] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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15
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Ibrahim F, Faisal T, Salim MIM, Taib MN. Non-invasive diagnosis of risk in dengue patients using bioelectrical impedance analysis and artificial neural network. Med Biol Eng Comput 2010; 48:1141-8. [PMID: 20683676 DOI: 10.1007/s11517-010-0669-z] [Citation(s) in RCA: 20] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2009] [Accepted: 07/09/2010] [Indexed: 01/02/2023]
Abstract
This paper presents a new approach to diagnose and classify early risk in dengue patients using bioelectrical impedance analysis (BIA) and artificial neural network (ANN). A total of 223 healthy subjects and 207 hospitalized dengue patients were prospectively studied. The dengue risk severity criteria was determined and grouped based on three blood investigations, namely, platelet (PLT) count (less than or equal to 30,000 cells per mm(3)), hematocrit (HCT) (increase by more than or equal to 20%), and either aspartate aminotransferase (AST) level (raised by fivefold the normal upper limit) or alanine aminotransferase (ALT) level (raised by fivefold the normal upper limit). The dengue patients were classified according to their risk groups and the corresponding BIA parameters were subsequently obtained and quantified. Four parameters were used for training and testing the ANN which are day of fever, reactance, gender, and risk group's quantification. Day of fever was defined as the day of fever subsided, i.e., when the body temperature fell below 37.5°C. The blood investigation and the BIA data were taken for 5 days. The ANN was trained via the steepest descent back propagation with momentum algorithm using the log-sigmoid transfer function while the sum-squared error was used as the network's performance indicator. The best ANN architecture of 3-6-1 (3 inputs, 6 neurons in the hidden layer, and 1 output), learning rate of 0.1, momentum constant of 0.2, and iteration rate of 20,000 was pruned using a weight-eliminating method. Eliminating a weight of 0.05 enhances the dengue's prediction risk classification accuracy of 95.88% for high risk and 96.83% for low risk groups. As a result, the system is able to classify and diagnose the risk in the dengue patients with an overall prediction accuracy of 96.27%.
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Affiliation(s)
- F Ibrahim
- Department of Biomedical Engineering, Faculty of Engineering, University of Malaya, 50603, Kuala Lumpur, Malaysia.
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16
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Neural network diagnostic system for dengue patients risk classification. J Med Syst 2010; 36:661-76. [PMID: 20703665 DOI: 10.1007/s10916-010-9532-x] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2010] [Accepted: 05/17/2010] [Indexed: 01/12/2023]
Abstract
With the dramatic increase of the worldwide threat of dengue disease, it has been very crucial to correctly diagnose the dengue patients in order to decrease the disease severity. However, it has been a great challenge for the physicians to identify the level of risk in dengue patients due to overlapping of the medical classification criteria. Therefore, this study aims to construct a noninvasive diagnostic system to assist the physicians for classifying the risk in dengue patients. Systematic producers have been followed to develop the system. Firstly, the assessment of the significant predictors associated with the level of risk in dengue patients was carried out utilizing the statistical analyses technique. Secondly, Multilayer perceptron neural network models trained via Levenberg-Marquardt and Scaled Conjugate Gradient algorithms was employed for constructing the diagnostic system. Finally, precise tuning for the models' parameters was conducted in order to achieve the optimal performance. As a result, 9 noninvasive predictors were found to be significantly associated with the level of risk in dengue patients. By employing those predictors, 75% prediction accuracy has been achieved for classifying the risk in dengue patients using Scaled Conjugate Gradient algorithm while 70.7% prediction accuracy were achieved by using Levenberg-Marquardt algorithm.
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17
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Faisal T, Ibrahim F, Taib MN. Analysis of significant factors for dengue infection prognosis using the self organizing map. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2009; 2008:5140-3. [PMID: 19163874 DOI: 10.1109/iembs.2008.4650371] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
This study presents a new approach to determine the significant prognosis factors in dengue patients utilizing the self-organizing map (SOM). SOM was used to visualize and determine the significant factors that can differentiate between the dengue patients and the healthy subjects. Bioimpedance analysis (BIA) parameters and symptoms/signs obtained from the 210 dengue patients during their hospitalization were used in this study. Database comprised of 329 sample (210 dengue patients and 119 healthy subjects) were used in the study. Accordingly, two maps were constructed. A total of 35 predictors (17 BIA parameters, 18 symptoms/signs) were investigated on the day of defervescence of fever. The first map was constructed based on BIA parameters while the second map utilized the symptoms and signs. The visualized results indicated that, the significant BIA prognosis factors for differentiating the dengue patients from the healthy subjects are reactance, intracellular water, ratio of the extracellular water and intracellular water, and ratio of the extracellular mass and body cell mass.
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Affiliation(s)
- Tarig Faisal
- Department of Biomedical Engineering, Faculty of Engineering, University of Malaya, Kuala Lumpur, 50603, Malaysia
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18
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Hapugoda MD, Batra G, Abeyewickreme W, Swaminathan S, Khanna N. Single antigen detects both immunoglobulin M (IgM) and IgG antibodies elicited by all four dengue virus serotypes. CLINICAL AND VACCINE IMMUNOLOGY : CVI 2007; 14:1505-14. [PMID: 17898184 PMCID: PMC2168164 DOI: 10.1128/cvi.00145-07] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
Abstract
The resurgence of dengue (DEN) virus infections in the last few decades coupled with the lack of a preventive vaccine and specific antiviral drugs has jointly contributed to making this a significant global public health problem. Currently, symptomatic supportive treatment and fluid replacement therapy are the only means available to minimize DEN-induced mortality. As the clinical symptoms associated with DEN virus infections are indistinguishable from those of many other viral, bacterial, and parasitic infections, specific diagnostic tests assume critical importance in the unequivocal identification of DEN virus infections. We have designed a novel chimeric antigen based on envelope domain III (EDIII), a critical antigenic region of the major structural protein of DEN viruses. We fused EDIIIs corresponding to each of the four DEN virus serotypes using pentaglycyl linkers, overexpressed the resultant tetravalent chimeric protein in Escherichia coli, and affinity purified it in high yields, obtaining approximately 30 mg protein of >95% purity per liter of culture. We show that this tetravalent antigen could specifically recognize anti-DEN virus antibodies of both the immunoglobulin M (IgM) and IgG classes. Using a large panel of IgM antibody capture-enzyme-linked immunosorbent assay- and hemagglutination inhibition-confirmed DEN virus-infected and uninfected patient sera (n = 289), we demonstrate that this tetravalent antigen can function as a diagnostic tool of high sensitivity and specificity.
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Affiliation(s)
- Menaka D Hapugoda
- Recombinant Gene Products Group, International Centre for Genetic Engineering and Biotechnology, P.O. Box 10504, Aruna Asaf Ali Marg, New Delhi 110067, India
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19
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Abstract
The need for models of dengue disease has reached a pinnacle as the transmission of this mosquito-borne virus has increased dramatically. Little is known about the mechanisms that lead to dengue fever and its more severe form, dengue hemorrhagic fever; this is owing to the fact that only humans show signs of disease. In the past 5 years, research has better identified the initial target cells of infection, and this has led to the development of models of infection in primary human cell cultures. Mouse-human chimeras, containing these target cells, have also led to progress in developing animal models. These advances should soon end the stalemate in testing antivirals and vaccine preparations that had necessarily been done in incomplete or irrelevant models.
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Affiliation(s)
- Dennis A Bente
- Southwest Foundation for Biomedical Research, 7620 NW Loop 410, San Antonio, TX 78227, USA
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