Pub Date : 2021-11-19DOI: 10.2174/18750362021140100114
V. Shymanskyi, Yaroslav Sokolovskyy
The aim of this study was to develop the mathematical models of the linear elasticity theory of biomaterials by taking into account their fractal structure. This study further aimed to construct a variational formulation of the problem, obtain the main relationships of the finite element method to calculate the rheological characteristics of a biomaterial with a fractal structure, and develop application software for calculating the components of the stress-strain state of biomaterials while considering their fractal structure. The obtained results were analyzed. The development of adequate mathematical models of the linear elasticity theory for biomaterials with a fractal structure is an urgent scientific task. Finding its solution will make it possible to analyze the rheological behavior of biomaterials exposed to external loads by taking into account the existing effects of memory, spatial non-locality, self-organization, and deterministic chaos in the material. The objective of this study was the deformation process of biomaterials with a fractal structure under external load. The equations of the linear elasticity theory for the construction of the mathematical models of the deformation process of biomaterials under external load were used. Mathematical apparatus of integro-differentiation of fractional order to take into account the fractal structure of the biomaterial was used. A variational formulation of the linear elasticity problem while taking into account the fractal structure of the biomaterial was formulated. The finite element method with a piecewise linear basis for finding an approximate solution to the problem was used. The main relations of the linear elasticity problem, which takes into account the fractal structure of the biomaterial, were obtained. A variational formulation of the problem was constructed. The main relations of the finite-element calculation of the linear elasticity problem of a biomaterial with a fractal structure using a piecewise-linear basis are found. The main components of the stress-strain state of the biomaterial exposed to external loads are found. Using the mathematical apparatus of integro-differentiation of fractional order in the construction of the mathematical models of the deformation process of biomaterials with a fractal structure makes it possible to take into account the existing effects of memory, spatial non-locality, self-organization, and deterministic chaos in the material. Also, this approach makes it possible to determine the residual stresses in the biomaterial, which play an important role in the appearance of stresses during repeated loads.
{"title":"Finite Element Calculation of the Linear Elasticity Problem for Biomaterials with Fractal Structure","authors":"V. Shymanskyi, Yaroslav Sokolovskyy","doi":"10.2174/18750362021140100114","DOIUrl":"https://doi.org/10.2174/18750362021140100114","url":null,"abstract":"\u0000 \u0000 The aim of this study was to develop the mathematical models of the linear elasticity theory of biomaterials by taking into account their fractal structure. This study further aimed to construct a variational formulation of the problem, obtain the main relationships of the finite element method to calculate the rheological characteristics of a biomaterial with a fractal structure, and develop application software for calculating the components of the stress-strain state of biomaterials while considering their fractal structure. The obtained results were analyzed.\u0000 \u0000 \u0000 \u0000 The development of adequate mathematical models of the linear elasticity theory for biomaterials with a fractal structure is an urgent scientific task. Finding its solution will make it possible to analyze the rheological behavior of biomaterials exposed to external loads by taking into account the existing effects of memory, spatial non-locality, self-organization, and deterministic chaos in the material.\u0000 \u0000 \u0000 \u0000 The objective of this study was the deformation process of biomaterials with a fractal structure under external load.\u0000 \u0000 \u0000 \u0000 The equations of the linear elasticity theory for the construction of the mathematical models of the deformation process of biomaterials under external load were used. Mathematical apparatus of integro-differentiation of fractional order to take into account the fractal structure of the biomaterial was used. A variational formulation of the linear elasticity problem while taking into account the fractal structure of the biomaterial was formulated. The finite element method with a piecewise linear basis for finding an approximate solution to the problem was used.\u0000 \u0000 \u0000 \u0000 The main relations of the linear elasticity problem, which takes into account the fractal structure of the biomaterial, were obtained. A variational formulation of the problem was constructed. The main relations of the finite-element calculation of the linear elasticity problem of a biomaterial with a fractal structure using a piecewise-linear basis are found. The main components of the stress-strain state of the biomaterial exposed to external loads are found.\u0000 \u0000 \u0000 \u0000 Using the mathematical apparatus of integro-differentiation of fractional order in the construction of the mathematical models of the deformation process of biomaterials with a fractal structure makes it possible to take into account the existing effects of memory, spatial non-locality, self-organization, and deterministic chaos in the material. Also, this approach makes it possible to determine the residual stresses in the biomaterial, which play an important role in the appearance of stresses during repeated loads.\u0000","PeriodicalId":38956,"journal":{"name":"Open Bioinformatics Journal","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46021052","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-11-19DOI: 10.2174/1875036202114010073
I. Lytvynenko, S. Lupenko, Petro Onyskiv, A. Zozulia
We have developed a new approach to the study of human heart rate, which is based on the use of a vector rhythmocardiosignal, which includes as its component the classical rhythmocardiosignal in the form of a sequence of heart cycle durations in an electrocardiogram. Most modern automated heart rate analysis systems are based on a statistical analysis of the rhythmocardiogram, which is an ordered set of R-R interval durations in a recorded electrocardiogram. However, this approach is not very informative, since R-R intervals reflect only the change in the duration of cardiac cycles over time and not the entire set of time intervals between single-phase values of the electrocardiosignal for all its phases. The aim of this paper is to present a mathematical model in the form of a vector of stationary and permanently connected random sequences of a rhythmocardiosignal with an increased resolution for its processing problems. It shows how the vector rhythmocardiosignal is formed and processed in diagnostic systems. The structure of probabilistic characteristics of this model is recorded for statistical analysis of heart rate in modern cardiodiagnostics systems. Based on a new mathematical model of a vector rhythmocardiosignal in the form of a vector of stationary and permanently connected random sequences, new methods for statistical estimation of spectral-correlation characteristics of heart rate with increased resolution have been developed. The spectral power densities of the components of the vector rhythmocardiosignal are justified as new diagnostic features when performing rhythm analysis in modern cardiodiagnostics systems, complementing the known signs and increasing the informative value of heart rate analysis in modern cardiodiagnostics systems. The structure of probabilistic characteristics of the proposed mathematical model for heart rate analysis in modern cardiodiagnostics systems is studied. It is shown how the vector rhythmocardiosignal is formed, and its statistical processing is carried out on the basis of the proposed mathematical model and developed methods.
{"title":"Modeling and Methods of Statistical Processing of a Vector Rhytmocardiosignal","authors":"I. Lytvynenko, S. Lupenko, Petro Onyskiv, A. Zozulia","doi":"10.2174/1875036202114010073","DOIUrl":"https://doi.org/10.2174/1875036202114010073","url":null,"abstract":"\u0000 \u0000 We have developed a new approach to the study of human heart rate, which is based on the use of a vector rhythmocardiosignal, which includes as its component the classical rhythmocardiosignal in the form of a sequence of heart cycle durations in an electrocardiogram.\u0000 \u0000 \u0000 \u0000 Most modern automated heart rate analysis systems are based on a statistical analysis of the rhythmocardiogram, which is an ordered set of R-R interval durations in a recorded electrocardiogram. However, this approach is not very informative, since R-R intervals reflect only the change in the duration of cardiac cycles over time and not the entire set of time intervals between single-phase values of the electrocardiosignal for all its phases.\u0000 \u0000 \u0000 \u0000 The aim of this paper is to present a mathematical model in the form of a vector of stationary and permanently connected random sequences of a rhythmocardiosignal with an increased resolution for its processing problems. It shows how the vector rhythmocardiosignal is formed and processed in diagnostic systems. The structure of probabilistic characteristics of this model is recorded for statistical analysis of heart rate in modern cardiodiagnostics systems.\u0000 \u0000 \u0000 \u0000 Based on a new mathematical model of a vector rhythmocardiosignal in the form of a vector of stationary and permanently connected random sequences, new methods for statistical estimation of spectral-correlation characteristics of heart rate with increased resolution have been developed.\u0000 \u0000 \u0000 \u0000 The spectral power densities of the components of the vector rhythmocardiosignal are justified as new diagnostic features when performing rhythm analysis in modern cardiodiagnostics systems, complementing the known signs and increasing the informative value of heart rate analysis in modern cardiodiagnostics systems.\u0000 \u0000 \u0000 \u0000 The structure of probabilistic characteristics of the proposed mathematical model for heart rate analysis in modern cardiodiagnostics systems is studied. It is shown how the vector rhythmocardiosignal is formed, and its statistical processing is carried out on the basis of the proposed mathematical model and developed methods.\u0000","PeriodicalId":38956,"journal":{"name":"Open Bioinformatics Journal","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48829190","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-11-19DOI: 10.2174/18750362021140100123
Yevgeniy V. Bodyanskiy, A. Deineko, I. Pliss, O. Chala
The medical diagnostic task in conditions of the limited dataset and overlapping classes is considered. Such limitations happen quite often in real-world tasks. The lack of long training datasets during solving real tasks in the problem of medical diagnostics causes not being able to use the mathematical apparatus of deep learning. Additionally, considering other factors, such as in a dataset, classes can be overlapped in the feature space; also data can be specified in various scales: in the numerical interval, numerical ratios, ordinal (rank), nominal and binary, which does not allow the use of known neural networks. In order to overcome arising restrictions and problems, a hybrid neuro-fuzzy system based on a probabilistic neural network and adaptive neuro-fuzzy interference system that allows solving the task in these situations is proposed. Computational intelligence, artificial neural networks, neuro-fuzzy systems compared to conventional artificial neural networks, the proposed system requires significantly less training time, and in comparison with neuro-fuzzy systems, it contains significantly fewer membership functions in the fuzzification layer. The hybrid learning algorithm for the system under consideration based on self-learning according to the principle “Winner takes all” and lazy learning according to the principle “Neurons at data points” has been introduced. The proposed system solves the problem of classification in conditions of overlapping classes with the calculation of the membership levels of the formed diagnosis to various possible classes. The proposed system is quite simple in its numerical implementation, characterized by a high speed of information processing, both in the learning process and in the decision-making process; it easily adapts to situations when the number of diagnostics features changes during the system's functioning.
{"title":"Adaptive Probabilistic Neuro-Fuzzy System and its Hybrid Learning in Medical Diagnostics Task","authors":"Yevgeniy V. Bodyanskiy, A. Deineko, I. Pliss, O. Chala","doi":"10.2174/18750362021140100123","DOIUrl":"https://doi.org/10.2174/18750362021140100123","url":null,"abstract":"\u0000 \u0000 The medical diagnostic task in conditions of the limited dataset and overlapping classes is considered. Such limitations happen quite often in real-world tasks. The lack of long training datasets during solving real tasks in the problem of medical diagnostics causes not being able to use the mathematical apparatus of deep learning. Additionally, considering other factors, such as in a dataset, classes can be overlapped in the feature space; also data can be specified in various scales: in the numerical interval, numerical ratios, ordinal (rank), nominal and binary, which does not allow the use of known neural networks. In order to overcome arising restrictions and problems, a hybrid neuro-fuzzy system based on a probabilistic neural network and adaptive neuro-fuzzy interference system that allows solving the task in these situations is proposed.\u0000 \u0000 \u0000 \u0000 \u0000 Computational intelligence, artificial neural networks, neuro-fuzzy systems compared to conventional artificial neural networks, the proposed system requires significantly less training time, and in comparison with neuro-fuzzy systems, it contains significantly fewer membership functions in the fuzzification layer. The hybrid learning algorithm for the system under consideration based on self-learning according to the principle “Winner takes all” and lazy learning according to the principle “Neurons at data points” has been introduced.\u0000 \u0000 \u0000 \u0000 The proposed system solves the problem of classification in conditions of overlapping classes with the calculation of the membership levels of the formed diagnosis to various possible classes.\u0000 \u0000 \u0000 \u0000 The proposed system is quite simple in its numerical implementation, characterized by a high speed of information processing, both in the learning process and in the decision-making process; it easily adapts to situations when the number of diagnostics features changes during the system's functioning.\u0000","PeriodicalId":38956,"journal":{"name":"Open Bioinformatics Journal","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43665182","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-11-19DOI: 10.2174/1875036202114010093
Pavlo Radiuk, O. Barmak, I. Krak
This study investigates the topology of convolutional neural networks and proposes an information technology for the early detection of pneumonia in X-rays. For the past decade, pneumonia has been one of the most widespread respiratory diseases. Every year, a significant part of the world's population suffers from pneumonia, which leads to millions of deaths worldwide. Inflammation occurs rapidly and usually proceeds in severe forms. Thus, early detection of the disease plays a critical role in its successful treatment. The most operating means of diagnosing pneumonia is the chest X-ray, which produces radiographs. Automated diagnostics using computing devices and computer vision techniques have become beneficial in X-ray image analysis, serving as an ancillary decision-making system. Nonetheless, such systems require continuous improvement for individual patient adjustment to ensure a successful, timely diagnosis. Nowadays, artificial neural networks serve as a promising solution for identifying pneumonia in radiographs. Despite the high level of recognition accuracy, neural networks have been perceived as black boxes because of the unclear interpretation of their performance results. Altogether, an insufficient explanation for the early diagnosis can be perceived as a severe negative feature of automated decision-making systems, as the lack of interpretation results may negatively affect the final clinical decision. To address this issue, we propose an approach to the automated diagnosis of early pneumonia, based on the classification of radiographs with weakly expressed disease features. An effective spatial convolution operation with several dilated rates, combining various receptive feature fields, was used in convolutional layers to detect and analyze visual deviations in the X-ray image. Due to applying the dilated convolution operation, the network avoids significant losses of objects' spatial information providing relatively low computational costs. We also used transfer training to overcome the lack of data in the early diagnosis of pneumonia. An image analysis strategy based on class activation maps was used to interpret the classification results, critical for clinical decision making. According to the computational results, the proposed convolutional architecture may be an excellent solution for instant diagnosis in case of the first suspicion of early pneumonia.
{"title":"An Approach to Early Diagnosis of Pneumonia on Individual Radiographs based on the CNN Information Technology","authors":"Pavlo Radiuk, O. Barmak, I. Krak","doi":"10.2174/1875036202114010093","DOIUrl":"https://doi.org/10.2174/1875036202114010093","url":null,"abstract":"\u0000 \u0000 This study investigates the topology of convolutional neural networks and proposes an information technology for the early detection of pneumonia in X-rays.\u0000 \u0000 \u0000 \u0000 For the past decade, pneumonia has been one of the most widespread respiratory diseases. Every year, a significant part of the world's population suffers from pneumonia, which leads to millions of deaths worldwide. Inflammation occurs rapidly and usually proceeds in severe forms. Thus, early detection of the disease plays a critical role in its successful treatment.\u0000 \u0000 \u0000 \u0000 The most operating means of diagnosing pneumonia is the chest X-ray, which produces radiographs. Automated diagnostics using computing devices and computer vision techniques have become beneficial in X-ray image analysis, serving as an ancillary decision-making system. Nonetheless, such systems require continuous improvement for individual patient adjustment to ensure a successful, timely diagnosis.\u0000 \u0000 \u0000 \u0000 Nowadays, artificial neural networks serve as a promising solution for identifying pneumonia in radiographs. Despite the high level of recognition accuracy, neural networks have been perceived as black boxes because of the unclear interpretation of their performance results. Altogether, an insufficient explanation for the early diagnosis can be perceived as a severe negative feature of automated decision-making systems, as the lack of interpretation results may negatively affect the final clinical decision. To address this issue, we propose an approach to the automated diagnosis of early pneumonia, based on the classification of radiographs with weakly expressed disease features.\u0000 \u0000 \u0000 \u0000 An effective spatial convolution operation with several dilated rates, combining various receptive feature fields, was used in convolutional layers to detect and analyze visual deviations in the X-ray image. Due to applying the dilated convolution operation, the network avoids significant losses of objects' spatial information providing relatively low computational costs. We also used transfer training to overcome the lack of data in the early diagnosis of pneumonia. An image analysis strategy based on class activation maps was used to interpret the classification results, critical for clinical decision making.\u0000 \u0000 \u0000 \u0000 According to the computational results, the proposed convolutional architecture may be an excellent solution for instant diagnosis in case of the first suspicion of early pneumonia.\u0000","PeriodicalId":38956,"journal":{"name":"Open Bioinformatics Journal","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43909063","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-11-02DOI: 10.2174/1875036202114010021
Yesenia Cevallos, T. Nakano, Luis Tello-Oquendo, Deysi Inca, Ivone Santillán, A. Shirazi, A. Rushdi, Nicolay Samaniego
Digital communication theories have been well-established and extensively used to model and analyze information transfer and exchange processes. Due to their robustness and thoroughness, they have been recently extended to the modeling and analyzing data flow, storage, and networking in biological systems. This article analyses gene expression from a digital communication system perspective. Specifically, network theories, such as addressing, error control, flow control, traffic control, and Shannon's theorem are used to design an end-to-end digital communication system representing gene expression. We provide a layered network model representing the transcription and translation of deoxyribonucleic acid (DNA) and the end-to-end transmission of proteins to a target organ. The layered network model takes advantage of digital communication systems' key features, such as efficiency and performance, to transmit biological information in gene expression systems. Thus, we define the transmission of information through a bio-internetwork (LAN-WAN-LAN) composed of a transmitter network (nucleus of the cell, ribosomes and endoplasmic reticulum), a router (Golgi Apparatus), and a receiver network (target organ). Our proposal can be applied in critical scenarios such as the development of communication systems for medical purposes. For instance, in cancer treatment, the model and analysis presented in this article may help understand side effects due to the transmission of drug molecules to a target organ to achieve optimal treatments.
{"title":"Modeling Gene Expression and Protein Delivery as an End-to-End Digital Communication System","authors":"Yesenia Cevallos, T. Nakano, Luis Tello-Oquendo, Deysi Inca, Ivone Santillán, A. Shirazi, A. Rushdi, Nicolay Samaniego","doi":"10.2174/1875036202114010021","DOIUrl":"https://doi.org/10.2174/1875036202114010021","url":null,"abstract":"\u0000 \u0000 Digital communication theories have been well-established and extensively used to model and analyze information transfer and exchange processes. Due to their robustness and thoroughness, they have been recently extended to the modeling and analyzing data flow, storage, and networking in biological systems.\u0000 \u0000 \u0000 \u0000 This article analyses gene expression from a digital communication system perspective. Specifically, network theories, such as addressing, error control, flow control, traffic control, and Shannon's theorem are used to design an end-to-end digital communication system representing gene expression. We provide a layered network model representing the transcription and translation of deoxyribonucleic acid (DNA) and the end-to-end transmission of proteins to a target organ. The layered network model takes advantage of digital communication systems' key features, such as efficiency and performance, to transmit biological information in gene expression systems.\u0000 \u0000 \u0000 \u0000 Thus, we define the transmission of information through a bio-internetwork (LAN-WAN-LAN) composed of a transmitter network (nucleus of the cell, ribosomes and endoplasmic reticulum), a router (Golgi Apparatus), and a receiver network (target organ).\u0000 \u0000 \u0000 \u0000 Our proposal can be applied in critical scenarios such as the development of communication systems for medical purposes. For instance, in cancer treatment, the model and analysis presented in this article may help understand side effects due to the transmission of drug molecules to a target organ to achieve optimal treatments.\u0000","PeriodicalId":38956,"journal":{"name":"Open Bioinformatics Journal","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46629626","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-07-14DOI: 10.1101/2021.07.12.21260298
S. Hashim, S. Farooq, E. Syriopoulos, K. D. Cremer, A. Vogt, N. de Jong, V. Aguado, M. Popescu, A. Mohamed, M. Amin
The COVID-19 pandemic has presented a series of new challenges to governments and health care systems. Testing is one important method for monitoring and therefore controlling the spread of COVID-19. Yet with a serious discrepancy in the resources available between rich and poor countries not every country is able to employ widespread testing. Here we developed machine learning models for predicting the number of COVID-19 cases in a country based on multilinear regression and neural networks models. The models are trained on data from US states and tested against the reported infections in the European countries. The model is based on four features: Number of tests Population Percentage Urban Population and Gini index. The population and number of tests have the strongest correlation with the number of infections. The model was then tested on data from European countries for which the correlation coefficient between the actual and predicted cases R2 was found to be 0.88 in the multi linear regression and 0.91 for the neural network model. The model predicts that the actual number of infections in countries where the number of tests is less than 10% of their populations is at least 26 times greater than the reported numbers.
{"title":"Machine Learning Model for Predicting Number of COVID19 Cases in Countries with Low Number of Tests","authors":"S. Hashim, S. Farooq, E. Syriopoulos, K. D. Cremer, A. Vogt, N. de Jong, V. Aguado, M. Popescu, A. Mohamed, M. Amin","doi":"10.1101/2021.07.12.21260298","DOIUrl":"https://doi.org/10.1101/2021.07.12.21260298","url":null,"abstract":"The COVID-19 pandemic has presented a series of new challenges to governments and health care systems. Testing is one important method for monitoring and therefore controlling the spread of COVID-19. Yet with a serious discrepancy in the resources available between rich and poor countries not every country is able to employ widespread testing. Here we developed machine learning models for predicting the number of COVID-19 cases in a country based on multilinear regression and neural networks models. The models are trained on data from US states and tested against the reported infections in the European countries. The model is based on four features: Number of tests Population Percentage Urban Population and Gini index. The population and number of tests have the strongest correlation with the number of infections. The model was then tested on data from European countries for which the correlation coefficient between the actual and predicted cases R2 was found to be 0.88 in the multi linear regression and 0.91 for the neural network model. The model predicts that the actual number of infections in countries where the number of tests is less than 10% of their populations is at least 26 times greater than the reported numbers.","PeriodicalId":38956,"journal":{"name":"Open Bioinformatics Journal","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48265504","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-03-22DOI: 10.2174/1875036202114010001
Yuanchen Ma, Yinong Huang, Tao Wang, A. Xiang, Weijun Huang
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a lineage B coronavirus, causing the worldwide outbreak of Corona Virus Disease 2019 (COVID-19). Despite genetically closed to SARS-CoV, SARS-CoV-2 seems to possess enhanced infectivity and subtle different clinical features, which may hamper the early screening of suspected patients as well as the control of virus transmission. Unfortunately, there are few tools to predict the potential target organ damage and possible clinical manifestations caused by such novel coronavirus. To solve this problem, we use the online single-cell sequence datasets to analyze the expression of the major receptor in host cells that mediates the virus entry, including angiotensin converting enzyme 2 (ACE2), and its co-expressed membrane endopeptidases. The results indicated the differential expression of ADAM10 and ADAM17 might contribute to the ACE2 shedding and affect the membrane ACE2 abundance. We further confirm a putative furin-cleavage site reported recently in the spike protein of SARS-CoV-2, which may facilitate the virus-cell fusion. Based on these findings, we develop an approach that comprehensively analyzed the virus receptor expression, ACE2 shedding, membrane fusion activity, virus uptake and virus replication to evaluate the infectivity of SARS-CoV-2 to different human organs. Our results indicate that, in addition to airway epithelia, cardiac tissue and enteric canals are susceptible to SARS-CoV-2 as well.
{"title":"ACE2 Shedding and Furin Abundance in Target Organs may Influence the Efficiency of SARS-CoV-2 Entry","authors":"Yuanchen Ma, Yinong Huang, Tao Wang, A. Xiang, Weijun Huang","doi":"10.2174/1875036202114010001","DOIUrl":"https://doi.org/10.2174/1875036202114010001","url":null,"abstract":"\u0000 \u0000 Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a lineage B coronavirus, causing the worldwide outbreak of Corona Virus Disease 2019 (COVID-19). Despite genetically closed to SARS-CoV, SARS-CoV-2 seems to possess enhanced infectivity and subtle different clinical features, which may hamper the early screening of suspected patients as well as the control of virus transmission. Unfortunately, there are few tools to predict the potential target organ damage and possible clinical manifestations caused by such novel coronavirus.\u0000 \u0000 \u0000 \u0000 To solve this problem, we use the online single-cell sequence datasets to analyze the expression of the major receptor in host cells that mediates the virus entry, including angiotensin converting enzyme 2 (ACE2), and its co-expressed membrane endopeptidases.\u0000 \u0000 \u0000 \u0000 The results indicated the differential expression of ADAM10 and ADAM17 might contribute to the ACE2 shedding and affect the membrane ACE2 abundance. We further confirm a putative furin-cleavage site reported recently in the spike protein of SARS-CoV-2, which may facilitate the virus-cell fusion. Based on these findings, we develop an approach that comprehensively analyzed the virus receptor expression, ACE2 shedding, membrane fusion activity, virus uptake and virus replication to evaluate the infectivity of SARS-CoV-2 to different human organs.\u0000 \u0000 \u0000 \u0000 Our results indicate that, in addition to airway epithelia, cardiac tissue and enteric canals are susceptible to SARS-CoV-2 as well.\u0000","PeriodicalId":38956,"journal":{"name":"Open Bioinformatics Journal","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45361099","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-12-31DOI: 10.2174/1875036202013010137
M. Mielczarek, Bartosz Czech, J. Stanczyk, J. Szyda, B. Guldbrandtsen
The command line is a standard way of using the Linux operating system. It contains many features essential for efficiently handling data editing and analysis processes. Therefore, it is very useful in bioinformatics applications. Commands allow for rapid manipulation of large ASCII files or very numerous files, making basic command line programming skills a critical component in modern life science research. The following article is not a guide to Linux commands. In this manuscript, in contrast to many various Linux manuals, we aim to present basic command line tools helpful in handling biological sequence data. This manuscript provides a collection of simple and popular hacks dedicated to users with very basic experience in the area of the Linux command line. It includes a description of data formats and examples of editing of four types of data formats popular in bioinformatics applications.
{"title":"Extraordinary Command Line: Basic Data Editing Tools for Biologists Dealing with Sequence Data","authors":"M. Mielczarek, Bartosz Czech, J. Stanczyk, J. Szyda, B. Guldbrandtsen","doi":"10.2174/1875036202013010137","DOIUrl":"https://doi.org/10.2174/1875036202013010137","url":null,"abstract":"The command line is a standard way of using the Linux operating system. It contains many features essential for efficiently handling data editing and analysis processes. Therefore, it is very useful in bioinformatics applications. Commands allow for rapid manipulation of large ASCII files or very numerous files, making basic command line programming skills a critical component in modern life science research. The following article is not a guide to Linux commands. In this manuscript, in contrast to many various Linux manuals, we aim to present basic command line tools helpful in handling biological sequence data. This manuscript provides a collection of simple and popular hacks dedicated to users with very basic experience in the area of the Linux command line. It includes a description of data formats and examples of editing of four types of data formats popular in bioinformatics applications.","PeriodicalId":38956,"journal":{"name":"Open Bioinformatics Journal","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46872618","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-12-23DOI: 10.2174/1875036202013010119
N. K. Papathoti, Dusadee Kiddeejing, J. Daddam, Toan Le Thanh, N. Buensanteai
Spodoptera litura, otherwise known as cutworm, belongs to the Noctuidae tribe, which is a severe scourge for numerous crop systems and is considered one of Asian tropical agriculture's most important insects. The world's leading environmental threats are plant pests, and the already commercialized pesticides are extremely poisonous and non-biodegradable and maybe additional residues harmful to the ecosystem. The increased resistance in pests often demands the need for advanced, active pesticides that are environmentally friendly and biodegradable. In the current work, the significance of proteases for the Spodoptera litura digestive system has been determined by the use of microbial metabolite protease inhibitor (Iturin A) in silico models. In the present study, we developed a model based on sequence structural alignment of known crystal structure 2D1I protease from Homo sapiens. The model's reliability evaluation was performed using programs such as PROCHECK, WHAT IF, PROSA, Validate 3D, ERRAT, etc. In an attempt to find new inhibitors for Protease docking, the study was carried out with Iturin A. PMDB ID for the produced protease model was submitted to identify new inhibitors for Protease docking, and its accession number is PM0082285. The detailed study of enzyme-inhibitor interactions identified similar core residues; GLU215, LEU216, LYS217, and GLU237 have demonstrated their role in the binding efficacy of ligands. The latest homology modeling and docking experiments on the protease model will provide useful insight knowledge for the logical approach of constructing a wide spectrum of novel insecticide against Spodoptera.
{"title":"Identification of Protease Inhibition Mechanism by Iturin A against Agriculture Cutworm (Spodoptera litura) by Homology Modeling and Molecular Dynamics","authors":"N. K. Papathoti, Dusadee Kiddeejing, J. Daddam, Toan Le Thanh, N. Buensanteai","doi":"10.2174/1875036202013010119","DOIUrl":"https://doi.org/10.2174/1875036202013010119","url":null,"abstract":"\u0000 \u0000 Spodoptera litura, otherwise known as cutworm, belongs to the Noctuidae tribe, which is a severe scourge for numerous crop systems and is considered one of Asian tropical agriculture's most important insects. The world's leading environmental threats are plant pests, and the already commercialized pesticides are extremely poisonous and non-biodegradable and maybe additional residues harmful to the ecosystem. The increased resistance in pests often demands the need for advanced, active pesticides that are environmentally friendly and biodegradable.\u0000 \u0000 \u0000 In the current work, the significance of proteases for the Spodoptera litura digestive system has been determined by the use of microbial metabolite protease inhibitor (Iturin A) in silico models. In the present study, we developed a model based on sequence structural alignment of known crystal structure 2D1I protease from Homo sapiens. The model's reliability evaluation was performed using programs such as PROCHECK, WHAT IF, PROSA, Validate 3D, ERRAT, etc.\u0000 \u0000 \u0000 \u0000 In an attempt to find new inhibitors for Protease docking, the study was carried out with Iturin A. PMDB ID for the produced protease model was submitted to identify new inhibitors for Protease docking, and its accession number is PM0082285. The detailed study of enzyme-inhibitor interactions identified similar core residues; GLU215, LEU216, LYS217, and GLU237 have demonstrated their role in the binding efficacy of ligands.\u0000 \u0000 \u0000 The latest homology modeling and docking experiments on the protease model will provide useful insight knowledge for the logical approach of constructing a wide spectrum of novel insecticide against Spodoptera.\u0000","PeriodicalId":38956,"journal":{"name":"Open Bioinformatics Journal","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47419184","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-11-30DOI: 10.2174/1875036202013010106
Santisudha Panigrahi, T. Swarnkar
Oral diseases are the 6th most revealed malignancy happening in head and neck regions found mainly in south Asian countries. It is the most common cancer with fourteen deaths in an hour on a yearly basis, as per the WHO oral cancer incidence in India. Due to the cost of tests, mistakes in the recognition procedure, and the enormous remaining task at hand of the cytopathologist, oral growths cannot be diagnosed promptly. This area is open to be looked into by biomedical analysts to identify it at an early stage. At present, with the advent of entire slide computerized scanners and tissue histopathology, there is a gigantic aggregation of advanced digital histopathological images, which has prompted the necessity for their analysis. A lot of computer aided analysis techniques have been developed by utilizing machine learning strategies for prediction and prognosis of cancer. In this review paper, first various steps of obtaining histopathological images, followed by the visualization and classification done by the doctors are discussed. As machine learning techniques are well known, in the second part of this review, the works done for histopathological image analysis as well as other oral datasets using these strategies for growth prognosis and anticipation are discussed. Comparing the pitfalls of machine learning and how it has overcome by deep learning mostly for image recognition tasks are also discussed subsequently. The third part of the manuscript describes how deep learning is beneficial and widely used in different cancer domains. Due to the remarkable growth of deep learning and wide applicability, it is best suited for the prognosis of oral disease. The aim of this review is to provide insight to the researchers opting to work for oral cancer by implementing deep learning and artificial neural networks.
{"title":"Machine Learning Techniques used for the Histopathological Image Analysis of Oral Cancer-A Review","authors":"Santisudha Panigrahi, T. Swarnkar","doi":"10.2174/1875036202013010106","DOIUrl":"https://doi.org/10.2174/1875036202013010106","url":null,"abstract":"Oral diseases are the 6th most revealed malignancy happening in head and neck regions found mainly in south Asian countries. It is the most common cancer with fourteen deaths in an hour on a yearly basis, as per the WHO oral cancer incidence in India. Due to the cost of tests, mistakes in the recognition procedure, and the enormous remaining task at hand of the cytopathologist, oral growths cannot be diagnosed promptly. This area is open to be looked into by biomedical analysts to identify it at an early stage. At present, with the advent of entire slide computerized scanners and tissue histopathology, there is a gigantic aggregation of advanced digital histopathological images, which has prompted the necessity for their analysis. A lot of computer aided analysis techniques have been developed by utilizing machine learning strategies for prediction and prognosis of cancer. In this review paper, first various steps of obtaining histopathological images, followed by the visualization and classification done by the doctors are discussed. As machine learning techniques are well known, in the second part of this review, the works done for histopathological image analysis as well as other oral datasets using these strategies for growth prognosis and anticipation are discussed. Comparing the pitfalls of machine learning and how it has overcome by deep learning mostly for image recognition tasks are also discussed subsequently. The third part of the manuscript describes how deep learning is beneficial and widely used in different cancer domains. Due to the remarkable growth of deep learning and wide applicability, it is best suited for the prognosis of oral disease. The aim of this review is to provide insight to the researchers opting to work for oral cancer by implementing deep learning and artificial neural networks.","PeriodicalId":38956,"journal":{"name":"Open Bioinformatics Journal","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45892530","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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