Pub Date : 2021-10-25DOI: 10.1109/BIBE52308.2021.9635374
L. S. Becirovic, Amar Deumic, L. G. Pokvic, A. Badnjević
Machine learning algorithms have been drawing attention in lung disease research. However, due to their algorithmic learning complexity and the variability of their architecture, there is an ongoing need to analyze their performance. This study reviews the input parameters and the performance of machine learning applied to diagnosis of chronic obstructive pulmonary disease (COPD). One research focus of this study was on clearly identifying problems and issues related to the implementation of machine learning in clinical studies. Following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) protocol, 179, 1032, and 36,500 titles were identified from the PubMed, Scopus, and Google Scholar databases respectively. Studies that used machine learning to detect COPD and provided performance measures were included in our analysis. In the final analysis, 24 studies were included. The analysis of machine learning methods to detect COPD reveals the limited usage of the methods and the lack of standards that hinder the implementation of machine learning in clinical applications. The performance of machine learning for diagnosis of COPD was considered satisfactory for several studies; however, given the limitations indicated in our study, further studies are warranted to extend the potential use of machine learning to clinical settings.
{"title":"Aritificial Inteligence Challenges in COPD management: a review","authors":"L. S. Becirovic, Amar Deumic, L. G. Pokvic, A. Badnjević","doi":"10.1109/BIBE52308.2021.9635374","DOIUrl":"https://doi.org/10.1109/BIBE52308.2021.9635374","url":null,"abstract":"Machine learning algorithms have been drawing attention in lung disease research. However, due to their algorithmic learning complexity and the variability of their architecture, there is an ongoing need to analyze their performance. This study reviews the input parameters and the performance of machine learning applied to diagnosis of chronic obstructive pulmonary disease (COPD). One research focus of this study was on clearly identifying problems and issues related to the implementation of machine learning in clinical studies. Following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) protocol, 179, 1032, and 36,500 titles were identified from the PubMed, Scopus, and Google Scholar databases respectively. Studies that used machine learning to detect COPD and provided performance measures were included in our analysis. In the final analysis, 24 studies were included. The analysis of machine learning methods to detect COPD reveals the limited usage of the methods and the lack of standards that hinder the implementation of machine learning in clinical applications. The performance of machine learning for diagnosis of COPD was considered satisfactory for several studies; however, given the limitations indicated in our study, further studies are warranted to extend the potential use of machine learning to clinical settings.","PeriodicalId":343724,"journal":{"name":"2021 IEEE 21st International Conference on Bioinformatics and Bioengineering (BIBE)","volume":"57 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128412919","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 : 2021-10-25DOI: 10.1109/BIBE52308.2021.9635169
Miloš Anić, Momcilo Prodanovic, S. Milenkovic, Nenad D Filipović, N. Grujovic, F. Živić
This paper presents a short review of the piezoelectric materials in energy harvesting. Energy harvesting principle, as the method for obtaining energy from environment has been described. Materials and material combinations for creating an energy harvesting composites are discussed, such as ceramic- and polymer-based composites and their mechanical properties. The list of the mostly used piezoelectric materials is presented and elaborated. Possible applications of the energy harvesting materials are discussed, including nanogenerators, biosensors and biomedical applications.
{"title":"The Review of Materials for Energy Harvesting","authors":"Miloš Anić, Momcilo Prodanovic, S. Milenkovic, Nenad D Filipović, N. Grujovic, F. Živić","doi":"10.1109/BIBE52308.2021.9635169","DOIUrl":"https://doi.org/10.1109/BIBE52308.2021.9635169","url":null,"abstract":"This paper presents a short review of the piezoelectric materials in energy harvesting. Energy harvesting principle, as the method for obtaining energy from environment has been described. Materials and material combinations for creating an energy harvesting composites are discussed, such as ceramic- and polymer-based composites and their mechanical properties. The list of the mostly used piezoelectric materials is presented and elaborated. Possible applications of the energy harvesting materials are discussed, including nanogenerators, biosensors and biomedical applications.","PeriodicalId":343724,"journal":{"name":"2021 IEEE 21st International Conference on Bioinformatics and Bioengineering (BIBE)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125813567","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 : 2021-10-25DOI: 10.1109/BIBE52308.2021.9635538
Biozid Bostami, V. Calhoun, H. V. D. Horn, V. Vergara
Neuroscience studies have begun to benefit from combining large numbers of data from different sites to increase statistical power. Pooling data from various sites into a single analysis introduces additional variability from site-effects due to differences in scanner protocols, imaging protocol, and acquisition methods, among others. These site-effects can reduce statistical power or lead to erroneous conclusions. Harmonization is the process of combining data aiming at reducing site variability. One recent approach for harmonizing data called ComBat has been shown to be helpful in the context of functional MRI and static functional connectivity. However, ComBat has not been applied to the analysis of dynamic functional network connectivity (dFNC). Here we explore the impact of ComBat harmonization on dFNC data collected from two different mild traumatic brain injury (mTBI) studies. Results show that ComBat harmonization of dFNC can reduce site effects producing a more robust analysis of patient effects across sites.
{"title":"Harmonization of Multi-site Dynamic Functional Connectivity Network Data","authors":"Biozid Bostami, V. Calhoun, H. V. D. Horn, V. Vergara","doi":"10.1109/BIBE52308.2021.9635538","DOIUrl":"https://doi.org/10.1109/BIBE52308.2021.9635538","url":null,"abstract":"Neuroscience studies have begun to benefit from combining large numbers of data from different sites to increase statistical power. Pooling data from various sites into a single analysis introduces additional variability from site-effects due to differences in scanner protocols, imaging protocol, and acquisition methods, among others. These site-effects can reduce statistical power or lead to erroneous conclusions. Harmonization is the process of combining data aiming at reducing site variability. One recent approach for harmonizing data called ComBat has been shown to be helpful in the context of functional MRI and static functional connectivity. However, ComBat has not been applied to the analysis of dynamic functional network connectivity (dFNC). Here we explore the impact of ComBat harmonization on dFNC data collected from two different mild traumatic brain injury (mTBI) studies. Results show that ComBat harmonization of dFNC can reduce site effects producing a more robust analysis of patient effects across sites.","PeriodicalId":343724,"journal":{"name":"2021 IEEE 21st International Conference on Bioinformatics and Bioengineering (BIBE)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127166394","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 : 2021-10-25DOI: 10.1109/BIBE52308.2021.9635485
Harsh Bhasin, R. Agrawal
The classification of Mild Cognitive Impairment can be done using 2-D CNN, which take a single slice at a time as input and do not consider pixel information from adjacent slices or spatial correlation amongst the slices of the brain volume or 3-D CNN, which requires huge computation time and memory as a significantly large number of parameters involved in 3D-CNN in comparison to 2D-CNN. To reduce the spatial correlation, computational complexity, and memory requirement, we use t-Distributed Stochastic Neighbor Embedding (t-SNE) on MRI volume to reduce its dimensions. Also, we use parallel CNN instead of sequential to analyze MRI volumes and a combination of RELU, sigmoid, and SIREN activation functions to learn better features for the classification of MCI. To check the efficacy of the proposed t-SNE Multiple-Activation Parallel Convolution Network, experiments are performed on publicly available Alzheimer's Disease Neuroimaging Initiative dataset, and performance is compared with existing methods. We obtain classification accuracy of 94.15 and 94.89 on MCI-C Vs. MCI-NC data and MCI Vs. Controls data respectively.
{"title":"Multiple-Activation Parallel Convolution Network in Combination with t-SNE for the Classification of Mild Cognitive Impairment","authors":"Harsh Bhasin, R. Agrawal","doi":"10.1109/BIBE52308.2021.9635485","DOIUrl":"https://doi.org/10.1109/BIBE52308.2021.9635485","url":null,"abstract":"The classification of Mild Cognitive Impairment can be done using 2-D CNN, which take a single slice at a time as input and do not consider pixel information from adjacent slices or spatial correlation amongst the slices of the brain volume or 3-D CNN, which requires huge computation time and memory as a significantly large number of parameters involved in 3D-CNN in comparison to 2D-CNN. To reduce the spatial correlation, computational complexity, and memory requirement, we use t-Distributed Stochastic Neighbor Embedding (t-SNE) on MRI volume to reduce its dimensions. Also, we use parallel CNN instead of sequential to analyze MRI volumes and a combination of RELU, sigmoid, and SIREN activation functions to learn better features for the classification of MCI. To check the efficacy of the proposed t-SNE Multiple-Activation Parallel Convolution Network, experiments are performed on publicly available Alzheimer's Disease Neuroimaging Initiative dataset, and performance is compared with existing methods. We obtain classification accuracy of 94.15 and 94.89 on MCI-C Vs. MCI-NC data and MCI Vs. Controls data respectively.","PeriodicalId":343724,"journal":{"name":"2021 IEEE 21st International Conference on Bioinformatics and Bioengineering (BIBE)","volume":"24 8","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114085782","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 : 2021-10-25DOI: 10.1109/BIBE52308.2021.9635320
Sandi Baressi Segota, N. Anđelić, I. Lorencin, J. Musulin, D. Štifanić, Z. Car
Simplified Molecular Input Line Entry System (SMILES) is a type of chemical notation. The SMILES format allows the representation of chemical structures in a shape easily readable by computer programs. This allows many techniques, such as Artificial Neural Networks (ANNs) to be applied on the SMILES formatted data. One of the highest-performing ANN types is the Convolutional Neural Networks (CNNs), designed to work on images or matrix-shaped data. In this paper, the authors will present the preparation of the SMILES dataset for use by CNNs. The paper will start with a brief description of the SMILES format, followed by the explanation of the dataset transformation into an NPY matrix-based format, with an example of utilization via the application of popular CNN architectures on a transformed dataset. The proposed architecture achieves satisfactory results (AUC=0.92), with the transformation algorithm speed also proving satisfactory (0.08 seconds per data point)
{"title":"Preparation of Simplified Molecular Input Line Entry System Notation Datasets for use in Convolutional Neural Networks","authors":"Sandi Baressi Segota, N. Anđelić, I. Lorencin, J. Musulin, D. Štifanić, Z. Car","doi":"10.1109/BIBE52308.2021.9635320","DOIUrl":"https://doi.org/10.1109/BIBE52308.2021.9635320","url":null,"abstract":"Simplified Molecular Input Line Entry System (SMILES) is a type of chemical notation. The SMILES format allows the representation of chemical structures in a shape easily readable by computer programs. This allows many techniques, such as Artificial Neural Networks (ANNs) to be applied on the SMILES formatted data. One of the highest-performing ANN types is the Convolutional Neural Networks (CNNs), designed to work on images or matrix-shaped data. In this paper, the authors will present the preparation of the SMILES dataset for use by CNNs. The paper will start with a brief description of the SMILES format, followed by the explanation of the dataset transformation into an NPY matrix-based format, with an example of utilization via the application of popular CNN architectures on a transformed dataset. The proposed architecture achieves satisfactory results (AUC=0.92), with the transformation algorithm speed also proving satisfactory (0.08 seconds per data point)","PeriodicalId":343724,"journal":{"name":"2021 IEEE 21st International Conference on Bioinformatics and Bioengineering (BIBE)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122705732","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 : 2021-10-25DOI: 10.1109/BIBE52308.2021.9635216
Milica G. Nikolić, T. Šušteršič, Nenad Filipović
The paper describes mathematical model and numerical simulation of Mason-Weaver equation using finite difference method, FDM, for simulation of sedimentation process. Different FDM schemes have been developed and tested for several different initial conditions. Possible issues with numerical convergence and conservation of concentration are explained. Performed analysis can be important for any numerical simulation that captures sedimentation process. The results of this research can be further used in modelling epithelial cell behavior and lung-on-a-chip systems.
{"title":"Numerical Simulation of Sedimentation Process using Mason-Weaver Equation","authors":"Milica G. Nikolić, T. Šušteršič, Nenad Filipović","doi":"10.1109/BIBE52308.2021.9635216","DOIUrl":"https://doi.org/10.1109/BIBE52308.2021.9635216","url":null,"abstract":"The paper describes mathematical model and numerical simulation of Mason-Weaver equation using finite difference method, FDM, for simulation of sedimentation process. Different FDM schemes have been developed and tested for several different initial conditions. Possible issues with numerical convergence and conservation of concentration are explained. Performed analysis can be important for any numerical simulation that captures sedimentation process. The results of this research can be further used in modelling epithelial cell behavior and lung-on-a-chip systems.","PeriodicalId":343724,"journal":{"name":"2021 IEEE 21st International Conference on Bioinformatics and Bioengineering (BIBE)","volume":"118 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130138681","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 : 2021-10-25DOI: 10.1109/BIBE52308.2021.9635428
Momcilo Prodanovic, B. Stojanovic, Danica Prodanovic, N. Filipovic, S. Mijailovich
Hypertrophic and Dilated Cardiomyopathies are caused by inherited mutations in sarcomeric proteins: Myosin (M), Troponin (Tn), Tropomyosin (Tm) and Myosin Binding Protein-C (MyBP-C). A quantitative understanding of how mutations change protein behaviour, and hence cardiac muscle contraction, and how adaptations to these changes result in disease, could accelerate the design of novel personalized treatments and therapeutics. Newly developed multiscale computational tools, tightly interlaced with multiple experiments, can enhance efforts to correct the problems associated with cardiomyopathies and prevent or more effectively manage the disease. Using these computational tools, we examined the effects of mutations in myosin and troponin on cardiac muscle contractility and overall heart functional behaviour. We also examined the effects of potential therapeutics that modulate protein interactions and cardiac muscle contractility.
{"title":"Computational Modeling of Sarcomere Protein Mutations and Drug Effects on Cardiac Muscle Behavior","authors":"Momcilo Prodanovic, B. Stojanovic, Danica Prodanovic, N. Filipovic, S. Mijailovich","doi":"10.1109/BIBE52308.2021.9635428","DOIUrl":"https://doi.org/10.1109/BIBE52308.2021.9635428","url":null,"abstract":"Hypertrophic and Dilated Cardiomyopathies are caused by inherited mutations in sarcomeric proteins: Myosin (M), Troponin (Tn), Tropomyosin (Tm) and Myosin Binding Protein-C (MyBP-C). A quantitative understanding of how mutations change protein behaviour, and hence cardiac muscle contraction, and how adaptations to these changes result in disease, could accelerate the design of novel personalized treatments and therapeutics. Newly developed multiscale computational tools, tightly interlaced with multiple experiments, can enhance efforts to correct the problems associated with cardiomyopathies and prevent or more effectively manage the disease. Using these computational tools, we examined the effects of mutations in myosin and troponin on cardiac muscle contractility and overall heart functional behaviour. We also examined the effects of potential therapeutics that modulate protein interactions and cardiac muscle contractility.","PeriodicalId":343724,"journal":{"name":"2021 IEEE 21st International Conference on Bioinformatics and Bioengineering (BIBE)","volume":"303 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124698509","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 : 2021-10-25DOI: 10.1109/BIBE52308.2021.9635241
R. W. Oei, Jiewen Zhang, Jin Zhong, Guanqun Hou, Nuntawat Chanajarunvit, N. Xu
Actin cytoskeleton has been identified as a potential therapeutic target for cancer. Therefore, to identify cell responses to such chemical agents has been an essential part in the past studies, which is often measured visually. This kind of visual recognition task currently is performed by human experts, which poses a great challenge since the features can hardly be detected using only human eyes. This article presents the application of convolutional neural networks (CNNs) in classifying human breast epithelial cells based on different dosages of drug exposure. MCF-10A cell line was chosen for the experiments and was treated with 90 nM and 400 nM cytochalasin D. The CNNs were evaluated on a large immunofluorescence images of intracellular actin filament networks captured after the exposure of different drug concentrations. During the image pre-processing, we implemented image enhancement and data augmentation approaches. Two well-known CNNs, VGG-16 and ResNet-50, were trained with or without transfer learning. The study revealed that the CNN performed better in the classification task compared to human experts. In conclusion, ResN et-50 with transfer learning achieved the best performance.
{"title":"Convolutional Neural Networks for Cellular Drug Response Prediction Using Immunofluorescence Images of Intracellular Actin Filament Networks","authors":"R. W. Oei, Jiewen Zhang, Jin Zhong, Guanqun Hou, Nuntawat Chanajarunvit, N. Xu","doi":"10.1109/BIBE52308.2021.9635241","DOIUrl":"https://doi.org/10.1109/BIBE52308.2021.9635241","url":null,"abstract":"Actin cytoskeleton has been identified as a potential therapeutic target for cancer. Therefore, to identify cell responses to such chemical agents has been an essential part in the past studies, which is often measured visually. This kind of visual recognition task currently is performed by human experts, which poses a great challenge since the features can hardly be detected using only human eyes. This article presents the application of convolutional neural networks (CNNs) in classifying human breast epithelial cells based on different dosages of drug exposure. MCF-10A cell line was chosen for the experiments and was treated with 90 nM and 400 nM cytochalasin D. The CNNs were evaluated on a large immunofluorescence images of intracellular actin filament networks captured after the exposure of different drug concentrations. During the image pre-processing, we implemented image enhancement and data augmentation approaches. Two well-known CNNs, VGG-16 and ResNet-50, were trained with or without transfer learning. The study revealed that the CNN performed better in the classification task compared to human experts. In conclusion, ResN et-50 with transfer learning achieved the best performance.","PeriodicalId":343724,"journal":{"name":"2021 IEEE 21st International Conference on Bioinformatics and Bioengineering (BIBE)","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123596548","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 : 2021-10-25DOI: 10.1109/BIBE52308.2021.9635257
Žiko B. Milanović, Edina H. Avdović, Dušica M Simijonović, Z. Marković
Different phenolic coumarin derivatives represent a widespread class of compounds that have shown remarkable activity in removing reactive oxygen species. For this reason, within this study, the antiradical activity of previously synthesized phenolic derivatives of 4,7 -dihydroxycoumarin: (E)-3-(1-((2-hydroxyphenyl)amino) ethylidene) -2,4-dioxochroman-7-yl (A-20H), $(E)$ -3-(1((3-hydroxyphenyl)amino)ethylidene)-2,4-dioxochroman-7-yl acetate (A-30H), $(E)$. -3-(1((4-hydroxyphenyl)amino) ethylidene) -2,4-dioxochroman-7-yl (A-40H) acetate against the 2,2-diphenyl-1-picrylhydrazyl (DPPH·) radical was investigated. All research is supported by Density Functional Theory $(mathbf{DFT}/mathbf{M06}-mathbf{2X/6-311++}mathbf{G}(mathbf{d, p})$ level of theory and CPCM solvation model-methanol) in combination with global chemical reactivity parameters. The results of experimental scavenging activity towards DPPH· indicate that A-20H shows the best activity. The most probable scavenging route was determined based on the thermodynamic parameters. A good correlation between experiment and theory showed that Hydrogen Atom Transfer (HAT, $Deltatext{rGHAT}$) was the dominant pathway of the reduction of DPPH·. In general, the results of global chemical reactivity parameters show that the A-40H compound shows the best electron-donating properties, which is correlated with thermodynamic parameters obtained for the Single Electron Transfer (SET, $Delta{text{rGSET}}$) mechanism.
{"title":"Estimation of antiradical properties of series of 4, 7 - dihydroxycoumarin derivatives towards DPPH radical-experimental and DFT study","authors":"Žiko B. Milanović, Edina H. Avdović, Dušica M Simijonović, Z. Marković","doi":"10.1109/BIBE52308.2021.9635257","DOIUrl":"https://doi.org/10.1109/BIBE52308.2021.9635257","url":null,"abstract":"Different phenolic coumarin derivatives represent a widespread class of compounds that have shown remarkable activity in removing reactive oxygen species. For this reason, within this study, the antiradical activity of previously synthesized phenolic derivatives of 4,7 -dihydroxycoumarin: (E)-3-(1-((2-hydroxyphenyl)amino) ethylidene) -2,4-dioxochroman-7-yl (A-20H), $(E)$ -3-(1((3-hydroxyphenyl)amino)ethylidene)-2,4-dioxochroman-7-yl acetate (A-30H), $(E)$. -3-(1((4-hydroxyphenyl)amino) ethylidene) -2,4-dioxochroman-7-yl (A-40H) acetate against the 2,2-diphenyl-1-picrylhydrazyl (DPPH·) radical was investigated. All research is supported by Density Functional Theory $(mathbf{DFT}/mathbf{M06}-mathbf{2X/6-311++}mathbf{G}(mathbf{d, p})$ level of theory and CPCM solvation model-methanol) in combination with global chemical reactivity parameters. The results of experimental scavenging activity towards DPPH· indicate that A-20H shows the best activity. The most probable scavenging route was determined based on the thermodynamic parameters. A good correlation between experiment and theory showed that Hydrogen Atom Transfer (HAT, $Deltatext{rGHAT}$) was the dominant pathway of the reduction of DPPH·. In general, the results of global chemical reactivity parameters show that the A-40H compound shows the best electron-donating properties, which is correlated with thermodynamic parameters obtained for the Single Electron Transfer (SET, $Delta{text{rGSET}}$) mechanism.","PeriodicalId":343724,"journal":{"name":"2021 IEEE 21st International Conference on Bioinformatics and Bioengineering (BIBE)","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123699361","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 : 2021-10-25DOI: 10.1109/BIBE52308.2021.9635369
Styliani P. Zelilidou, E. Tripoliti, Kostas I. Vlachos, S. Konitsiotis, D. Fotiadis
This paper presents a clustering-based method for the detection of Multiple Sclerosis (MS) lesions, by including anatomical information, brain geometry and lesion features, while volume quantification is performed. The proposed method utilizes Fluid Attenuated Inversion Recovery (FLAIR) images for the delineation of the plaques and brain atrophy estimation. The methodology includes five steps: (i) image preprocessing, (ii) image segmentation utilizing the K-means clustering algorithm, (iii) post processing for elimination of false positives, (iv) delineation and visualization of the MS lesions, and (v) brain atrophy estimation. It is implemented in two different datasets; (a) a dataset of 3D FLAIR MR Images, acquired in 30 MS patients, and (b) a dataset of 15 FLAIR MR Images, provided by the MICCAI Challenge 2016. A sensitivity 73.80%, and 71.52% was achieved for the two datasets, respectively. Brain atrophy was determined only on the first dataset, since follow up scans are available.
{"title":"Clustering based Segmentation of MR Images for the Delineation and Monitoring of Multiple Sclerosis Progression","authors":"Styliani P. Zelilidou, E. Tripoliti, Kostas I. Vlachos, S. Konitsiotis, D. Fotiadis","doi":"10.1109/BIBE52308.2021.9635369","DOIUrl":"https://doi.org/10.1109/BIBE52308.2021.9635369","url":null,"abstract":"This paper presents a clustering-based method for the detection of Multiple Sclerosis (MS) lesions, by including anatomical information, brain geometry and lesion features, while volume quantification is performed. The proposed method utilizes Fluid Attenuated Inversion Recovery (FLAIR) images for the delineation of the plaques and brain atrophy estimation. The methodology includes five steps: (i) image preprocessing, (ii) image segmentation utilizing the K-means clustering algorithm, (iii) post processing for elimination of false positives, (iv) delineation and visualization of the MS lesions, and (v) brain atrophy estimation. It is implemented in two different datasets; (a) a dataset of 3D FLAIR MR Images, acquired in 30 MS patients, and (b) a dataset of 15 FLAIR MR Images, provided by the MICCAI Challenge 2016. A sensitivity 73.80%, and 71.52% was achieved for the two datasets, respectively. Brain atrophy was determined only on the first dataset, since follow up scans are available.","PeriodicalId":343724,"journal":{"name":"2021 IEEE 21st International Conference on Bioinformatics and Bioengineering (BIBE)","volume":"380 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122774745","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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