With the increasing age, chronicity of illness, and medication side-effects, the psychiatric patients tend to have higher incidence of commodity and rate of accidents. Falls is the most devastating accidents and the ability to maintain balance is pivotal for the well-beings of this population. It appeared that different patterns of fall risk factors may be evident in psychiatric patients but with few scientific evidence. The existing tools for evaluation of risk of falls were not applicable on this population. This study intended to quantitatively measure the balance ability in psychiatric patients with and without fall history. The results could further contribute to falls prevention in long term care unit.
{"title":"The Balance Ability of Fallers and Non-Fallers in Psychiatric Patients at a Long Term Care Unit","authors":"San-Ping Wang, Jen-Suh Chern, Jer-Hao Chang, Bo-Jian Wu, Hsiao-Ju Sun, Jiunn-Ying Liou","doi":"10.1109/BIBE.2016.58","DOIUrl":"https://doi.org/10.1109/BIBE.2016.58","url":null,"abstract":"With the increasing age, chronicity of illness, and medication side-effects, the psychiatric patients tend to have higher incidence of commodity and rate of accidents. Falls is the most devastating accidents and the ability to maintain balance is pivotal for the well-beings of this population. It appeared that different patterns of fall risk factors may be evident in psychiatric patients but with few scientific evidence. The existing tools for evaluation of risk of falls were not applicable on this population. This study intended to quantitatively measure the balance ability in psychiatric patients with and without fall history. The results could further contribute to falls prevention in long term care unit.","PeriodicalId":377504,"journal":{"name":"2016 IEEE 16th International Conference on Bioinformatics and Bioengineering (BIBE)","volume":"54 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131590366","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}
Chien-Liang Chen, Ping-Chia Li, C. Chuang, Chi-Wen Lung, Jing-Shia Tang
This study aimed to examine the correlations between susceptibility to motion sickness and pulmonary function and aerobic capacity. We supposed that people with different susceptibilities to motion sickness differ in their cardiorespiratory responses. Thirteen healthy young men were recruited. They were asked to complete a motion sickness history questionnaire and were divided into susceptible and non-susceptible groups. A rotary chair was rotated for 10 minutes at 20 rpm to induce motion sickness. After the rotation, Graybiel's diagnostic criteria were used to grade the severity of motion sickness. This study also conducted evaluations of cardiorespiratory responses. Participants underwent a pulmonary function test and an exercise test to evaluate aerobic capacity. A generalized estimating equation was used to analyze the correlation between Graybiel's scores and the pulmonary function and exercise test results. The differences between the two groups were analyzed for the physiological parameters measured. The results indicated higher severities of motion sickness in participants with lower values of maximum voluntary ventilation (MVV). Moreover, the group of susceptible participants demonstrated significantly higher Graybiel's scores and stronger cardiorespiratory responses, significant interaction effects were observed in oxygen consumption, carbon dioxide production (VCO2), ventilation, and respiratory rate. To summarize, MVV is proposed as a reference index for evaluating susceptibility to motion sickness. Severe motion sickness in susceptible participants is suggested to be related to hyperventilation, which causes excessive VCO2.
{"title":"Comparison of Motion Sickness-Induced Cardiorespiratory Responses between Susceptible and Non-susceptible Subjects and the Factors Associated with Symptom Severity","authors":"Chien-Liang Chen, Ping-Chia Li, C. Chuang, Chi-Wen Lung, Jing-Shia Tang","doi":"10.1109/BIBE.2016.16","DOIUrl":"https://doi.org/10.1109/BIBE.2016.16","url":null,"abstract":"This study aimed to examine the correlations between susceptibility to motion sickness and pulmonary function and aerobic capacity. We supposed that people with different susceptibilities to motion sickness differ in their cardiorespiratory responses. Thirteen healthy young men were recruited. They were asked to complete a motion sickness history questionnaire and were divided into susceptible and non-susceptible groups. A rotary chair was rotated for 10 minutes at 20 rpm to induce motion sickness. After the rotation, Graybiel's diagnostic criteria were used to grade the severity of motion sickness. This study also conducted evaluations of cardiorespiratory responses. Participants underwent a pulmonary function test and an exercise test to evaluate aerobic capacity. A generalized estimating equation was used to analyze the correlation between Graybiel's scores and the pulmonary function and exercise test results. The differences between the two groups were analyzed for the physiological parameters measured. The results indicated higher severities of motion sickness in participants with lower values of maximum voluntary ventilation (MVV). Moreover, the group of susceptible participants demonstrated significantly higher Graybiel's scores and stronger cardiorespiratory responses, significant interaction effects were observed in oxygen consumption, carbon dioxide production (VCO2), ventilation, and respiratory rate. To summarize, MVV is proposed as a reference index for evaluating susceptibility to motion sickness. Severe motion sickness in susceptible participants is suggested to be related to hyperventilation, which causes excessive VCO2.","PeriodicalId":377504,"journal":{"name":"2016 IEEE 16th International Conference on Bioinformatics and Bioengineering (BIBE)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114591776","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}
In several years, deep learning is a new area of machine learning field, which is the motivation of developing machine learning near to artificial intelligent. The neural networks belongs to deep learning are progressively important ideas in a variety of fields with great performance. Accordingly, utilization of deep learning in bioinformatics to enhance performance is very important. Convolutional neural networks is a network of deep learning which is claimed to be the best model to solve the problem of object recognition and detection utilizing GPU computing. In this study, we try to use CNN to identify efflux proteins in membrane and transport proteins, which is a famous problem in bioinformatics field. We construct the CNN from PSSM profiles with CUDA and Keras package based on Theano backend. Finally this approach achieved a significant improvement after we compare with the previous paper on efflux proteins. The proposed method can serve as an effective tool for identifying efflux proteins and can help biologists understand the functions of the efflux proteins. Moreover this study provides a basis for further research that can enrich a field of applying deep learning in bioinformatics.
{"title":"Using Deep Learning with Position Specific Scoring Matrices to Identify Efflux Proteins in Membrane and Transport Proteins","authors":"Semmy Wellem Taju, N. Le, Yu-Yen Ou","doi":"10.1109/BIBE.2016.69","DOIUrl":"https://doi.org/10.1109/BIBE.2016.69","url":null,"abstract":"In several years, deep learning is a new area of machine learning field, which is the motivation of developing machine learning near to artificial intelligent. The neural networks belongs to deep learning are progressively important ideas in a variety of fields with great performance. Accordingly, utilization of deep learning in bioinformatics to enhance performance is very important. Convolutional neural networks is a network of deep learning which is claimed to be the best model to solve the problem of object recognition and detection utilizing GPU computing. In this study, we try to use CNN to identify efflux proteins in membrane and transport proteins, which is a famous problem in bioinformatics field. We construct the CNN from PSSM profiles with CUDA and Keras package based on Theano backend. Finally this approach achieved a significant improvement after we compare with the previous paper on efflux proteins. The proposed method can serve as an effective tool for identifying efflux proteins and can help biologists understand the functions of the efflux proteins. Moreover this study provides a basis for further research that can enrich a field of applying deep learning in bioinformatics.","PeriodicalId":377504,"journal":{"name":"2016 IEEE 16th International Conference on Bioinformatics and Bioengineering (BIBE)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115278007","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}
Jira Jindalertudomdee, M. Hayashida, Jiangning Song, T. Akutsu
Understanding how pathogen's proteins interact with its host's proteins is the key concept for understanding pathogen's infection mechanism, which can lead to the discovery of improved therapeutics for treating infectious diseases. Several studies suggest that proteins from various pathogens tend to interact with human proteins involved in the same biological pathway. This implies that pathogens are inclined to target host's proteins with similar function. In addition, conservation between a protein's function and its local topological structure in a protein-protein interaction network (PIN) has been previously characterized. This leads to the hypothesis that pathogens target the host's proteins with a similar local topological structure in a PIN. In this work, this hypothesis is examined by adding a graphlet degree vector of a protein in the human PIN as a feature in the prediction model and using that model to predict the protein-protein interaction between human and four pathogens. The results show that this graphlet degree vector increases the performance significantly for all pathogens. This suggests that the intraspecies protein-protein interactions should be taken into consideration when developing prediction methods for host-pathogen protein interaction. The results also support the hypothesis that there exists a relationship between a protein's function and the local topology of the PIN.
{"title":"Host-Pathogen Protein Interaction Prediction Based on Local Topology Structures of a Protein Interaction Network","authors":"Jira Jindalertudomdee, M. Hayashida, Jiangning Song, T. Akutsu","doi":"10.1109/BIBE.2016.26","DOIUrl":"https://doi.org/10.1109/BIBE.2016.26","url":null,"abstract":"Understanding how pathogen's proteins interact with its host's proteins is the key concept for understanding pathogen's infection mechanism, which can lead to the discovery of improved therapeutics for treating infectious diseases. Several studies suggest that proteins from various pathogens tend to interact with human proteins involved in the same biological pathway. This implies that pathogens are inclined to target host's proteins with similar function. In addition, conservation between a protein's function and its local topological structure in a protein-protein interaction network (PIN) has been previously characterized. This leads to the hypothesis that pathogens target the host's proteins with a similar local topological structure in a PIN. In this work, this hypothesis is examined by adding a graphlet degree vector of a protein in the human PIN as a feature in the prediction model and using that model to predict the protein-protein interaction between human and four pathogens. The results show that this graphlet degree vector increases the performance significantly for all pathogens. This suggests that the intraspecies protein-protein interactions should be taken into consideration when developing prediction methods for host-pathogen protein interaction. The results also support the hypothesis that there exists a relationship between a protein's function and the local topology of the PIN.","PeriodicalId":377504,"journal":{"name":"2016 IEEE 16th International Conference on Bioinformatics and Bioengineering (BIBE)","volume":"174 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123522500","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}
Charles C. N. Wang, Yu-Liang Lee, P. Sheu, J. Tsai
Cardiovascular disease (CVD) is group of diseases of the heart and blood vessels and a major global cause of death, with more people dying every year from CVDs than from any other cause. Therefore, controlling and preventing CVDs and their complex pathogenesis (i.e., influenced by genetic and lifestyle factors) has gained considerable attention. In this study, we use the LSA algorithm to cluster highly related CVD association genes. The LSA can further explore each functional gene cluster including listing of the consensus terms shared by the genes in the cluster. A comprehensive network biology analysis can integrates clustered genes in the CVD genes with pathway information by building a network of interconnected pathways. Based on LSA, the CVD association genes from the GAD database are divided into 5 clusters (k=5). The gene set enrichment analysis reveals 25 significantly pathways. Further analysis to integrate pathway analysis and to combine miRNA and Drugbank data to gain more insights in the interplay can be useful to suggest drug repositioning.
{"title":"Application of Latent Semantic Analysis to Clustering of Cardiovascular Gene Ontology","authors":"Charles C. N. Wang, Yu-Liang Lee, P. Sheu, J. Tsai","doi":"10.1109/BIBE.2016.71","DOIUrl":"https://doi.org/10.1109/BIBE.2016.71","url":null,"abstract":"Cardiovascular disease (CVD) is group of diseases of the heart and blood vessels and a major global cause of death, with more people dying every year from CVDs than from any other cause. Therefore, controlling and preventing CVDs and their complex pathogenesis (i.e., influenced by genetic and lifestyle factors) has gained considerable attention. In this study, we use the LSA algorithm to cluster highly related CVD association genes. The LSA can further explore each functional gene cluster including listing of the consensus terms shared by the genes in the cluster. A comprehensive network biology analysis can integrates clustered genes in the CVD genes with pathway information by building a network of interconnected pathways. Based on LSA, the CVD association genes from the GAD database are divided into 5 clusters (k=5). The gene set enrichment analysis reveals 25 significantly pathways. Further analysis to integrate pathway analysis and to combine miRNA and Drugbank data to gain more insights in the interplay can be useful to suggest drug repositioning.","PeriodicalId":377504,"journal":{"name":"2016 IEEE 16th International Conference on Bioinformatics and Bioengineering (BIBE)","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116417540","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}
Tzu-Fan Chen, Rong-Ming Chen, J. Tsai, Rouh-Mei Hu
Human microbiota account for 1-3 % total human body mass. The gastro-intestinal tract, especially the gut, is rich in different microorganisms, which play important role in our health and diseases. Understanding our gut microbiome may help us to increase the precision of disease prediction and treatment. Traditional culture-based methods are time-consuming, expensive and incomplete. The 16S rRNA metagenomic approach provided a simple and culture-independent way to get more information of gut microbiota. In this study, we analyzed 30 human stool microbiome samples. The hierarchical clustering method was applied to classify the enterotypes. The results showed that: (1) There is a positive correlation between the original NGS data volume, rRNA read number and microbiota diversity. (2) Some bacterial genera presented dominantly in human gut, so that a sufficient sequencing depth is important to identify the minor microbiota component. (3) Bacteroides, Lachnospiracea_incertae_sedis and Ruminococcus2 are the most frequently presented genera. Bacteroides, Prevotella and Alistipes are the most abundant genera. (4) Clustering result showed that there are four main enterotypes: Parabacteroides, Prevotella, Ruminococcus, and Bacterioides. We can still divide Parabacteroides into 3 subclasses according to the composition of bacteria.
{"title":"Fine Classification of Human Gut Microbiota by Using Hierarchical Clustering Approach","authors":"Tzu-Fan Chen, Rong-Ming Chen, J. Tsai, Rouh-Mei Hu","doi":"10.1109/BIBE.2016.33","DOIUrl":"https://doi.org/10.1109/BIBE.2016.33","url":null,"abstract":"Human microbiota account for 1-3 % total human body mass. The gastro-intestinal tract, especially the gut, is rich in different microorganisms, which play important role in our health and diseases. Understanding our gut microbiome may help us to increase the precision of disease prediction and treatment. Traditional culture-based methods are time-consuming, expensive and incomplete. The 16S rRNA metagenomic approach provided a simple and culture-independent way to get more information of gut microbiota. In this study, we analyzed 30 human stool microbiome samples. The hierarchical clustering method was applied to classify the enterotypes. The results showed that: (1) There is a positive correlation between the original NGS data volume, rRNA read number and microbiota diversity. (2) Some bacterial genera presented dominantly in human gut, so that a sufficient sequencing depth is important to identify the minor microbiota component. (3) Bacteroides, Lachnospiracea_incertae_sedis and Ruminococcus2 are the most frequently presented genera. Bacteroides, Prevotella and Alistipes are the most abundant genera. (4) Clustering result showed that there are four main enterotypes: Parabacteroides, Prevotella, Ruminococcus, and Bacterioides. We can still divide Parabacteroides into 3 subclasses according to the composition of bacteria.","PeriodicalId":377504,"journal":{"name":"2016 IEEE 16th International Conference on Bioinformatics and Bioengineering (BIBE)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125979789","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}
T. Otani, Satoshi Ii, S. Wada, T. Shigematsu, T. Fujinaka, T. Ozaki, M. Hirata
Computational fluid dynamics (CFD) study of the blood flow in the coiled cerebral aneurysm is one of powerful tools to explore the treatment mechanism of the endovascular coiling. Although several computational techniques were proposed to represent the realistic coil configuration in the aneurysm for the CFD studies, the CFD methodology for the blood flow analysis in the densely coiled aneurysm is still not fully discussed. The present study develops a CFD approach for the blood flow analysis in the densely coiled cerebral aneurysm without unstructured volume mesh creation. Patient-specific aneurysm geometry with realistic coil configuration was implicitly represented in a Cartesian-grid by using the volume of fraction (VOF) function. A Cartesian-grid CFD simulation was conducted in a finite difference manner with using the VOF function by the method of Weymouth and Yue (J. Compt. Phys., 2011). We conducted that two cases of numerical example of the blood flow analysis in the aneurysm prior to the coiling and after coiling with the packing density of 27%. These examples clearly exhibited that the developed CFD framework successfully resolved the fine flow characteristics around coils in the aneurysm despite the absence of explicit coil surface.
{"title":"A Computational Approach for Blood Flow Analysis in the Densely Coiled Cerebral Aneurysm","authors":"T. Otani, Satoshi Ii, S. Wada, T. Shigematsu, T. Fujinaka, T. Ozaki, M. Hirata","doi":"10.1109/BIBE.2016.22","DOIUrl":"https://doi.org/10.1109/BIBE.2016.22","url":null,"abstract":"Computational fluid dynamics (CFD) study of the blood flow in the coiled cerebral aneurysm is one of powerful tools to explore the treatment mechanism of the endovascular coiling. Although several computational techniques were proposed to represent the realistic coil configuration in the aneurysm for the CFD studies, the CFD methodology for the blood flow analysis in the densely coiled aneurysm is still not fully discussed. The present study develops a CFD approach for the blood flow analysis in the densely coiled cerebral aneurysm without unstructured volume mesh creation. Patient-specific aneurysm geometry with realistic coil configuration was implicitly represented in a Cartesian-grid by using the volume of fraction (VOF) function. A Cartesian-grid CFD simulation was conducted in a finite difference manner with using the VOF function by the method of Weymouth and Yue (J. Compt. Phys., 2011). We conducted that two cases of numerical example of the blood flow analysis in the aneurysm prior to the coiling and after coiling with the packing density of 27%. These examples clearly exhibited that the developed CFD framework successfully resolved the fine flow characteristics around coils in the aneurysm despite the absence of explicit coil surface.","PeriodicalId":377504,"journal":{"name":"2016 IEEE 16th International Conference on Bioinformatics and Bioengineering (BIBE)","volume":"13 4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115826391","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}
Clinical baseline matching is a critical step in the process of transforming real-world clinical records into novel medical knowledge, more precise diagnosis and efficacious treatments. Conventionally, the baseline matching was performed by heuristic methods or even manually. Here, a baseline-matching algorithm called "dynamite" was proposed using the dynamic programming technique. This algorithm minimizes the discrepancy of values of clinical variables or propensity scores between two patient groups in the study, while at the same time includes as many patients as possible so as to maximize statistical power. This algorithm was applied to an anti-hepatitis B research where two classes of approved drugs, peginterferon and nucleos(t)ide reverse transcription inhibitors (NRTI), were compared in terms of their protective effect in patients against hepatocellular carcinoma (HCC). Patients treated by the two classes of medications were retrospectively recruited from clinical records. Initially, age and HBeAg positivity were significantly different between candidate study subjects in the two treatment groups, with 153 and 177 patients respectively. Using the baseline-matching algorithm, the baseline characteristics of the two included patient groups, each comprised 120 patients, were well matched. Longitudinal analysis showed that the peginterferon-treated group achieved better HCC-free survival than the NRTI-treated groups (P = 0.0087293).
{"title":"Dynamic programming for clinical baseline matching and its application to anti-hepatitis B research","authors":"Kung-Hao Liang","doi":"10.1109/BIBE.2016.72","DOIUrl":"https://doi.org/10.1109/BIBE.2016.72","url":null,"abstract":"Clinical baseline matching is a critical step in the process of transforming real-world clinical records into novel medical knowledge, more precise diagnosis and efficacious treatments. Conventionally, the baseline matching was performed by heuristic methods or even manually. Here, a baseline-matching algorithm called \"dynamite\" was proposed using the dynamic programming technique. This algorithm minimizes the discrepancy of values of clinical variables or propensity scores between two patient groups in the study, while at the same time includes as many patients as possible so as to maximize statistical power. This algorithm was applied to an anti-hepatitis B research where two classes of approved drugs, peginterferon and nucleos(t)ide reverse transcription inhibitors (NRTI), were compared in terms of their protective effect in patients against hepatocellular carcinoma (HCC). Patients treated by the two classes of medications were retrospectively recruited from clinical records. Initially, age and HBeAg positivity were significantly different between candidate study subjects in the two treatment groups, with 153 and 177 patients respectively. Using the baseline-matching algorithm, the baseline characteristics of the two included patient groups, each comprised 120 patients, were well matched. Longitudinal analysis showed that the peginterferon-treated group achieved better HCC-free survival than the NRTI-treated groups (P = 0.0087293).","PeriodicalId":377504,"journal":{"name":"2016 IEEE 16th International Conference on Bioinformatics and Bioengineering (BIBE)","volume":"216 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124265174","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}
Van Du Nguyen, Jiwon Han, Shaohui Zheng, Viet Ha Le, Seung Yoon Song, Jong-Oh Park, Sukho Park
This study proposes a prototype of bacteria-based microrobots using chitosan-coated liposomes attached to a Salmonella enteritidis bacterial strain. The attachment of the liposome and the bacteria is performed by interaction between the positive charge surface of the liposomes and the gram-negative bacteria. The liposomes are fabricated using hydration method to obtain the average diameter of 10 um and they are coated with chitosan. The chitosan coating is shown to have effect on motility of the microrobots. First, the bacteria cannot attach to chitosan-uncoated liposomes, but they can easily adhere to the chitosan-coated liposomes. Second, when the concentration of chitosan increases from 0.1% to 0.5%, average velocity of the microrobots increases from 1.20±0.12 um/s to 3.25±0.35 um/s. However, when chitosan concentration increases to 1% the average velocity of the microrobots slightly decreases from 3.25±0.35 um/s to 3.17±0.33 um/s, respectively. The study suggests that using chitosan coating can be a potential method for further development of therapeutic bacteria-based liposomal microrobots.
{"title":"Effect of Chitosan on Motility of Bacteria-Driven Liposomal Microrobots","authors":"Van Du Nguyen, Jiwon Han, Shaohui Zheng, Viet Ha Le, Seung Yoon Song, Jong-Oh Park, Sukho Park","doi":"10.1109/BIBE.2016.20","DOIUrl":"https://doi.org/10.1109/BIBE.2016.20","url":null,"abstract":"This study proposes a prototype of bacteria-based microrobots using chitosan-coated liposomes attached to a Salmonella enteritidis bacterial strain. The attachment of the liposome and the bacteria is performed by interaction between the positive charge surface of the liposomes and the gram-negative bacteria. The liposomes are fabricated using hydration method to obtain the average diameter of 10 um and they are coated with chitosan. The chitosan coating is shown to have effect on motility of the microrobots. First, the bacteria cannot attach to chitosan-uncoated liposomes, but they can easily adhere to the chitosan-coated liposomes. Second, when the concentration of chitosan increases from 0.1% to 0.5%, average velocity of the microrobots increases from 1.20±0.12 um/s to 3.25±0.35 um/s. However, when chitosan concentration increases to 1% the average velocity of the microrobots slightly decreases from 3.25±0.35 um/s to 3.17±0.33 um/s, respectively. The study suggests that using chitosan coating can be a potential method for further development of therapeutic bacteria-based liposomal microrobots.","PeriodicalId":377504,"journal":{"name":"2016 IEEE 16th International Conference on Bioinformatics and Bioengineering (BIBE)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125834344","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}
This study investigates a feasibility for patient-specific blood flow simulation, using a set of measurement data obtained from DSA and PC-MRI with respect to a geometry and velocity. The present approach naturally satisfies the physical consistency through boundary condition. The pressure boundary values are evaluated by relaxing a misfit of velocity field between measurement and simulation based on a proportional feedback control. The investigation involves a patient-specific aneurysm model reconstructed from DSA image, where the aneurysm is developed at the bifurcation with three branches. The result shows that a difference of velocity field between the measurement of the PC-MRI and simulation is reduced to 19.8% in systole condition, and then a reasonable wall shear stress distribution can be reproduced by the use of the measurement velocity data without explicitly giving the boundary conditions. The present approach exhibits a feasibility of the simulation-based blood flow analysis for understanding patient-specific hemodynamics.
{"title":"Patient-Specific Blood Flows Simulation on Cerebral Aneurysm Based on Physically Consistency Feedback Control","authors":"M. Adib, Satoshi, Yoshiyuki Watanabe, S. Wada","doi":"10.1109/BIBE.2016.32","DOIUrl":"https://doi.org/10.1109/BIBE.2016.32","url":null,"abstract":"This study investigates a feasibility for patient-specific blood flow simulation, using a set of measurement data obtained from DSA and PC-MRI with respect to a geometry and velocity. The present approach naturally satisfies the physical consistency through boundary condition. The pressure boundary values are evaluated by relaxing a misfit of velocity field between measurement and simulation based on a proportional feedback control. The investigation involves a patient-specific aneurysm model reconstructed from DSA image, where the aneurysm is developed at the bifurcation with three branches. The result shows that a difference of velocity field between the measurement of the PC-MRI and simulation is reduced to 19.8% in systole condition, and then a reasonable wall shear stress distribution can be reproduced by the use of the measurement velocity data without explicitly giving the boundary conditions. The present approach exhibits a feasibility of the simulation-based blood flow analysis for understanding patient-specific hemodynamics.","PeriodicalId":377504,"journal":{"name":"2016 IEEE 16th International Conference on Bioinformatics and Bioengineering (BIBE)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129879274","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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