- This paper presents a mathematical model that explains the mechanism behind the drainage of urine from a healthy human kidney through the ureter. Computer simulation is used to study the conduction velocity and output flow rate of a urine bolus through the Ureter lumen. The conduction velocity calculated by the simulation model is 4.8 cm/sec which is in within the range of experimental values of 2 to 6 cm/sec. The urine output flow rate is calculated to be 0.053 ml/sec, which results in a total of 1.8 liter of urine disposition from two kidneys every 24 hours. The simulation result yields toward the nominal quantity of 1.5 liter of urine disposed by a healthy adult with normal kidney function.
{"title":"A Simulation Study of Urine Transport Through the Ureter","authors":"Poupak Kermani","doi":"10.11159/jbeb.2023.001","DOIUrl":"https://doi.org/10.11159/jbeb.2023.001","url":null,"abstract":"- This paper presents a mathematical model that explains the mechanism behind the drainage of urine from a healthy human kidney through the ureter. Computer simulation is used to study the conduction velocity and output flow rate of a urine bolus through the Ureter lumen. The conduction velocity calculated by the simulation model is 4.8 cm/sec which is in within the range of experimental values of 2 to 6 cm/sec. The urine output flow rate is calculated to be 0.053 ml/sec, which results in a total of 1.8 liter of urine disposition from two kidneys every 24 hours. The simulation result yields toward the nominal quantity of 1.5 liter of urine disposed by a healthy adult with normal kidney function.","PeriodicalId":92699,"journal":{"name":"Open access journal of biomedical engineering and biosciences","volume":"114 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72805737","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}
{"title":"Suggestive Decreasing Effects of the COVID-19 Pandemic on Reported Adverse Arrhythmic Events and 30-Day Fills For Anti-Arrhythmic Agents","authors":"Eshaan Gandhi, Sujata Bhatia","doi":"10.11159/jbeb.2023.004","DOIUrl":"https://doi.org/10.11159/jbeb.2023.004","url":null,"abstract":"","PeriodicalId":92699,"journal":{"name":"Open access journal of biomedical engineering and biosciences","volume":"58 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136302803","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}
Arife Uzundurukan, Sébastien Poncet, Daria Camilla Boffito, Philippe Micheau
{"title":"Realistic 3D CT-FEM for Target-based Multiple Organ Inclusive Studies","authors":"Arife Uzundurukan, Sébastien Poncet, Daria Camilla Boffito, Philippe Micheau","doi":"10.11159/jbeb.2023.005","DOIUrl":"https://doi.org/10.11159/jbeb.2023.005","url":null,"abstract":"","PeriodicalId":92699,"journal":{"name":"Open access journal of biomedical engineering and biosciences","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136302018","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}
{"title":"Development of Sporobeads Coated With Hecad1/2 for Rapid Detection and Capturing Of Pathogenic Listeria Monocytogenes","authors":"Khosrow Mohammadi, Per Erik Joakim Saris","doi":"10.11159/jbeb.2023.006","DOIUrl":"https://doi.org/10.11159/jbeb.2023.006","url":null,"abstract":"","PeriodicalId":92699,"journal":{"name":"Open access journal of biomedical engineering and biosciences","volume":"73 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136373852","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}
{"title":"Affordability Assessment on Generic and Brand-name Anti-depressants","authors":"Sophia Lin","doi":"10.11159/jbeb.2023.002","DOIUrl":"https://doi.org/10.11159/jbeb.2023.002","url":null,"abstract":"","PeriodicalId":92699,"journal":{"name":"Open access journal of biomedical engineering and biosciences","volume":"68-69 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135911137","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}
Jozsef Nagy, Julia Maier, Veronika Miron, Wolfgang Fenz, Zoltan Major, Andreas Gruber, Matthias Gmeiner
{"title":"Methods, Validation and Clinical Implementation of a Simulation Method of Cerebral Aneurysms","authors":"Jozsef Nagy, Julia Maier, Veronika Miron, Wolfgang Fenz, Zoltan Major, Andreas Gruber, Matthias Gmeiner","doi":"10.11159/jbeb.2023.003","DOIUrl":"https://doi.org/10.11159/jbeb.2023.003","url":null,"abstract":"","PeriodicalId":92699,"journal":{"name":"Open access journal of biomedical engineering and biosciences","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135914634","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}
Bouke L. Scheltinga, Hazal Usta, J. Reenalda, J. Buurke
- Quantification of biomechanical load is crucial to gain insights in the mechanisms causing running related injuries. Ground reaction forces (GRF) can give insights into biomechanical loading, however, measuring GRF is restricted to a gait laboratory. Developments in inertial sensor technology make it possible to measure segment accelerations and orientations outside the lab in the runners’ own environment. The main objective of this study is to estimate vertical GRF with three inertial measurement units using a generic algorithm based on Newtons second law. When using Newton’s second law, it is known that the mass distribution per corresponding acceleration and filtering settings of the acceleration signal do have an influence on the estimated force. Therefore, filtering settings and the mass of the segments were optimized in this study. To apply Newton’s second law to the full body, the accelerations and masses of every segment should be known. However, this requires >10 sensors. By minimizing the number of segments to three, a setup is created that is less obtrusive. Twelve rear foot strike (RFS) runners performed nine trials at three different velocities (10, 12 and 14km/h) and three different stride frequencies (low, preferred, high), on a instrumented treadmill. Inertial measurement units were placed at sternum, pelvis, upper legs, tibias and feet. An optimization was performed to find the optimal sensor configuration. The root mean squared error (RMSE) between the estimated GRF and measured GRF was used as loss function in the optimization. As performance measure of the algorithm, RMSE, active peak error and Pearson’s correlation coefficient were used. The setup with sensors on the tibia and pelvis showed the best result, with an average RMSE of 0.179 bodyweight, peak error of 3.6% and Pearson’s correlation coefficient of 0.98. Using leave-one-subject-out cross validation, it is shown that the algorithm is generalizable within the population of RFS runners. Model performance decreases with velocity but increases with stride frequency. The main error of the algorithm is seen in the first 25% of the stance phase, however, the general performance is comparable or better than what is described in current literature.
{"title":"Estimating Vertical Ground Reaction Force during Running with 3 Inertial Measurement Units","authors":"Bouke L. Scheltinga, Hazal Usta, J. Reenalda, J. Buurke","doi":"10.11159/jbeb.2022.006","DOIUrl":"https://doi.org/10.11159/jbeb.2022.006","url":null,"abstract":"- Quantification of biomechanical load is crucial to gain insights in the mechanisms causing running related injuries. Ground reaction forces (GRF) can give insights into biomechanical loading, however, measuring GRF is restricted to a gait laboratory. Developments in inertial sensor technology make it possible to measure segment accelerations and orientations outside the lab in the runners’ own environment. The main objective of this study is to estimate vertical GRF with three inertial measurement units using a generic algorithm based on Newtons second law. When using Newton’s second law, it is known that the mass distribution per corresponding acceleration and filtering settings of the acceleration signal do have an influence on the estimated force. Therefore, filtering settings and the mass of the segments were optimized in this study. To apply Newton’s second law to the full body, the accelerations and masses of every segment should be known. However, this requires >10 sensors. By minimizing the number of segments to three, a setup is created that is less obtrusive. Twelve rear foot strike (RFS) runners performed nine trials at three different velocities (10, 12 and 14km/h) and three different stride frequencies (low, preferred, high), on a instrumented treadmill. Inertial measurement units were placed at sternum, pelvis, upper legs, tibias and feet. An optimization was performed to find the optimal sensor configuration. The root mean squared error (RMSE) between the estimated GRF and measured GRF was used as loss function in the optimization. As performance measure of the algorithm, RMSE, active peak error and Pearson’s correlation coefficient were used. The setup with sensors on the tibia and pelvis showed the best result, with an average RMSE of 0.179 bodyweight, peak error of 3.6% and Pearson’s correlation coefficient of 0.98. Using leave-one-subject-out cross validation, it is shown that the algorithm is generalizable within the population of RFS runners. Model performance decreases with velocity but increases with stride frequency. The main error of the algorithm is seen in the first 25% of the stance phase, however, the general performance is comparable or better than what is described in current literature.","PeriodicalId":92699,"journal":{"name":"Open access journal of biomedical engineering and biosciences","volume":"43 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76322427","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}
- The COVID-19 pandemic forced cardiologists to adapt to unprecedented circumstances. We chose to investigate the pandemic’s effect on heart valve replacements, in particular focussing on device failure in mitral valve replacements and percutaneous aortic valve prostheses. In order to measure this effect, we examined adverse event reports of these two devices in the Food and Drug Administration (FDA)’s Manufacturer and User Facility Device Experience (MAUDE) database. We compared weekly numbers of adverse event reports during the pandemic (March 2020-March 2021) to those of the year before (March 2019-March 2020). We find that reports of deaths, injuries, and malfunctions attributed to mitral valve repair devices all showed no significant changes during the pandemic, compared to the year preceding. However, we have also found that during the pandemic, there was a 107.4% increase in deaths reported to the FDA that were attributed to percutaneous aortic valve prostheses, and a 45.1% increase in reports of malfunctions as well compared to the year preceding the pandemic. These results suggest that the pandemic may have induced an increase in transcatheter aortic valve replacements vs. surgical aortic valve replacements, leading to an increase in adverse event reports associated with percutaneous aortic valve prostheses. In contrast, transcatheter mitral valve repair is not commonly performed, and the pandemic is unlikely to have changed treatment protocols for mitral valve repair.
{"title":"Analyzing Adverse Events of Mitral and Aortic Valves during the Pandemic","authors":"E. Zhou, S. Bhatia","doi":"10.11159/jbeb.2022.004","DOIUrl":"https://doi.org/10.11159/jbeb.2022.004","url":null,"abstract":"- The COVID-19 pandemic forced cardiologists to adapt to unprecedented circumstances. We chose to investigate the pandemic’s effect on heart valve replacements, in particular focussing on device failure in mitral valve replacements and percutaneous aortic valve prostheses. In order to measure this effect, we examined adverse event reports of these two devices in the Food and Drug Administration (FDA)’s Manufacturer and User Facility Device Experience (MAUDE) database. We compared weekly numbers of adverse event reports during the pandemic (March 2020-March 2021) to those of the year before (March 2019-March 2020). We find that reports of deaths, injuries, and malfunctions attributed to mitral valve repair devices all showed no significant changes during the pandemic, compared to the year preceding. However, we have also found that during the pandemic, there was a 107.4% increase in deaths reported to the FDA that were attributed to percutaneous aortic valve prostheses, and a 45.1% increase in reports of malfunctions as well compared to the year preceding the pandemic. These results suggest that the pandemic may have induced an increase in transcatheter aortic valve replacements vs. surgical aortic valve replacements, leading to an increase in adverse event reports associated with percutaneous aortic valve prostheses. In contrast, transcatheter mitral valve repair is not commonly performed, and the pandemic is unlikely to have changed treatment protocols for mitral valve repair.","PeriodicalId":92699,"journal":{"name":"Open access journal of biomedical engineering and biosciences","volume":"11 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90483646","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}
M. Luján, J.M. Sotos, Ana Torres Aranda, Alejandro L. Borja
- In this paper, different techniques based on deep learning algorithms used for the classification and diagnosis of patients with mental disorders i.e., schizophrenia and bipolar disorder, are presented. To this aim, the signals obtained from 32 unipolar electrodes of non-invasive electroencephalogram analysis are studied to obtain its main features. More specifically, the analysis performed utilizes an innovative radial basis function neural network based on fuzzy means algorithm. Furthermore, the analysis of the variance of statistical parameters and entropy is applied. In total, 312 subjects with schizophrenia and 105 patients with bipolar disorder have been evaluated. The results obtained show a correct classification in patients compared to healthy controls. The proposed methods achieved a better performance than other machine learning techniques such as support vector machine or k-nearest neighbour, with an accuracy close to 96%. It can be concluded that this type of classifications will allow the training of algorithms that can be used to identify and classify different mental disorders with very high accuracy.
{"title":"EEG Based Schizophrenia and Bipolar Disorder Classification by Means of Deep Learning Methods","authors":"M. Luján, J.M. Sotos, Ana Torres Aranda, Alejandro L. Borja","doi":"10.11159/jbeb.2022.001","DOIUrl":"https://doi.org/10.11159/jbeb.2022.001","url":null,"abstract":"- In this paper, different techniques based on deep learning algorithms used for the classification and diagnosis of patients with mental disorders i.e., schizophrenia and bipolar disorder, are presented. To this aim, the signals obtained from 32 unipolar electrodes of non-invasive electroencephalogram analysis are studied to obtain its main features. More specifically, the analysis performed utilizes an innovative radial basis function neural network based on fuzzy means algorithm. Furthermore, the analysis of the variance of statistical parameters and entropy is applied. In total, 312 subjects with schizophrenia and 105 patients with bipolar disorder have been evaluated. The results obtained show a correct classification in patients compared to healthy controls. The proposed methods achieved a better performance than other machine learning techniques such as support vector machine or k-nearest neighbour, with an accuracy close to 96%. It can be concluded that this type of classifications will allow the training of algorithms that can be used to identify and classify different mental disorders with very high accuracy.","PeriodicalId":92699,"journal":{"name":"Open access journal of biomedical engineering and biosciences","volume":"12 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86572788","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：