- Disease models have been a helpful resource which have guided health organizations in choosing appropriate interventions during the COVID-19 pandemic. However, most current models simulate disease spread on a countrywide/statewide level, lacking specificity for localities such as towns or counties. As a result, one-size-fits-all policies are being instituted for entire states despite localities being heterogeneous in many important factors (population density, age demographics, and vaccination rate). Models tailored to individual localities are necessary to facilitate local level health action. In this research, a novel agent-based disease model was created using NetLogo to simulate localized COVID-19 disease dynamics. Individual agents represent each member of a population, and their individual traits (vaccination status, age, etc.) conform to the model input (vaccination rate, age distribution, etc.). Interactions between these agents produce the model outputs, which include predicted infections and deaths. The model was validated using data from state and local health agencies for Westchester County, NY (84.2% accuracy). Using the model, this research aims to answer the following question: what local factors affect COVID-19 outbreak severity and intervention impact? To accomplish this, a sensitivity analysis was conducted for three local variables (vaccination rate, age distribution, intervention applied) and a comparison of locality simulation was conducted for four different U.S. counties. From the results attained, this research concluded that vaccination rate, age distribution, and intervention applied in a locality all contribute significantly to risk level differences between localities, and that higher risk localities are impacted harder by interventions than those with lower risk. Localities can use this model to make health related decisions, and a website (www.localcovidmodel.org) has been created for model access.
{"title":"Agent-Based Simulation for Localized COVID-19 Intervention Decision","authors":"Jason Starr, Morgan P. Kain","doi":"10.11159/jbeb.2022.005","DOIUrl":"https://doi.org/10.11159/jbeb.2022.005","url":null,"abstract":"- Disease models have been a helpful resource which have guided health organizations in choosing appropriate interventions during the COVID-19 pandemic. However, most current models simulate disease spread on a countrywide/statewide level, lacking specificity for localities such as towns or counties. As a result, one-size-fits-all policies are being instituted for entire states despite localities being heterogeneous in many important factors (population density, age demographics, and vaccination rate). Models tailored to individual localities are necessary to facilitate local level health action. In this research, a novel agent-based disease model was created using NetLogo to simulate localized COVID-19 disease dynamics. Individual agents represent each member of a population, and their individual traits (vaccination status, age, etc.) conform to the model input (vaccination rate, age distribution, etc.). Interactions between these agents produce the model outputs, which include predicted infections and deaths. The model was validated using data from state and local health agencies for Westchester County, NY (84.2% accuracy). Using the model, this research aims to answer the following question: what local factors affect COVID-19 outbreak severity and intervention impact? To accomplish this, a sensitivity analysis was conducted for three local variables (vaccination rate, age distribution, intervention applied) and a comparison of locality simulation was conducted for four different U.S. counties. From the results attained, this research concluded that vaccination rate, age distribution, and intervention applied in a locality all contribute significantly to risk level differences between localities, and that higher risk localities are impacted harder by interventions than those with lower risk. Localities can use this model to make health related decisions, and a website (www.localcovidmodel.org) has been created for model access.","PeriodicalId":92699,"journal":{"name":"Open access journal of biomedical engineering and biosciences","volume":"65 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75746170","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}
Dominique L. Tanner, M. Privitera, M. Rao, I. Basu
- Epilepsy is a complex disease that causes unpredictable seizures, which can lead to severe neurological impairments. Not knowing when a seizure will occur, many people with epilepsy often experience feelings such as anxiety, fear, and stress. In an effort to predict when seizures might occur, investigators have used data from patients’ electronic seizure diaries, as well as machine-learning methods, like decision trees. The objective of this work is to create patient-specific decision trees to 1) forecast seizure occurrence and identify seizure precipitants that influence seizure occurrences, and 2) determine seizure precipitants’ level of influence on seizure occurrences. Patients’ (n=64) seizure diaries were examined individually. Diaries contained data on how patients rated mood, predictive symptoms, stress, seizure occurrences, and seizure likelihood using a 5-point Likert scale. Diaries were recorded in the morning and in the evening, thereby evaluating seizures by half days. R Programming software was used for data analysis and decision tree development, and a confusion matrix was used for predictive accuracy. Results showed that precipitants’ influence on patient’s seizure outcome was greater in the morning than in the evening. Patients were also categorized in groups based on shared seizure precipitants. This work introduced non-invasive, personalized healthcare regimen for people with epilepsy.
{"title":"Decision Trees as a Method for Forecasting Seizure Precipitants and Identifying Their Influences on Seizure Outcome","authors":"Dominique L. Tanner, M. Privitera, M. Rao, I. Basu","doi":"10.11159/jbeb.2022.007","DOIUrl":"https://doi.org/10.11159/jbeb.2022.007","url":null,"abstract":"- Epilepsy is a complex disease that causes unpredictable seizures, which can lead to severe neurological impairments. Not knowing when a seizure will occur, many people with epilepsy often experience feelings such as anxiety, fear, and stress. In an effort to predict when seizures might occur, investigators have used data from patients’ electronic seizure diaries, as well as machine-learning methods, like decision trees. The objective of this work is to create patient-specific decision trees to 1) forecast seizure occurrence and identify seizure precipitants that influence seizure occurrences, and 2) determine seizure precipitants’ level of influence on seizure occurrences. Patients’ (n=64) seizure diaries were examined individually. Diaries contained data on how patients rated mood, predictive symptoms, stress, seizure occurrences, and seizure likelihood using a 5-point Likert scale. Diaries were recorded in the morning and in the evening, thereby evaluating seizures by half days. R Programming software was used for data analysis and decision tree development, and a confusion matrix was used for predictive accuracy. Results showed that precipitants’ influence on patient’s seizure outcome was greater in the morning than in the evening. Patients were also categorized in groups based on shared seizure precipitants. This work introduced non-invasive, personalized healthcare regimen for people with epilepsy.","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":"90163012","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}
N. Shchotkina, A. Sokol, L. Dolinchuk, O. Galkin, Glib I. Glib I., Dmytro A. Yemets, A. Dovghaliuk, I. Skorokhod, Olena V. Olena V., N. Rudenko, I. Yemets
The use of xenotissue for the needs of regenerative and cardiovasculare medicine is a promising area of tissue engineering. The decellularization process provides complete purification of the elastin-collagen matrix of the bovine pericardium from cells and their components. The use of high concentrations of sodium dodecyl sulfate and glutaraldehyde can lead to a damage of the extracellular matrix. Therefore, the purpose of this study was to study the microarchitectonics of the decellularized matrix using a low concentration of sodium dodecyl sulfate (0.1% solution) and avoiding glutaraldehyde. Further stabilization and fixation of the matrix was carried out using 10 mM 1-Ethyl-3 (3dimethylaminopropyl) carbodiimide hydrochloride and 10 mM N-Hydroxysuccinimide. The effect of decellularization was assessed by staining the samples with hematoxylin-eosin and by scanning electron microscopy. Also the research results confirmed the absence of structural changes in the collagenelastin fibers of the matrix after sterilization dose of 10 kGy. Thus, it can be assumed that the radiation method of sterilization may be safe in use for sterilization of bioimplants.
利用异种组织满足再生和心血管医学的需要是组织工程的一个有前途的领域。脱细胞过程提供了从细胞及其成分中完全纯化牛心包弹性蛋白-胶原基质。使用高浓度的十二烷基硫酸钠和戊二醛可导致细胞外基质的破坏。因此，本研究的目的是使用低浓度十二烷基硫酸钠(0.1%溶液)和避免戊二醛来研究脱细胞基质的微结构。用10 mM 1-乙基-3(3 -二甲氨基丙基)卡二亚胺盐酸盐和10 mM n -羟基琥珀酰亚胺进一步稳定和固定基质。用苏木精-伊红染色和扫描电镜观察脱细胞效果。研究结果也证实了10 kGy灭菌剂量后基质的胶原弹性蛋白纤维没有结构变化。因此，可以假定辐射灭菌方法可安全用于生物植入物的灭菌。
{"title":"The Effect of Sterilization on the Bovine Pericardium Scaffold Decellularized By the Glutaraldehyde-Free Technology","authors":"N. Shchotkina, A. Sokol, L. Dolinchuk, O. Galkin, Glib I. Glib I., Dmytro A. Yemets, A. Dovghaliuk, I. Skorokhod, Olena V. Olena V., N. Rudenko, I. Yemets","doi":"10.11159/jbeb.2021.004","DOIUrl":"https://doi.org/10.11159/jbeb.2021.004","url":null,"abstract":"The use of xenotissue for the needs of regenerative and cardiovasculare medicine is a promising area of tissue engineering. The decellularization process provides complete purification of the elastin-collagen matrix of the bovine pericardium from cells and their components. The use of high concentrations of sodium dodecyl sulfate and glutaraldehyde can lead to a damage of the extracellular matrix. Therefore, the purpose of this study was to study the microarchitectonics of the decellularized matrix using a low concentration of sodium dodecyl sulfate (0.1% solution) and avoiding glutaraldehyde. Further stabilization and fixation of the matrix was carried out using 10 mM 1-Ethyl-3 (3dimethylaminopropyl) carbodiimide hydrochloride and 10 mM N-Hydroxysuccinimide. The effect of decellularization was assessed by staining the samples with hematoxylin-eosin and by scanning electron microscopy. Also the research results confirmed the absence of structural changes in the collagenelastin fibers of the matrix after sterilization dose of 10 kGy. Thus, it can be assumed that the radiation method of sterilization may be safe in use for sterilization of bioimplants.","PeriodicalId":92699,"journal":{"name":"Open access journal of biomedical engineering and biosciences","volume":"22 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84264163","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}
C. Hariharan, F. Z. Kadayifci, Fan-chao Zhang, Lei Xu, Jun Li, Shasha Shasha, Jiayu Liao
Cell death is a major process in a biological cell that occurs during development, homeostasis and immune regulation in multicellular organisms. Dysregulation of cell death pathway has been implicated in many diseases. Principal cell death pathways include apoptosis, autophagy, necrosis, mitotic catastrophe, etc. Knowledge of cell death pathways and the reason the cell chooses to die are key factors to understand the disease, the way it affects the cellular system and subsequent drug discovery. This study is focused on developing genetically encoded Förster Resonance Energy Transfer (FRET) based biosensors to identify autophagy pathways in vitro. FRET is an energy transfer phenomenon that occurs between two spectrum-overlapping fluorophores that are within 10nm of each other. The design of the sensor is based on enzyme-substrate dynamics and consists of a reporter gene fused between fluorescent proteins. Additionally, FRET-based protease assay has been used to determine the kinetics of Atg4A, an enzyme involved in autophagy. The kinetic parameters Km, kcat, kcat /Km were derived using real-time detection methods. A further aim of this research is to transfect the sensor in H460 lung cancer cell line to identify the type of death that the cell chooses on treatment with drugs.
{"title":"Development of High Sensitive and Quantitative FRET Based Biosensor to Detect Atg4A Kinetics in Autophagy Cell Death Pathway","authors":"C. Hariharan, F. Z. Kadayifci, Fan-chao Zhang, Lei Xu, Jun Li, Shasha Shasha, Jiayu Liao","doi":"10.11159/jbeb.2021.001","DOIUrl":"https://doi.org/10.11159/jbeb.2021.001","url":null,"abstract":"Cell death is a major process in a biological cell that occurs during development, homeostasis and immune regulation in multicellular organisms. Dysregulation of cell death pathway has been implicated in many diseases. Principal cell death pathways include apoptosis, autophagy, necrosis, mitotic catastrophe, etc. Knowledge of cell death pathways and the reason the cell chooses to die are key factors to understand the disease, the way it affects the cellular system and subsequent drug discovery. This study is focused on developing genetically encoded Förster Resonance Energy Transfer (FRET) based biosensors to identify autophagy pathways in vitro. FRET is an energy transfer phenomenon that occurs between two spectrum-overlapping fluorophores that are within 10nm of each other. The design of the sensor is based on enzyme-substrate dynamics and consists of a reporter gene fused between fluorescent proteins. Additionally, FRET-based protease assay has been used to determine the kinetics of Atg4A, an enzyme involved in autophagy. The kinetic parameters Km, kcat, kcat /Km were derived using real-time detection methods. A further aim of this research is to transfect the sensor in H460 lung cancer cell line to identify the type of death that the cell chooses on treatment with drugs.","PeriodicalId":92699,"journal":{"name":"Open access journal of biomedical engineering and biosciences","volume":"27 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79614559","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 detection of infection in clinical practice is time consuming and laborious. The ability to monitor infection status in real time, for example in wounds, would enable earlier intervention and improved prognosis. This study describes the real time electrochemical detection of clinically important wound pathogens. Using impedance spectroscopy in conjunction with a normalisation approach, the growth of Proteus mirabilis in LB medium was detected 1 hour after sample inoculation at a cell concentration of 7.4 x106 CFU/mL. Furthermore, a significant decrease in charge transfer resistance arose over the 24 hour growth period (p = 0.009), modelled using a simple equivalent circuit. Additional experiments performed in 0.9% w/v NaCl (where growth was inhibited) indicated that processes facilitated by this organism’s metabolism and growth dominated the impedance response in LB medium. Further, immediate detection of a high concentration of P. mirabilis cells was possible (5.0 x108 CFU/mL). Finally, a simulated wound fluid was used to explore the growths of P. mirabilis, Pseudomonas aeruginosa and Staphylococcus aureus in a more complex environment representative of a wound bed. Similar changes to normalised impedance were observed, and decreases in normalised phase emerged as a characteristic indicator of bacterial growth. The ability of these low cost sensors to rapidly detect bacteria highlights their potential for adoption into point-of-care infection monitoring devices.
{"title":"Rapidly Detected Common Wound Pathogens via Easy-to-Use Electrochemical Sensors","authors":"A. Hannah, A. C. Ward, P. Connolly","doi":"10.11159/jbeb.2021.002","DOIUrl":"https://doi.org/10.11159/jbeb.2021.002","url":null,"abstract":"The detection of infection in clinical practice is time consuming and laborious. The ability to monitor infection status in real time, for example in wounds, would enable earlier intervention and improved prognosis. This study describes the real time electrochemical detection of clinically important wound pathogens. Using impedance spectroscopy in conjunction with a normalisation approach, the growth of Proteus mirabilis in LB medium was detected 1 hour after sample inoculation at a cell concentration of 7.4 x106 CFU/mL. Furthermore, a significant decrease in charge transfer resistance arose over the 24 hour growth period (p = 0.009), modelled using a simple equivalent circuit. Additional experiments performed in 0.9% w/v NaCl (where growth was inhibited) indicated that processes facilitated by this organism’s metabolism and growth dominated the impedance response in LB medium. Further, immediate detection of a high concentration of P. mirabilis cells was possible (5.0 x108 CFU/mL). Finally, a simulated wound fluid was used to explore the growths of P. mirabilis, Pseudomonas aeruginosa and Staphylococcus aureus in a more complex environment representative of a wound bed. Similar changes to normalised impedance were observed, and decreases in normalised phase emerged as a characteristic indicator of bacterial growth. The ability of these low cost sensors to rapidly detect bacteria highlights their potential for adoption into point-of-care infection monitoring devices.","PeriodicalId":92699,"journal":{"name":"Open access journal of biomedical engineering and biosciences","volume":"20 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83026401","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":"Using Deep Learning for Efficient Diagnoses of COVID-19, Viral Illnesses (Other than COVID-19), and Bacterial Illnesses","authors":"V. Vibha","doi":"10.11159/jbeb.2021.005","DOIUrl":"https://doi.org/10.11159/jbeb.2021.005","url":null,"abstract":"","PeriodicalId":92699,"journal":{"name":"Open access journal of biomedical engineering and biosciences","volume":"32 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85143422","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}
Laura Lazzari, P. McCarthy, Jonathan Martin, S. Schultz
{"title":"Temporal Selectivity in the LGN: A Computational Neuroscience Approach","authors":"Laura Lazzari, P. McCarthy, Jonathan Martin, S. Schultz","doi":"10.11159/jbeb.2020.003","DOIUrl":"https://doi.org/10.11159/jbeb.2020.003","url":null,"abstract":"","PeriodicalId":92699,"journal":{"name":"Open access journal of biomedical engineering and biosciences","volume":"9 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90850339","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}
G. Melito, A. Jafarinia, T. Hochrainer, K. Ellermann
Aortic dissection is a severe cardiovascular disease caused by the occurrence of a tear in the aortic wall. As a result, the blood penetrates the wall and makes a new blood channel called the false lumen. The haemodynamic conditions in the false lumen may contribute to the formation of thrombi, which influence the patient's diagnosis and outcomes. In this study, the focus is on a haemodynamic-based model of thrombus formation. Since the model construction entails uncertainties in the model parameters, a variance-based sensitivity analysis is performed. Thrombus formation at a backward-facing step is considered as a benchmark for the numerical simulations and sensitivity analysis. This geometry is capable of representing the main contributions of the model in thrombus formation. The study aims at improving the understanding of the model's structure and at preparing model simplifications to enable efficient patient-specific simulations in the future. A polynomial chaos expansion is employed as a surrogate model, from which the quantitative sensitivity indices are derived. In this study, nine model parameters are selected, whose proper values are not well known. The model responses taken into account are the maximum volume fraction of thrombus, its time development, and the thrombus growth rate. The results show that the model lends itself to model reduction since some of the model parameters show little to no influence on the model's outputs. A threshold value related to the concentration of bounded platelets and the bounded platelets reaction rate are identified as the key input parameters dominating the thrombus model predictions in the current geometry. Furthermore, the introduced thrombus characteristic growth time is driven by both the aforementioned variables.
{"title":"Sensitivity Analysis of a Phenomenological Thrombosis Model and Growth Rate Characterisation","authors":"G. Melito, A. Jafarinia, T. Hochrainer, K. Ellermann","doi":"10.11159/jbeb.2020.004","DOIUrl":"https://doi.org/10.11159/jbeb.2020.004","url":null,"abstract":"Aortic dissection is a severe cardiovascular disease caused by the occurrence of a tear in the aortic wall. As a result, the blood penetrates the wall and makes a new blood channel called the false lumen. The haemodynamic conditions in the false lumen may contribute to the formation of thrombi, which influence the patient's diagnosis and outcomes. In this study, the focus is on a haemodynamic-based model of thrombus formation. Since the model construction entails uncertainties in the model parameters, a variance-based sensitivity analysis is performed. Thrombus formation at a backward-facing step is considered as a benchmark for the numerical simulations and sensitivity analysis. This geometry is capable of representing the main contributions of the model in thrombus formation. The study aims at improving the understanding of the model's structure and at preparing model simplifications to enable efficient patient-specific simulations in the future. A polynomial chaos expansion is employed as a surrogate model, from which the quantitative sensitivity indices are derived. In this study, nine model parameters are selected, whose proper values are not well known. The model responses taken into account are the maximum volume fraction of thrombus, its time development, and the thrombus growth rate. The results show that the model lends itself to model reduction since some of the model parameters show little to no influence on the model's outputs. A threshold value related to the concentration of bounded platelets and the bounded platelets reaction rate are identified as the key input parameters dominating the thrombus model predictions in the current geometry. Furthermore, the introduced thrombus characteristic growth time is driven by both the aforementioned variables.","PeriodicalId":92699,"journal":{"name":"Open access journal of biomedical engineering and biosciences","volume":"44 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86850120","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}
A. Jafarinia, T. S. Müller, U. Windberger, G. Brenn, T. Hochrainer
Aortic dissection is a disease caused by the occurrence of a rupture in the innermost layer of the aortic wall. Due to the pulsation of the heart, blood penetrates through the tear between the layers of the aortic wall, which causes a new, so-called false lumen (FL). The local haemodynamic conditions in the FL significantly contribute to clotting of blood, so the formation of a thrombus. The level of thrombosis in the FL affects patients’ prognosis and chances of survival, in which a complete thrombosis is usually beneficial. In recent studies on platelet deposition in the FL, it is demonstrated that haemodynamic conditions influence on platelet activation and aggregation, effectively boosting in regions of recirculation. Blood coagulation has the highest chance of occurrence in these recirculation regions within the FL. Considering the dominant influence of shear rate in FL thrombosis, the non-Newtonian rheological properties and behaviour of blood play a crucial role. The most important rheological factor is the volume fraction of red blood cells in the blood, i.e., the haematocrit value (HCT), which affects the shear rate dependent viscosity and the yield stress observed in regions of low shear rate and stress, respectively, in the blood flow. In the current work, the influence of the haematocrit value on thrombosis in the FL is simulated. The simulations are done in idealized aortic dissection phantom models employing HCT-dependent non-Newtonian haemodynamics. The value for the HCT was varied within a physiological range. On the one hand, an increase in the total volume of thrombus in time was found for all HCT values. On the other hand, with increasing HCT values, less thrombus is formed in the FL. This suggests that high HCT values impede thrombus formation due to rheological effects and that patients with higher haematocrit values have less chance of benefiting from complete thrombosis in the FL.
{"title":"Blood Rheology Influence on False Lumen Thrombosis in Type B Aortic Dissection","authors":"A. Jafarinia, T. S. Müller, U. Windberger, G. Brenn, T. Hochrainer","doi":"10.11159/jbeb.2020.002","DOIUrl":"https://doi.org/10.11159/jbeb.2020.002","url":null,"abstract":"Aortic dissection is a disease caused by the occurrence of a rupture in the innermost layer of the aortic wall. Due to the pulsation of the heart, blood penetrates through the tear between the layers of the aortic wall, which causes a new, so-called false lumen (FL). The local haemodynamic conditions in the FL significantly contribute to clotting of blood, so the formation of a thrombus. The level of thrombosis in the FL affects patients’ prognosis and chances of survival, in which a complete thrombosis is usually beneficial. In recent studies on platelet deposition in the FL, it is demonstrated that haemodynamic conditions influence on platelet activation and aggregation, effectively boosting in regions of recirculation. Blood coagulation has the highest chance of occurrence in these recirculation regions within the FL. Considering the dominant influence of shear rate in FL thrombosis, the non-Newtonian rheological properties and behaviour of blood play a crucial role. The most important rheological factor is the volume fraction of red blood cells in the blood, i.e., the haematocrit value (HCT), which affects the shear rate dependent viscosity and the yield stress observed in regions of low shear rate and stress, respectively, in the blood flow. In the current work, the influence of the haematocrit value on thrombosis in the FL is simulated. The simulations are done in idealized aortic dissection phantom models employing HCT-dependent non-Newtonian haemodynamics. The value for the HCT was varied within a physiological range. On the one hand, an increase in the total volume of thrombus in time was found for all HCT values. On the other hand, with increasing HCT values, less thrombus is formed in the FL. This suggests that high HCT values impede thrombus formation due to rheological effects and that patients with higher haematocrit values have less chance of benefiting from complete thrombosis in the FL.","PeriodicalId":92699,"journal":{"name":"Open access journal of biomedical engineering and biosciences","volume":"28 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84220429","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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