Elizabeth Irene Reiser, By And Through Her Guardian, Richard E. Reiser And Eleanor Reiser v. Richard Lohner And Howard Francis, Medical Doctors, And Provo Obstetrical And Gynecology Clinic, Inc., A Professional Corporation : Brief of Defendants-Respondents

Appeal from a Verdict of the Fourth Judicial District Court of Utah County, State of Utah Honorable James S. Sawaya, Presidin

Patient-specific, multi-scale modeling of neointimal hyperplasia in vein grafts

Neointimal hyperplasia is amongst the major causes of failure of bypass grafts. The disease progression varies from patient to patient due to a range of different factors. In this paper, a mathematical model will be used to understand neointimal hyperplasia in individual patients, combining information from biological experiments and patient-specific data to analyze some aspects of the disease, particularly with regard to mechanical stimuli due to shear stresses on the vessel wall. By combining a biochemical model of cell growth and a patient-specific computational fluid dynamics analysis of blood flow in the lumen, remodeling of the blood vessel is studied by means of a novel computational framework. The framework was used to analyze two vein graft bypasses from one patient: a femoro-popliteal and a femoro-distal bypass. The remodeling of the vessel wall and analysis of the flow for each case was then compared to clinical data and discussed as a potential tool for a better understanding of the disease. Simulation results from this first computational approach showed an overall agreement on the locations of hyperplasia in these patients and demonstrated the potential of using new integrative modeling tools to understand disease progression

A systematic review of clinical and biomechanical engineering perspectives on the prediction of restenosis in coronary and peripheral arteries

Objective: Restenosis is a significant complication of revascularization treatments in coronary and peripheral arteries, sometimes necessitating repeated intervention. Establishing when restenosis will happen is extremely difficult due to the interplay of multiple variables and factors. Standard clinical and Doppler ultrasound scans surveillance follow-ups are the only tools clinicians can rely on to monitor intervention outcomes. However, implementing efficient surveillance programs is hindered by health care system limitations, patients’ comorbidities, and compliance. Predictive models classifying patients according to their risk of developing restenosis over a specific period will allow the development of tailored surveillance, prevention programs, and efficient clinical workflows. This review aims to: (1) summarize the state-of-the-art in predictive models for restenosis in coronary and peripheral arteries; (2) compare their performance in terms of predictive power; and (3) provide an outlook for potentially improved predictive models. Methods: We carried out a comprehensive literature review by accessing the PubMed/MEDLINE database according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. The search strategy consisted of a combination of keywords and included studies focusing on predictive models of restenosis published between January 1993 and April 2023. One author independently screened titles and abstracts and checked for eligibility. The rest of the authors independently confirmed and discussed in case of any disagreement. The search of published literature identified 22 studies providing two perspectives—clinical and biomechanical engineering—on restenosis and comprising distinct methodologies, predictors, and study designs. We compared predictive models’ performance on discrimination and calibration aspects. We reported the performance of models simulating reocclusion progression, evaluated by comparison with clinical images. Results: Clinical perspective studies consider only routinely collected patient information as restenosis predictors. Our review reveals that clinical models adopting traditional statistics (n = 14) exhibit only modest predictive power. The latter improves when machine learning algorithms (n = 4) are employed. The logistic regression models of the biomechanical engineering perspective (n = 2) show enhanced predictive power when hemodynamic descriptors linked to restenosis are fused with a limited set of clinical risk factors. Biomechanical engineering studies simulating restenosis progression (n = 2) are able to capture its evolution but are computationally expensive and lack risk scoring for individual patients at specific follow-ups. Conclusions: Restenosis predictive models, based solely on routine clinical risk factors and using classical statistics, inadequately predict the occurrence of restenosis. Risk stratification models with increased predictive power can be potentially built by adopting machine learning techniques and incorporating critical information regarding vessel hemodynamics arising from biomechanical engineering analyses

A modeling and machine learning approach to ECG feature engineering for the detection of ischemia using pseudo-ECG.

Early detection of coronary heart disease (CHD) has the potential to prevent the millions of deaths that this disease causes worldwide every year. However, there exist few automatic methods to detect CHD at an early stage. A challenge in the development of these methods is the absence of relevant datasets for their training and validation. Here, the ten Tusscher-Panfilov 2006 model and the O'Hara-Rudy model for human myocytes were used to create two populations of models that were in concordance with data obtained from healthy individuals (control populations) and included inter-subject variability. The effects of ischemia were subsequently included in the control populations to simulate the effects of mild and severe ischemic events on single cells, full ischemic cables of cells and cables of cells with various sizes of ischemic regions. Action potential and pseudo-ECG biomarkers were measured to assess how the evolution of ischemia could be quantified. Finally, two neural network classifiers were trained to identify the different degrees of ischemia using the pseudo-ECG biomarkers. The control populations showed action potential and pseudo-ECG biomarkers within the physiological ranges and the trends in the biomarkers commonly identified in ischemic patients were observed in the ischemic populations. On the one hand, inter-subject variability in the ischemic pseudo-ECGs precluded the detection and classification of early ischemic events using any single biomarker. On the other hand, the neural networks showed sensitivity and positive predictive value above 95%. Additionally, the neural networks revealed that the biomarkers that were relevant for the detection of ischemia were different from those relevant for its classification. This work showed that a computational approach could be used, when data is scarce, to validate proof-of-concept machine learning methods to detect ischemic events

A simplified method to account for wall motion in patient-specific blood flow simulations of aortic dissection : comparison with fluid-structure interaction

Aortic dissection (AD) is a complex and highly patient-specific vascular condition difficult to treat. Computational fluid dynamics (CFD) can aid the medical management of this pathology, yet its modelling and simulation are challenging. One aspect usually disregarded when modelling AD is the motion of the vessel wall, which has been shown to significantly impact simulation results. Fluid-structure interaction (FSI) methods are difficult to implement and are subject to assumptions regarding the mechanical properties of the vessel wall, which cannot be retrieved non-invasively. This paper presents a simplified 'moving-boundary method' (MBM) to account for the motion of the vessel wall in type-B AD CFD simulations, which can be tuned with non-invasive clinical images (e.g. 2D cine-MRI). The method is firstly validated against the 1D solution of flow through an elastic straight tube; it is then applied to a type-B AD case study and the results are compared to a state-of-the-art, full FSI simulation. Results show that the proposed method can capture the main effects due to the wall motion on the flow field: the average relative difference between flow and pressure waves obtained with the FSI and MBM simulations was less than 1.8% and 1.3%, respectively and the wall shear stress indices were found to have a similar distribution. Moreover, compared to FSI, MBM has the advantage to be less computationally expensive (requiring half of the time of an FSI simulation) and easier to implement, which are important requirements for clinical translatio

Metalliteollisuuden yritysten resurssitarvekartoitus

Tämän opinnäytetyön toimeksiantaja oli Kainuun Etu Oy. Opinnäytetyön tarkoituksena oli selvittää Kainuun ja lähialueiden metalliteollisuuden yritysten resurssitarpeita. Pääasiassa selvityksen alla oli yritysten erityiskoneistustarpeet ja suorittavan tason henkilökunnan koulutustarpeet. Resurssitarvekartoitus tehtiin osana Kajaanin Otanmäkeen suunnitteilla olleen koulutustehtaan perustamisselvitystä. Opinnäytetyön tavoite oli saada tietoa potentiaalisten asiakasyritysten tarpeista, että perustettavassa tehtaassa päätöksiä tekevät henkilöt saavat lisätietoa tai varmistavaa tietoa päätöksenteon tueksi. Tiedon pääasiallinen käyttötarkoitus oli tehtaan alkutuotannon suunnittelu asiakkaiden tarpeita varten. Kyselyn piiriin kuuluvilta Kainuulaisilta yrityksiltä tiedusteltiin myös yrityksen tai yrittäjän halukkuudesta lähteä osakkaaksi tehtaaseen. Resurssitarvekartoitus suoritettiin kvalitatiivisena tutkimuksena. Suunniteltu kyselylomake lähetettiin sähköpostilla ennakkoon päätettyihin yrityksiin, ja siten aineisto kerättiin kyselyyn vastanneiden yritysten vastausten pohjalta. Tutkimuksen tulokset heijastelevat koulutustarpeiden osalta toimialan työvoimapulan vaikutuksia. Tarvetta on etenkin joko suorittavan tason työntekijöistä, tai sitten halutaan tuotannon automaatioon liittyvää koulutusta. Koneistuspuolelta tarvetta löytyi lähinnä raskaasta aarporauksesta. Opinnäytetyön tuloksilla ei luultavasti ole myöhempiä käyttömahdollisuuksia muuten kuin opinnäytetyön toimeksiantajalle, tai vastaavanlaisen kartoituksen suunnittelijalle. Kaikki yritysten lähettämät vastaukset käsiteltiin opinnäytetyön raporttia tehdessä luottamuksellisesti ja nimettömänä.This thesis was commissioned by Kainuun Etu Oy. The purpose was to find out about the nature of resource demands at metal industry companies. The companies were mainly located in the Kainuu and Northern Ostrobothnia regions. The primary resource demands to be examined were the companies' special machining needs and training needs for the companies' executive personnel. The resource demand survey was made as a part of the foundation report for a training workshop that was planned to be founded in Otanmäki, Kajaani. The purpose was to gather information about the needs of the potential business clients, so that the workshop management would get information to support their decision making. The primary purpose of the information was the planning of the workshop production according to the clients' needs. The companies located in the Kainuu region were also asked about their interest in being a shareholder in the planned workshop. The resource demand survey was conducted as qualitative research. The questionnaire was e-mailed to the group of companies, which was decided beforehand. The data was gathered from the companies' answers to the questionnaire. The results of the survey seem to reflect the effects of the labor shortage in the metal industry, especially in the training needs. Companies seem to need either executive personnel or training associated with industrial automation. There were no major machining needs apart from reaming, especially when it comes to machining large and heavy objects. There are probably no later utilization possibilities for this thesis, apart from the client or someone who plans to conduct a similar survey. While writing this thesis report, all the companies' answers were reported with confidentiality and anonymously

Supplementary Material List from Computational tools for clinical support: a multi-scale compliant model for haemodynamic simulations in an aortic dissection based on multi-modal imaging data

Aortic dissection (AD) is a vascular condition with high morbidity and mortality rates. Computational fluid dynamics (CFD) can provide insight into the progression of AD and aid clinical decisions; however, oversimplified modelling assumptions and high computational cost compromise the accuracy of the information and impede clinical translation. To overcome these limitations, a patient-specific CFD multi-scale approach coupled to Windkessel boundary conditions and accounting for wall compliance was developed and used to study an AD patient. A new moving boundary algorithm was implemented to capture wall displacement and a rich <i>in vivo</i> clinical dataset was used to tune model parameters and for validation. Comparisons between <i>in silico</i> and <i>in vivo</i> data showed that this approach successfully captures flow and pressure waves for the patient-specific AD and is able to predict the pressure in the false lumen (FL), a critical variable for the clinical management of the condition. Results showed regions of low and oscillatory wall shear stress which, together with higher diastolic pressures predicted in the FL, may indicate risk of expansion. This study, at the interface of engineering and medicine, demonstrates a relatively simple and computationally efficient approach to account for arterial deformation and wave propagation phenomena in a three-dimensional model of AD, representing a step forward in the use of CFD as potential tool for AD management and clinical support

Investigating the physiology of normothermic ex vivo heart perfusion in an isolated slaughterhouse porcine model used for device testing and training

BACKGROUND: The PhysioHeart™ is a mature acute platform, based isolated slaughterhouse hearts and able to validate cardiac devices and techniques in working mode. Despite perfusion, myocardial edema and time-dependent function degradation are reported. Therefore, monitoring several variables is necessary to identify which of these should be controlled to preserve the heart function. This study presents biochemical, electrophysiological and hemodynamic changes in the PhysioHeart™ to understand the pitfalls of ex vivo slaughterhouse heart hemoperfusion. METHODS: Seven porcine hearts were harvested, arrested and revived using the PhysioHeart™. Cardiac output, SaO2, glucose and pH were maintained at physiological levels. Blood analyses were performed hourly and unipolar epicardial electrograms (UEG), pressures and flows were recorded to assess the physiological performance. RESULTS: Normal cardiac performance was attained in terms of mean cardiac output (5.1 ± 1.7 l/min) and pressures but deteriorated over time. Across the experiments, homeostasis was maintained for 171.4 ± 54 min, osmolarity and blood electrolytes increased significantly between 10 and 80%, heart weight increased by 144 ± 41 g, free fatty acids (- 60%), glucose and lactate diminished, ammonia increased by 273 ± 76% and myocardial necrosis and UEG alterations appeared and aggravated. Progressively deteriorating electrophysiological and hemodynamic functions can be explained by reperfusion injury, waste product intoxication (i.e. hyperammonemia), lack of essential nutrients, ion imbalances and cardiac necrosis as a consequence of hepatological and nephrological plasma clearance absence. CONCLUSIONS: The PhysioHeart™ is an acute model, suitable for cardiac device and therapy assessment, which can precede conventional animal studies. However, observations indicate that ex vivo slaughterhouse hearts resemble cardiac physiology of deteriorating hearts in a multi-organ failure situation and signalize the need for plasma clearance during perfusion to attenuate time-dependent function degradation. The presented study therefore provides an in-dept understanding of the sources and reasons causing the cardiac function loss, as a first step for future effort to prolong cardiac perfusion in the PhysioHeart™. These findings could be also of potential interest for other cardiac platforms