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.

Stenosis due to neointimal hyperplasia (NIH) is among the major causes of peripheral graft failure. Its link to abnormal hemodynamics in the graft is complex, and isolated use of hemodynamic markers is insufficient to fully capture its progression. Here, a computational model of NIH growth is presented, establishing a link between computational fluid dynamics simulations of flow in the lumen and a biochemical model representing NIH growth mechanisms inside the vessel wall. For all three patients analyzed, NIH at proximal and distal anastomoses was simulated by the model, with values of stenosis comparable to the computed tomography scans.

Contactless and remote measurement technologies have recently been at the core of the digital shopping experience, not only for their potential to provide better customization but to revolutionize the way customers shop -from minimizing returns due to misfits to reducing environmental impact by producing on-demand. For small-batch retailers, this is key in ensuring minimal waste and streamlining the consumer journey. From the consumer's point of view, fit is also an unaddressed issue. After conducting user interviews on 60+ women, we identified 70% of respondents reported difficulties finding proper fitting apparel by designers. Reports by Mintel [1] and Deloitte [2] also confirm that the issue of fit is paramount in the industry. Here we introduce "PS for you", our AI-powered solution to provide a fast and scalable platform for designers to obtain reliable measurements and for shoppers to find the right fit.Our solution uses computer vision models paired with a proprietary layer of post-processing algorithms to extract personalized measurements from photos that the user can upload from any device. The solution is deployed on a serverless CPU using Amazon Web Services (AWS) for easier interoperability and faster run times.To test the effectiveness of this approach, we collected 40 images from 20 volunteers of different body shapes and sizes (including petite, short-stature, and athletic). The system was able to detect measurements for waist, hips, and bust with an average difference of less than 3% with respect to tapemeasured data. The median difference remained <1in, a significant improvement which is 3x more reflective of true size than the current universal sizing methods [3].Working on this technology allows us to foster a new connection between shoppers interested in customization, sustainability, and comfort and designers who are interested in decreasing returns, eliminating waste, and understanding more about their consumers. Technology will not just help but can create opportunities to build new systems, without heavy manual support that connects and fosters experiences anywhere in the World. By using the right technology and data, responsibly, we're building a product that will open up opportunities in connected markets as well as enhancing existing ones.

Neointimal hyperplasia (NIH) is a major obstacle to graft patency in the peripheral arteries. A complex interaction of biomechanical factors contribute to NIH development and progression, and although haemodynamic markers such as wall shear stress have been linked to the disease, these have so far been insufficient to fully capture its behaviour. Using a computational model linking computational fluid dynamics (CFD) simulations of blood flow with a biochemical model representing NIH growth mechanisms, we analyse the effect of compliance mismatch, due to the presence of surgical stitches and/or to the change in distensibility between artery and vein graft, on the haemodynamics in the lumen and, subsequently, on NIH progression. The model enabled to simulate NIH at proximal and distal anastomoses of three patient-specific end-to-side saphenous vein grafts under two compliance-mismatch configurations, and a rigid wall case for comparison, obtaining values of stenosis similar to those observed in the computed tomography (CT) scans. The maximum difference in time-averaged wall shear stress between the rigid and compliant models was 3.4 Pa, and differences in estimation of NIH progression were only observed in one patient. The impact of compliance on the haemodynamic-driven development of NIH was small in the patient-specific cases considered.

Introduction: Neointimal hyperplasia (NIH) is a major obstacle to the long term patency of peripheral vascular grafts. The disease has a complex aetiology which is influenced, among other phenomena, by mechanical forces such as shear stresses acting on the arterial wall. Objectives: The aim of this work is to assess the impact of haemodynamic factors in a patient-specific, multi-scale modelling framework developed using computational fluid dynamics (CFD) and mathematical biology, for the quantification of NIH growth. Methods: Simulations were performed on datasets from two femoro-popliteal and one femoro-distal bypass patients. Patient data (imaging and haemodynamics) was obtained from Yale University School of Medicine. In this work, smooth muscle cells and collagen in the vascular tissue were modelled using ordinary differential equations. These were linked to wall shear stress (computed using CFD) through its relationship with nitric oxide and growth factors. Results: Results obtained by simulating the growth via the combined CFD and mathematical biology framework seems to outperform analyses performed with haemodynamic indices (obtained by CFD) alone, enabling to pinpoint the locations of NIH and to achieve quantification of its growth. For instance, when only accounting for the time-averaged wall shear stress in the rigid wall model, luminal narrowing was underestimated by up to 19.3%, while when also accounting for oscillatory behaviour the model was able to reach the amount of occlusion present (as measured in the CT scans), with an average overestimate of 10%. Conclusion: The study presents a patient-specific, multiscale simulation framework to model NIH progression. While a previous version of the model underestimated the occlusion of the lumen due to NIH, the results presented show an improvement in estimating occlusion by accounting for movement of the arterial wall, oscillatory behaviour of shear stress and non-Newtonian properties of blood viscosity.