Progressive false lumen aneurysmal degeneration in type B aortic dissection (TBAD) is a complex process with a multi-factorial etiology. Patient-specific computational fluid dynamics (CFD) simulations provide spatial and temporal hemodynamic quantities that facilitate understanding this disease progression. A longitudinal study was performed for a TBAD patient, who was diagnosed with the uncomplicated TBAD in 2006 and treated with optimal medical therapy but received surgery in 2010 due to late complication. Geometries of the aorta in 2006 and 2010 were reconstructed. With registration algorithms, we accurately quantified the evolution of the false lumen, while with CFD simulations we computed several hemodynamic indexes, including the wall shear stress (WSS), and the relative residence time (RRT). The numerical fluid model included large eddy simulation (LES) modeling for efficiently capturing the flow disturbances induced by the entry tears. In the absence of complete patient-specific data, the boundary conditions were based on a specific calibration method. Correlations between hemodynamics and the evolution field in time obtained by registration of the false lumen are discussed. Further testing of this methodology on a large cohort of patients may enable the use of CFD to predict whether patients, with originally uncomplicated TBAD, develop late complications.

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Background With the rapid development of medical treatment, many patients not only consider the survival time, but also care about the quality of life. Changes in physical, psychological and social functions after and during treatment have caused a lot of troubles to patients and their families. Based on the bio-psycho-social medical model theory, mental health plays an important role in treatment. Therefore, it is necessary for medical staff to know the diseases which have high potential to cause psychological trauma and social avoidance (PTSA). Results Firstly, we obtained diseases which can cause PTSA from literatures. Then, we calculated the similarities of related-diseases to build a disease network. The similarities between diseases were based on their known related genes. Then, we obtained these diseases-related proteins from UniProt. These proteins were extracted as the features of diseases. Therefore, in the disease network, each node denotes a disease and contains the information of its related proteins, and the edges of the network are the similarities of diseases. Then, graph convolutional network (GCN) was used to encode the disease network. In this way, each disease’s own feature and its relationship with other diseases were extracted. Finally, Xgboost was used to identify PTSA diseases. Conclusion We developed a novel method ‘GCN-Xgboost’ and compared it with some traditional methods. Using leave-one-out cross-validation, the AUC and AUPR were higher than some existing methods. In addition, case studies have been done to verify our results. We also discussed the trajectory of social avoidance and distress during acute survival of breast cancer patients.

Uncertainties affect the reliability of the numerical simulation of hemodynamics in patient-specific settings and rigorous Uncertainty Quantification (UQ) is in order. This work presents a UQ study on the aorta flow, for assessing the sensitivity of the clinical relevant quantities to the morphology and imprecise knowledge of the inflow boundary condition using the Polynomial Chaos Expansion based Sobol' indices. The geometrical uncertainty is modeled based on a set of longitudinal imaging data of a patient with the abdominal aortic aneurysm. The images of the patient's aorta at different stages of the disease are used to create a map that drives the realistic variation of the reconstructed morphology. The aorta is a peculiar site for hemodynamics, since the flow is highly disturbed due to the high Reynolds number during systole, and the modeling of turbulence helps to avoid the high computational costs. The deconvolution-based Leray model was considered in the past for these simulations. The LES model features problem-dependent numerical parameters to tune. We borrow the same UQ tools used for physical uncertain quantities to assess the sensitivity of the simulations to one of these numerical parameters, the filter radius. The sensitivity of the total kinetic energy, the time average wall shear stress, and the oscillatory shear index are analyzed. The results show that the geometry has the most dominant contribution to the uncertainty of all the quantities of interest. The sensitivity analysis provides confidence intervals for the simulations that quantify the reliability of the numerical predictions.