Study of optimal design of 3D mechanical metamaterials

This thesis aims to develop and extend numerical methods for solving the elastic problem in 2D to a 3D solution. The primary objective is to investigate and identify the modifications and extensions necessary to adapt existing techniques used in 2D to a 3D framework. The focus is on developing a robust and efficient numerical method that accurately models the behavior of elastic materials in three dimensions. The study encompasses several objectives to achieve a comprehensive understanding of 3D material design and topology optimization in the context of mechanical metamaterials. Firstly, an introduction to topology optimization is provided, including the formulation and regularization of the problem, and an explanation of density-based and the Level Set method. The thesis further investigates the SWAN repository’s code, an object-oriented Matlab software, assessing its applicability for conducting simulations. The thesis explores the optimization of both normal materials and metamaterials microstructures. For normal materials, a comparison is made between different optimization approaches, specifically the MMA optimizer utilizing a densitybased method and the Null Space optimizer employing a level set-based method. Additionally, the impact of varying final volume fractions on the optimization outcomes is studied. This investigation provides valuable insights into the influence of different parameter variations on the resulting microstructures and optimization performance. Furthermore, the study focuses on metamaterials microstructures and explores their optimization using the Null Space optimizer, and different α and β values are employed to examine their effects on the final design. The optimization process is also conducted for different final volume fractions to evaluate the influence of volume fraction on metamaterial performance. This study on material design and topology optimization has yielded several important conclusions: the simulations showcased the relationship between dimensionality and convergence speed, with 2D simulations demonstrating faster convergence compared to 3D simulations; analysis of parameters such as the cost function and the number of iterations has been conducted comparing different optimizers, it has been highlighted the challenges and unique considerations involved in optimizing metamaterials; and, overall, the research has contributed to the understanding of optimization processes and the generation of innovative material configurations

Serological response and breakthrough infection after COVID-19 vaccination in patients with cirrhosis and post-liver transplant

BACKGROUND: Vaccine hesitancy and lack of access remain major issues in disseminating COVID-19 vaccination to liver patients globally. Factors predicting poor response to vaccination and risk of breakthrough infection are important data to target booster vaccine programs. The primary aim of the current study was to measure humoral responses to 2 doses of COVID-19 vaccine. Secondary aims included the determination of factors predicting breakthrough infection. METHODS: COVID-19 vaccination and Biomarkers in cirrhosis And post-Liver Transplantation is a prospective, multicenter, observational case-control study. Participants were recruited at 4-10 weeks following first and second vaccine doses in cirrhosis [n = 325; 94% messenger RNA (mRNA) and 6% viral vaccine], autoimmune liver disease (AILD) (n = 120; 77% mRNA and 23% viral vaccine), post-liver transplant (LT) (n = 146; 96% mRNA and 3% viral vaccine), and healthy controls (n = 51; 72% mRNA, 24% viral and 4% heterologous combination). Serological end points were measured, and data regarding breakthrough SARS-CoV-2 infection were collected. RESULTS: After adjusting by age, sex, and time of sample collection, anti-Spike IgG levels were the lowest in post-LT patients compared to cirrhosis (p < 0.0001), AILD (p < 0.0001), and control (p = 0.002). Factors predicting reduced responses included older age, Child-Turcotte-Pugh B/C, and elevated IL-6 in cirrhosis; non-mRNA vaccine in AILD; and coronary artery disease, use of mycophenolate and dysregulated B-call activating factor, and lymphotoxin-α levels in LT. Incident infection occurred in 6.6%, 10.6%, 7.4%, and 15.6% of cirrhosis, AILD, post-LT, and control, respectively. The only independent factor predicting infection in cirrhosis was low albumin level. CONCLUSIONS: LT patients present the lowest response to the SARS-CoV-2 vaccine. In cirrhosis, the reduced response is associated with older age, stage of liver disease and systemic inflammation, and breakthrough infection with low albumin level