Search

The analysis of mechanobiology of arterial tissues remains an important topic of research for cardiovascular pathologies evaluation. In the current state of the art, the gold standard to characterize the tissue mechanical behavior is represented by experimental tests, requiring the harvesting of ex-vivo specimens. In recent years though, image-based techniques for the in vivo estimation of arterial tissue stiffness were presented. The aim of this study is to define a new approach to provide local distribution of arterial stiffness, estimated as the linearized Young’s Modulus, based on the knowledge of in vivo patient-specific imaging data. In particular, the strain and stress are estimated with sectional contour length ratios and a Laplace hypothesis/inverse engineering approach, respectively, and then used to calculate the Young’s Modulus. After describing the method, this was validated by using a set of Finite Element simulations as input. In particular, idealized cylinder and elbow shapes plus a single patient-specific geometry were simulated. Different stiffness distributions were tested for the simulated patient-specific case. After the validation from Finite Element data, the method was then applied to patient-specific ECG-gated Computed Tomography data by also introducing a mesh morphing approach to map the aortic surface along the cardiac phases. The validation process revealed satisfactory results. In the simulated patient-specific case, root mean square percentage errors below 10% for the homogeneous distribution and below 20% for proximal/distal distribution of stiffness. The method was then successfully used on the three ECG-gated patient-specific cases. The resulting distributions of stiffness exhibited significant heterogeneity, nevertheless the resulting Young’s moduli were always contained within the 1–3 MPa range, which is in line with literature.

We consider the problem of inferring the probability distribution of flux configurations in metabolic network models from empirical flux data. For the simple case in which experimental averages are to be retrieved, data are described by a Boltzmann-like distribution ($\propto e^{F/T}$) where $F$ is a linear combination of fluxes and the `temperature' parameter $T\geq 0$ allows for fluctuations. The zero-temperature limit corresponds to a Flux Balance Analysis scenario, where an objective function ($F$) is maximized. As a test, we have inverse modeled, by means of Boltzmann learning, the catabolic core of Escherichia coli in glucose-limited aerobic stationary growth conditions. Empirical means are best reproduced when $F$ is a simple combination of biomass production and glucose uptake and the temperature is finite, implying the presence of fluctuations. The scheme presented here has the potential to deliver new quantitative insight on cellular metabolism. Our implementation is however computationally intensive, and highlights the major role that effective algorithms to sample the high-dimensional solution space of metabolic networks can play in this field., Comment: 4 pages, comments welcome

We introduce an analytically solvable model for a fragmented object that, despite of a low degree of randomness and of the extreme simplicity of the breaking process, displays non self-averaging effects in its thermodynamic limit., Comment: 7 pages

Lack of self-averaging originates in many disordered models from a fragmentation of the phase space where the sizes of the fragments remain sample-dependent in the thermodynamic limit. On the basis of new results in percolation theory, we give here an argument in favour of the conjecture that critical two dimensional percolation on the square lattice lacks of self-averaging., Comment: 6 pages

Left ventricular free-wall rupture (LVFWR) is an uncommon but serious mechanical complication of acute myocardial infarction (AMI). Surgical repair, though challenging, is the only definitive treatment. However, given the rarity of this condition, results following surgery are still not well established. The aim of this study was to review a multicenter experience with the surgical management of post-infarction LVFWR and analyze the associated early outcomes. Background: Left ventricular free-wall rupture (LVFWR) is an uncommon but serious mechanical complication of acute myocardial infarction. Surgical repair, though challenging, is the only definitive treatment. Given the rarity of this condition, however, results after surgery are still not well established. The aim of this study was to review a multicenter experience with the surgical management of post-infarction LVFWR and analyze the associated early outcomes. Methods: Using the CAUTION (Mechanical Complications of Acute Myocardial Infarction: an International Multicenter Cohort Study) database, we identified 140 patients who were surgically treated for post–acute myocardial infarction LVFWR in 15 different centers from 2001 to 2018. The main outcome measured was operative mortality. Multivariate analysis was carried out by constructing a logistic regression model to identify predictors of postoperative mortality. Results: The mean age of patients was 69.4 years. The oozing type of LVFWR was observed in 79 patients (56.4%), and the blowout type in 61 (43.6%). Sutured repair was used in the 61.4% of cases. The operative mortality rate was 36.4%. Low cardiac output syndrome was the main cause of perioperative death. Myocardial rerupture after surgery occurred in 10 patients (7.1%). Multivariable analysis revealed that preoperative left ventricular ejection fraction (P