Computer-Assisted Algorithm for Quantification of Fibrosis by Native Cardiac CT: A Pilot Study.

Background/Objectives: Recent advances in artificial intelligence, particularly in cardiac imaging, can potentially enhance patients' diagnosis and prognosis and identify novel imaging markers. We propose an automated, computer-aided algorithm utilizing native cardiac computed tomography (CT) imaging to identify myocardial fibrosis. This study aims to evaluate its performance compared to CMR markers of fibrosis in a cohort of patients diagnosed with breast cancer. Methods: The study included patients diagnosed with early HER2+ breast cancer, who presented LV dysfunction (LVEF < 50%) and myocardial fibrosis detected on CMR at the time of diagnosis. The patients were also evaluated by cardiac CT, and the extracted images were processed for the implementation of the automatic, computer-assisted algorithm, which marked as fibrosis every pixel that fell within the range of 60-90 HU. The percentage of pixels with fibrosis was subsequently compared with CMR parameters. Results: A total of eight patients (n = 8) were included in the study. High positive correlations between the algorithm's result and the ECV fraction (r = 0.59, p = 0.126) and native T1 (r = 0.6, p = 0.112) were observed, and a very high positive correlation with LGE of the LV(g) and the LV-LGE/LV mass percentage (r = 0.77, p = 0.025; r = 0.81, p = 0.015). A very high negative correlation was found with GLS (r = -0.77, p = 0.026). The algorithm presented an intraclass correlation coefficient of 1 (95% CI 0.99-1), p < 0.001. Conclusions: The present pilot study proposes a novel promising imaging marker for myocardial fibrosis, generated by an automatic algorithm based on native cardiac CT images.

Mathematical Modeling and Optimization of Lactobacillus Species Single and Co-Culture Fermentation Processes in Wheat and Soy Dough Mixtures.

To improve food production via fermentation with co-cultures of microorganisms (e.g., multiple lactic acid bacteria-LAB strains), one must fully understand their metabolism and interaction patterns in various conditions. For example, LAB can bring added quality to bread by releasing several bioactive compounds when adding soy flour to wheat flour, thus revealing the great potential for functional food development. In the present work, the fermentation of three soy and wheat flour mixtures is studied using single cultures and co-cultures of Lactobacillus plantarum and Lactobacillus casei. Bio-chemical processes often require a significant amount of time to obtain the optimal amount of final product; creating a mathematical model can gain important information and aids in the optimization of the process. Consequently, mathematical modeling is used to optimize the fermentation process by following these LAB's growth kinetics and viability. The present work uses both multiple regression and artificial neural networks (ANN) to obtain the necessary mathematical model, useful in both prediction and process optimization. The main objective is to find a model with optimal performances, evaluated using an ANOVA test. To validate each obtained model, the simulation results are compared with the experimental data.

Organ-On-A-Chip: A Survey of Technical Results and Problems.

Organ-on-a-chip (OoC), also known as micro physiological systems or "tissue chips" have attracted substantial interest in recent years due to their numerous applications, especially in precision medicine, drug development and screening. Organ-on-a-chip devices can replicate key aspects of human physiology, providing insights into the studied organ function and disease pathophysiology. Moreover, these can accurately be used in drug discovery for personalized medicine. These devices present useful substitutes to traditional preclinical cell culture methods and can reduce the use of in vivo animal studies. In the last few years OoC design technology has seen dramatic advances, leading to a wide range of biomedical applications. These advances have also revealed not only new challenges but also new opportunities. There is a need for multidisciplinary knowledge from the biomedical and engineering fields to understand and realize OoCs. The present review provides a snapshot of this fast-evolving technology, discusses current applications and highlights advantages and disadvantages for biomedical approaches.