Exploring different processes for starch extraction from microalgae and synthesis of starch-chitosan plastic films.

Microalgae could become a more sustainable starch source than conventional crops. However, available refinery processes are lacking. In this study, we develop different innovative processes to refine microalgal starch and obtaining starch-based bioplastics.

Temporal Changes on Postgadolinium MR Vessel Wall Imaging Captures Enhancement Kinetics of Intracranial Atherosclerotic Plaques and Aneurysms.

Background And Purpose: Analysis of vessel wall contrast kinetics (ie, wash-in/washout) is a promising method for the diagnosis and risk-stratification of intracranial atherosclerotic disease plaque (ICAD-P) and the intracranial aneurysm walls (IA-W). We used black-blood MR imaging or MR vessel wall imaging to evaluate the temporal relationship of gadolinium contrast uptake kinetics in ICAD-Ps and IA-Ws compared with normal anatomic reference structures.

Materials And Methods: Patients with ICAD-Ps or IAs who underwent MR vessel wall imaging with precontrast, early postcontrast (5-15 minutes), and delayed postcontrast (20-30 minutes) 3D T1-weighted TSE sequences were retrospectively studied.

Cardioembolic Stroke.

Cardioembolism accounts globally for around 25% of ischemic strokes and is more often associated with higher rates of morbidity and mortality. Potential sources of cardioembolism into the intracranial circulation include paradoxic embolism, dysrhythmias, structural heart disease, and valvular heart disease. To identify the etiology of a patient's ischemic stroke, thorough investigation of the intracardiac structures, assessment of dysrhythmias, and consideration of high-risk events such as cardiac surgery are crucial.

Automated intracranial vessel segmentation of 4D flow MRI data in patients with atherosclerotic stenosis using a convolutional neural network.

Introduction: Intracranial 4D flow MRI enables quantitative assessment of hemodynamics in patients with intracranial atherosclerotic disease (ICAD). However, quantitative assessments are still challenging due to the time-consuming vessel segmentation, especially in the presence of stenoses, which can often result in user variability. To improve the reproducibility and robustness as well as to accelerate data analysis, we developed an accurate, fully automated segmentation for stenosed intracranial vessels using deep learning.