Deep learning techniques for imaging diagnosis and treatment of aortic aneurysm.

Objective: This study aims to review the application of deep learning techniques in the imaging diagnosis and treatment of aortic aneurysm (AA), focusing on screening, diagnosis, lesion segmentation, surgical assistance, and prognosis prediction.

Methods: A comprehensive literature review was conducted, analyzing studies that utilized deep learning models such as Convolutional Neural Networks (CNNs) in various aspects of AA management. The review covered applications in screening, segmentation, surgical planning, and prognosis prediction, with a focus on how these models improve diagnosis and treatment outcomes.

Synchronous enhancement of astaxanthin and lipid accumulation in Haematococcus lacustris through co-mutation of ethanol and atmospheric and room temperature plasma: Exploration of characteristics and underlying mechanisms.

Haematococcus lacustris is a precious algal species renowned for its ability to simultaneous production of astaxanthin and lipid. However, its slow growth rate necessitates the development of appropriate mutagenesis methodologies to effectively enhance its synchronous production of both astaxanthin and lipid. This study introduced the co-mutation of Atmospheric and Room Temperature Plasma (ARTP) and ethanol.