In silico analysis of Hsp70 genes in Ctenopharyngodon idella and their expression profiles in response to environmental stresses

AbstractHeat shock protein 70 (Hsp70) is a crucial member of the Hsp family, which is present in many animals, and acts as a chaperone to protect the organism from damage caused by various environmental stresses, particularly unfavorable temperatures. In this study, we used homologous gene search and domain analysis to identify sixteen Hsp70 genes (named as CiHSP genes) from the genome of grass carp (Ctenopharyngodon idella). These genes were classified into ten subfamilies based on their conserved structures and phylogenetic analysis. To investigate the biological functions of CiHSP genes in grass carp, we analyzed public RNA-Seq data, and found that most members of the CiHSP gene family were highly expressed in the brain and kidney, suggesting potential roles in protecting brain cells and participating in fish immunological processes. Additionally, these CiHSP genes were characterized as responding to high density and high temperature stress, with most members significantly upregulated under high temperature conditions. These findings demonstrate the critical roles of CiHSP genes in grass carp development and their response to environmental stress, which will provide valuable insights for determining their function and potential application in fish production in the future

Deep learning techniques for imaging diagnosis and treatment of aortic aneurysm

ObjectiveThis 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.MethodsA 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.ResultsDeep learning models demonstrated significant advancements in AA management. For screening and diagnosis, models like ResNet achieved high accuracy in identifying AA in non-contrast CT scans. In segmentation, techniques like U-Net provided precise measurements of aneurysm size and volume, crucial for surgical planning. Deep learning also assisted in surgical procedures by accurately predicting stent placement and postoperative complications. Furthermore, models were able to predict AA progression and patient prognosis with high accuracy.ConclusionDeep learning technologies show remarkable potential in enhancing the diagnosis, treatment, and management of AA. These advancements could lead to more accurate and personalized patient care, improving outcomes in AA management