Sex Differences in Intracranial Aneurysms: A Matched Cohort Study.

Aneurysmal subarachnoid hemorrhage (SAH) predominantly affects women, accounting for 65% of cases. Women have a 1.3 times higher relative risk than men, with the incidence rising particularly in women aged 55-85 years.

Characterization of an At-Risk Population for Nonalcoholic Fatty Liver Disease (NAFLD) in a Primary Care Setting Along the U.S.-Mexico Border.

Introduction: This study aimed to determine the burden of suspected nonalcoholic fatty liver disease (NAFLD) and nonalcoholic steatohepatitis (NASH) in a predominantly Hispanic patient population and explore the utility of the American Gastroenterological Association's NAFLD Clinical Care Pathway (CCP).

Methodology: Electronic medical records ( = 223) were used to divide patients into risk groups based on the amount of metabolic risk factors they presented, diabetic status, or if they presented other liver diseases. Fribosis-4 (FIB-4) scores were used to determine the risk for advanced fibrosis.

Anterior interhemispheric vs. pterional approach in the microsurgical management of anterior communicating artery aneurysms: a comparative analysis employing a novel multidimensional matching-tool.

The surgical management of anterior communicating artery aneurysms (AcomA) is challenging due to their deep midline position and proximity to complex skull base anatomy. This study compares the pterional craniotomy with the interhemispheric approach based on the specific aneurysm angulation. A total of 129 AcomA cases were analyzed, with 50 undergoing microsurgical clipping via either the pterional or interhemispheric approach.

Transformers for colorectal cancer segmentation in CT imaging.

Purpose: Most recently transformer models became the state of the art in various medical image segmentation tasks and challenges, outperforming most of the conventional deep learning approaches. Picking up on that trend, this study aims at applying various transformer models to the highly challenging task of colorectal cancer (CRC) segmentation in CT imaging and assessing how they hold up to the current state-of-the-art convolutional neural network (CNN), the nnUnet. Furthermore, we wanted to investigate the impact of the network size on the resulting accuracies, since transformer models tend to be significantly larger than conventional network architectures.