Access to Expensive Therapies and Diagnostics for Kidney Care in Switzerland.

Key Points: Inconsistent responses to the prior approval process for similar patients may lead to inequities in access to optimal care. The prior authorizations process leads to frustration among nephrologists and may contribute to moral distress. The prior authorizations process may lead to important delays in kidney care.

DLSIA: Deep Learning for Scientific Image Analysis.

DLSIA (Deep Learning for Scientific Image Analysis) is a Python-based machine learning library that empowers scientists and researchers across diverse scientific domains with a range of customizable convolutional neural network (CNN) architectures for a wide variety of tasks in image analysis to be used in downstream data processing. DLSIA features easy-to-use architectures, such as autoencoders, tunable U-Nets and parameter-lean mixed-scale dense networks (MSDNets). Additionally, this article introduces sparse mixed-scale networks (SMSNets), generated using random graphs, sparse connections and dilated convolutions connecting different length scales.

Long-term, non-invasive FTIR detection of low-dose ionizing radiation exposure.

Non-invasive methods of detecting radiation exposure show promise to improve upon current approaches to biological dosimetry in ease, speed, and accuracy. Here we developed a pipeline that employs Fourier transform infrared (FTIR) spectroscopy in the mid-infrared spectrum to identify a signature of low dose ionizing radiation exposure in mouse ear pinnae over time. Mice exposed to 0.

Deploying Machine Learning Based Segmentation for Scientific Imaging Analysis at Synchrotron Facilities.

Scientific user facilities present a unique set of challenges for image processing due to the large volume of data generated from experiments and simulations. Furthermore, developing and implementing algorithms for real-time processing and analysis while correcting for any artifacts or distortions in images remains a complex task, given the computational requirements of the processing algorithms. In a collaborative effort across multiple Department of Energy national laboratories, the "MLExchange" project is focused on addressing these challenges.

Eculizumab in Shiga toxin-producing Escherichia coli hemolytic uremic syndrome: a systematic review.

Background: Infection-associated hemolytic uremic syndrome (IA-HUS), most often due to infection with Shiga toxin-producing bacteria, mainly affects young children. It can be acutely life-threatening, as well as cause long-term kidney and neurological morbidity. Specific treatment with proven efficacy is lacking.