Incorporating patient-specific prior clinical knowledge to improve clinical target volume auto-segmentation generalisability for online adaptive radiotherapy of rectal cancer: A multicenter validation.

Background & Purpose: Deep learning (DL) based auto-segmentation has shown to be beneficial for online adaptive radiotherapy (OART). However, auto-segmentation of clinical target volumes (CTV) is complex, as clinical interpretations are crucial in their definition. The resulting variation between clinicians and institutes hampers the generalizability of DL networks.

Pancreatic Intraepithelial Neoplasia Revealed by Diffusion-Tensor MRI.

Objectives: Detecting premalignant lesions for pancreatic ductal adenocarcinoma, mainly pancreatic intraepithelial neoplasia (PanIN), is critical for early diagnosis and for understanding PanIN biology. Based on PanIN's histology, we hypothesized that diffusion tensor imaging (DTI) and T2* could detect PanIN.

Materials And Methods: DTI was explored for the detection and characterization of PanIN in genetically engineered mice (KC, KPC).

A computational framework for processing time-series of earth observation data based on discrete convolution: global-scale historical Landsat cloud-free aggregates at 30 m spatial resolution.

Processing large collections of earth observation (EO) time-series, often petabyte-sized, such as NASA's Landsat and ESA's Sentinel missions, can be computationally prohibitive and costly. Despite their name, even the Analysis Ready Data (ARD) versions of such collections can rarely be used as direct input for modeling because of cloud presence and/or prohibitive storage size. Existing solutions for readily using these data are not openly available, are poor in performance, or lack flexibility.

Development of an Electrochemical Paper-Based Device Modified with Functionalized Biochar for the Screening of Paracetamol in Substandard Medicines.

The global prevalence of counterfeit and low-quality pharmaceuticals poses significant health risks and challenges in medical treatments, creating a need for rapid and reliable drug screening technologies. This study introduces a cost-effective electrochemical paper-based device (ePAD) modified with functionalized bamboo-derived biochar (BCF) for the detection of paracetamol in substandard medicines. The sensor was fabricated using a custom 3D-printed stencil in PLA, designed for efficient production, and a 60:40 (m/m) graphite (GR) and glass varnish (GV) conductive ink, resulting in a robust and sensitive platform.