Personalized deep learning auto-segmentation models for adaptive fractionated magnetic resonance-guided radiation therapy of the abdomen.

Background: Manual contour corrections during fractionated magnetic resonance (MR)-guided radiotherapy (MRgRT) are time-consuming. Conventional population models for deep learning auto-segmentation might be suboptimal for MRgRT at MR-Linacs since they do not incorporate manual segmentation from treatment planning and previous fractions.

Purpose: In this work, we investigate patient-specific (PS) auto-segmentation methods leveraging expert-segmented planning and prior fraction MR images (MRIs) to improve auto-segmentation on consecutive treatment days.

MRgRT real-time target localization using foundation models for contour point tracking and promptable mask refinement.

. This study aimed to evaluate two real-time target tracking approaches for magnetic resonance imaging (MRI) guided radiotherapy (MRgRT) based on foundation artificial intelligence models..

Deep learning based super-resolution for CBCT dose reduction in radiotherapy.

Background: Cone-beam computed tomography (CBCT) is a crucial daily imaging modality in image-guided and adaptive radiotherapy. However, the use of ionizing radiation in CBCT imaging increases the risk of secondary cancers, which is particularly concerning for pediatric patients. Deep learning super-resolution has shown promising results in enhancing the resolution of photographic and medical images but has not yet been explored in the context of CBCT dose reduction.

Aβ status assessment in a hypothetical scenario prior to treatment with disease-modifying therapies: Evidence from 10-year real-world experience at university memory clinics.

Introduction: With the advent of disease-modifying therapies, accurate assessment of biomarkers indicating the presence of disease-associated amyloid beta (Aβ) pathology becomes crucial in patients with clinically suspected Alzheimer's disease (AD). We evaluated Aβ levels in cerebrospinal fluid (Aβ CSF) and Aβ levels in positron emission tomography (Aβ PET) biomarkers in a real-world memory-clinic setting to develop an efficient algorithm for clinical use.

Methods: Patients were evaluated for AD-related Aβ pathology from two independent cohorts (Ludwig Maximilian University [LMU],  = 402, and Medical University of Vienna [MUV],  = 144).

Spatial and temporal coordination of Duox/TrpA1/Dh31 and IMD pathways is required for the efficient elimination of pathogenic bacteria in the intestine of larvae.

Multiple gut antimicrobial mechanisms are coordinated in space and time to efficiently fight foodborne pathogens. In , production of reactive oxygen species (ROS) and antimicrobial peptides (AMPs) together with intestinal cell renewal play a key role in eliminating gut microbes. A complementary mechanism would be to isolate and treat pathogenic bacteria while allowing colonization by commensals.