Radiobiologically equivalent deformable dose mapping for re-irradiation Planning: Implementation, Robustness, and dosimetric benefits.

Background: Re-irradiation in radiotherapy presents complexities that require dedicated tools to generate optimal re-treatment plans. This study presents a robust workflow that considers fractionation size, anatomical variations between treatments, and cumulative bias doses to improve the re-irradiation planning process.

Methods: The workflow was automated in MIM® Software and the Elekta© Monaco® treatment planning system.

Public attitudes on performance for algorithmic and human decision-makers.

This study explores public preferences for algorithmic and human decision-makers (DMs) in high-stakes contexts, how these preferences are shaped by performance metrics, and whether public evaluations of performance differ depending on the type of DM. Leveraging a conjoint experimental design, approximately respondents chose between pairs of DM profiles in two high-stakes scenarios: pretrial release decisions and bank loan approvals. The profiles varied by type (human vs.

Vibrio cholerae RbmB is an α-1,4-polysaccharide lyase with biofilm-disrupting activity against Vibrio polysaccharide (VPS).

Many pathogenic bacteria form biofilms as a protective measure against environmental and host hazards. The underlying structure of the biofilm matrix consists of secreted macromolecules, often including exopolysaccharides. To escape the biofilm, bacteria may produce a number of matrix-degrading enzymes, including glycosidic enzymes that digest exopolysaccharide scaffolds.

NRG Oncology International Consensus Contouring Atlas on Target Volumes and Dosing Strategies for Dose-Escalated Pancreatic Cancer Radiation Therapy.

Purpose: Dose-escalated radiation therapy is increasingly used in the treatment of pancreatic cancer; however, approaches to target delineation vary widely. We present the first North American cooperative group consensus contouring atlas for dose-escalated pancreatic cancer radiation therapy.

Methods And Materials: An expert international panel comprising 15 radiation oncologists, 2 surgeons, and 1 radiologist was recruited.

Deep learning-based automatic contour quality assurance for auto-segmented abdominal MR-Linac contours.

Deep-learning auto-segmentation (DLAS) aims to streamline contouring in clinical settings. Nevertheless, achieving clinical acceptance of DLAS remains a hurdle in abdominal MRI, hindering the implementation of efficient clinical workflows for MR-guided online adaptive radiotherapy (MRgOART). Integrating automated contour quality assurance (ACQA) with automatic contour correction (ACC) techniques could optimize the performance of ACC by concentrating on inaccurate contours.