Dialogical and Relational Taster Training (DARTT): Service User, Carer & Public Perception.

In response to the paucity of training in therapeutic relationships and relational care for mental health clinicians, a new three-day training programme has been developed called Compassionate and Relational Enquiry (CARE). A condensed version of this training was recently delivered to mental health service users, carers and the public during a one-day online taster session- named Dialogical And Relational Taster Training (DARTT) - to understand their perception of this training programme. 21 people took part in this taster training day and subsequently completed a survey about their impression of different elements of the training.

Diversity of Pleosporalean Fungi Isolated from Rice ( L.) in Northern Thailand and Descriptions of Five New Species.

Pleosporales represents the largest order within the class Dothideomycetes (Fungi), comprising phytopathogenic, saprobic, and endophytic taxa with a widespread presence in terrestrial and aquatic environments. Rice () is a primary economic crop in numerous tropical countries, particularly in Thailand. Studying fungal species associated with rice holds the potential to enhance our understanding of fungal diversity, lifestyles, and biology of rice, offering valuable insights for future research aimed at disease management and yield improvement.

Application of CT-based foundational artificial intelligence and radiomics models for prediction of survival for lung cancer patients treated on the NRG/RTOG 0617 clinical trial.

Objectives: To apply CT-based foundational artificial intelligence (AI) and radiomics models for predicting overall survival (OS) for patients with locally advanced non-small cell lung cancer (NSCLC).

Methods: Data for 449 patients retrospectively treated on the NRG Oncology/Radiation Therapy Oncology Group (RTOG) 0617 clinical trial were analyzed. Foundational AI, radiomics, and clinical features were evaluated using univariate cox regression and correlational analyses to determine independent predictors of survival.

MUsculo-Skeleton-Aware (MUSA) deep learning for anatomically guided head-and-neck CT deformable registration.

Deep-learning-based deformable image registration (DL-DIR) has demonstrated improved accuracy compared to time-consuming non-DL methods across various anatomical sites. However, DL-DIR is still challenging in heterogeneous tissue regions with large deformation. In fact, several state-of-the-art DL-DIR methods fail to capture the large, anatomically plausible deformation when tested on head-and-neck computed tomography (CT) images.