The neuroendocrine transition in prostate cancer is dynamic and dependent on ASCL1.

Lineage plasticity is a recognized hallmark of cancer progression that can shape therapy outcomes. The underlying cellular and molecular mechanisms mediating lineage plasticity remain poorly understood. Here, we describe a versatile in vivo platform to identify and interrogate the molecular determinants of neuroendocrine lineage transformation at different stages of prostate cancer progression. Adenocarcinomas reliably develop following orthotopic transplantation of primary mouse prostate organoids acutely engineered with human-relevant driver alterations (e.g., Rb1-/-; Trp53-/-; cMyc+ or Pten-/-; Trp53-/-; cMyc+), but only those with Rb1 deletion progress to ASCL1+ neuroendocrine prostate cancer (NEPC), a highly aggressive, androgen receptor signaling inhibitor (ARSI)-resistant tumor. Importantly, we show this lineage transition requires a native in vivo microenvironment not replicated by conventional organoid culture. By integrating multiplexed immunofluorescence, spatial transcriptomics and PrismSpot to identify cell type-specific spatial gene modules, we reveal that ASCL1+ cells arise from KRT8+ luminal epithelial cells that progressively acquire transcriptional heterogeneity, producing large ASCL1+;KRT8- NEPC clusters. Ascl1 loss in established NEPC results in transient tumor regression followed by recurrence; however, Ascl1 deletion prior to transplantation completely abrogates lineage plasticity, yielding adenocarcinomas with elevated AR expression and marked sensitivity to castration. The dynamic feature of this model reveals the importance of timing of therapies focused on lineage plasticity and offers a platform for identification of additional lineage plasticity drivers.

Multimodal Medical Image Fusion Utilizing Two-scale Image Decomposition via Saliency Detection.

BACKGROUND: Modern medical imaging modalities used by clinicians have many applications in the diagnosis of complicated diseases. These imaging technologies reveal the internal anatomy and physiology of the body. The fundamental idea behind medical image fusion is to increase the image's global and local contrast, enhance the visual impact, and change its format so that it is better suited for computer processing or human viewing while preventing noise magnification and accomplishing excellent real-time performance. OBJECTIVE: The top goal is to combine data from various modal images (CT/MRI and MR-T1/MR-T2) into a solitary image that, to the greatest degree possible, retains the key characteristics (prominent features) of the source images. METHODS: The clinical accuracy of medical issues is compromised because innumerable classical fusion methods struggle to conserve all the prominent features of the original images. Furthermore, complex implementation, high computation time, and more memory requirements are key problems of transform domain methods. With the purpose of solving these problems, this research suggests a fusion framework for multimodal medical images that makes use of a multi-scale edge-preserving filter and visual saliency detection. The source images are decomposed using a two-scale edge-preserving filter into base and detail layers. Base layers are combined using the addition fusion rule, while detail layers are fused using weight maps constructed using the maximum symmetric surround saliency detection algorithm. RESULTS: The resultant image constructed by the presumed method has improved objective evaluation metrics than other classical methods, as well as unhindered edge contour, more global contrast, and no ringing effect or artifacts. CONCLUSION: The methodology offers a dominant and symbiotic arsenal of clinical symptomatic, therapeutic, and biomedical research competencies that have the prospective to considerably strengthen medical practice and biological understanding.

Aligning Santal Tribe Menu Templates with EAT-Lancet Commission's Dietary Guidelines for Sustainable and Healthy Diets: A Comparative Analysis.

BACKGROUND: In the context of global shifts in food systems, this paper explores the unique dietary practices of the Santal tribe, an indigenous group in eastern India, to understand the health, nutrition, and sustainability aspects of their traditional food systems. This study evaluates the nutritional content of the Santal diet in comparison to the EAT-Lancet Commission's 2019 dietary guidelines for healthy and sustainable diets. METHODS: The University of East Anglia, in collaboration with the NNEdPro Global Institute for Food, Nutrition and Health in Cambridge, PRADAN; colleagues in India and local Santal youth, conducted nutritional analyses of traditional Santal recipes. Two menu templates, Kanhu Thali and Jhano Thali, were selected for comparative analysis based on their representation of diverse dietary practices within the Santal community. Nutritional data, including energy as well as the distribution of macronutrients and micronutrients, were compiled and compared with the EAT-Lancet guidelines. RESULTS: The Santal menu templates (nutritionally complete meals) demonstrated alignment with EAT-Lancet recommendations in aspects such as whole grains, starchy vegetables, vegetables, plant-based protein sources, unsaturated fats, and limited added sugars. However, notable deviations included the absence of animal-based protein sources and dairy. The Santal diet showed high protein intake, largely from plant-based sources, and emphasised the importance of whole grains. Seasonal variations in nutritional content were observed between the two templates. CONCLUSIONS: While the Santal diet aligns with some aspects of global dietary guidelines, there are notable deviations that underscore the complexity of aligning traditional diets with universal recommendations. The findings emphasise the need for culturally sensitive dietary recommendations that respect traditional diets while promoting sustainability. Research needs to support tailored global guidelines enshrining core principles of nutritional adequacy which are inter-culturally operable in order to accommodate cultural diversity, local practices, and seasonal variations, crucial for fostering sustainable and healthy eating habits in diverse sociodemographic contexts.

Fusion of Multimodal Medical Images Based on Fine-Grained Saliency and Anisotropic Diffusion Filter.

BACKGROUND: A clinical medical image provides vital information about a person's health and bodily condition. Typically, doctors monitor and examine several types of medical images individually to gather supplementary information for illness diagnosis and treatment. As it is arduous to analyze and diagnose from a single image, multi-modality images have been shown to enhance the precision of diagnosis and evaluation of medical conditions. OBJECTIVE: Several conventional image fusion techniques strengthen the consistency of the information by combining varied image observations; nevertheless, the drawback of these techniques in retaining all crucial elements of the original images can have a negative impact on the accuracy of clinical diagnoses. This research develops an improved image fusion technique based on fine-grained saliency and an anisotropic diffusion filter to preserve structural and detailed information of the individual image. METHOD: In contrast to prior efforts, the saliency method is not executed using a pyramidal decomposition, but rather an integral image on the original scale is used to obtain features of superior quality. Furthermore, an anisotropic diffusion filter is utilized for the decomposition of the original source images into a base layer and a detail layer. The proposed algorithm's performance is then contrasted to those of cutting-edge image fusion algorithms. RESULTS: The proposed approach cannot only cope with the fusion of medical images well, both subjectively and objectively, according to the results obtained, but also has high computational efficiency. CONCLUSION: Furthermore, it provides a roadmap for the direction of future research.