The uptake of [F]-fluorodeoxyglucose by the renal allograft correlates with the acute Banff scores of cortex inflammation but not with the 1-year graft outcomes.

Introduction: [F]FDG PET/CT noninvasively disproves acute kidney allograft rejection (AR) in kidney transplant recipients (KTRs) with suspected AR. However, the correlation of biopsy-based Banff vs. PET/CT-based scores of acute inflammation remains unknown, as does the prognostic performance of [F]FDG PET/CT at one year suspected AR.

Successful multimodal management of a large hepatocellular carcinoma in a non-cirrhotic liver: a case report.

Background: Hepatocellular carcinoma (HCC) found in a non cirrhotic liver represents a minority of HCC cases and remains poorly studied. Due to its specific characteristics and evolution, this tumour requires a different management compared to HCC in a cirrhotic liver.

Case Report: The authors describe the case of a 68-year-old man diagnosed with a large giant and only mildly symptomatic HCC in a non-cirrhotic liver.

Natural variation of nutrient homeostasis among laboratory and field strains of Chlamydomonas reinhardtii.

Natural variation among individuals and populations exists in all species, playing key roles in response to environmental stress and adaptation. Micro- and macronutrients have a wide range of functions in photosynthetic organisms, and mineral nutrition thus plays a sizable role in biomass production. To maintain nutrient concentrations inside the cell within physiological limits and prevent the detrimental effects of deficiency or excess, complex homeostatic networks have evolved in photosynthetic cells.

Deep learning based identification of bone scintigraphies containing metastatic bone disease foci.

Purpose: Metastatic bone disease (MBD) is the most common form of metastases, most frequently deriving from prostate cancer. MBD is screened with bone scintigraphy (BS), which have high sensitivity but low specificity for the diagnosis of MBD, often requiring further investigations. Deep learning (DL) - a machine learning technique designed to mimic human neuronal interactions- has shown promise in the field of medical imaging analysis for different purposes, including segmentation and classification of lesions.