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This study employs computational algorithms to automatically identify and classify features in X‐Ray fluorescence (XRF) microscopy images. Principal component analysis (PCA) and unsupervised machine learning algorithms, such as Gaussian mixture (GM) clustering, are implemented to label features on a collection of XRF maps of human atherosclerotic plaque samples. The investigation involves the hard X‐Ray nanoprobe (NanoMAX) at MAX IV synchrotron radiation facility, utilizing scanning transmission X‐Ray microscopy (STXM) and XRF techniques. The analysis covers regions of interest scanned by the beam with a step size of 200 nm, yielding XRF maps of elements like calcium, iron, and zinc. These maps reveal intricate structures unsuitable for manual labeling. However, they can be accurately classified in an automated fashion using GM. Prior to clustering, PCA is used to deal with repeated patterns and background areas. The resulting clusters are associated with different types of features, which can be identified as specific tissues confirmed by histology. Regions of high concentrations of phosphorus, sulfur, calcium, and iron are found in the samples. These regions are also observed in the STXM results as spots of low transmission that typically are associated with calcium deposits only. [ABSTRACT FROM AUTHOR]

Aims: Biliary atresia (BA) is characterized by intrahepatic inflammation and rapid progression of liver fibrosis. Galectin‐3, a beta‐galactoside binding protein, is a key regulator of inflammation and fibrosis. The aim of this study was to characterize circulating and hepatic Galectin‐3 levels in children with BA. Methods: Plasma and liver samples were obtained from children with early BA at time of Kasai hepatoportoenterostomy, late BA at time of transplant, early and late other cholestatic liver diseases (CLD), and controls. Plasma Galectin‐3 was measured using standard enzyme‐linked immunoassay. Liver tissue was analyzed with multiplex immunohistochemistry and quantified using whole slide analysis. Statistical comparisons were made using nonparametric testing. Results: Plasma Galectin‐3 in late BA was significantly higher than in early BA (20.82 [12.45–30.46] vs. 11.30 [8.74–16.83] ng/mL, p = 0.0096). Galectin‐3 levels correlated with markers of disease severity and interleukin‐6. There were significantly more Galectin‐3+ M2 macrophages in late BA in comparison to late other CLD (162 [157–233] vs. 49 [33–59] cells/mm2, p = 0.03). The number of Galectin‐3+ M2 macrophages correlated with the number of activated hepatic stellate cells and bile duct proliferation. Conclusions: Plasma Galectin‐3 is higher in late BA at time of transplant in comparison to early BA at time of Kasai. The number of Galectin‐3 expressing M2 macrophages in late BA is elevated relative to late other CLD and was associated with other prognostic histological findings. Galectin‐3 targeted therapy may be beneficial in slowing disease progression to cirrhosis in children with BA. [ABSTRACT FROM AUTHOR]

The Lund Broad-band Neutron Facility provides access to a variety of neutron sources together with a well-established user infrastructure. Neutrons from radioactive sources have been successfully employed for the research and development of both detectors and materials for the European Spallation Source. A recently procured d-t neutron generator delivers higher neutron fluxes than those provided by the radioactive sources, and further allows for pulsed operation. With the currently on-going construction of a dedicated neutron beam-line at our 3 MeV Pelletron accelerator, the facility is anticipated to produce approximately 1010 n/s. Cost-effective access to neutrons as well as a platform for educational purposes are the ultimate goals of the project.