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Most of the time, the deep analysis of a biological sample requires the acquisition of images at different time points, using different modalities and/or different stainings. This information gives morphological, functional, and physiological insights, but the acquired images must be aligned to be able to proceed with the co-localisation analysis. Practically speaking, according to Aristotle's principle, "The whole is greater than the sum of its parts", multi-modal image registration is a challenging task that involves fusing complementary signals. In the past few years, several methods for image registration have been described in the literature, but unfortunately, there is not one method that works for all applications. In addition, there is currently no user-friendly solution for aligning images that does not require any computer skills. In this work, DS4H Image Alignment (DS4H-IA), an open-source ImageJ/Fiji plugin for aligning multimodality, immunohistochemistry (IHC), and/or immunofluorescence (IF) 2D microscopy images, designed with the goal of being extremely easy to use, is described. All of the available solutions for aligning 2D microscopy images have also been revised. The DS4H-IA source code; standalone applications for MAC, Linux, and Windows; video tutorials; manual documentation; and sample datasets are publicly available. [ABSTRACT FROM AUTHOR]

Gastro-entero-pancreatic neuroendocrine tumors (GEP-NETs) are rare diseases encompassing pancreatic (PanNETs) and ileal NETs (SINETs), characterized by heterogeneous somatostatin receptors (SSTRs) expression. Treatments for inoperable GEP-NETs are limited, and SSTR-targeted Peptide Receptor Radionuclide Therapy (PRRT) achieves variable responses. Prognostic biomarkers for the management of GEP-NET patients are required. 18F-FDG uptake is a prognostic indicator of aggressiveness in GEP-NETs. This study aims to identify circulating and measurable prognostic miRNAs associated with 18FFDG-PET/CT status, higher risk and lower response to PRRT. Methods: Whole miRNOme NGS profiling was conducted on plasma samples obtained from well-differentiated advanced, metastatic, inoperable G1, G2 and G3 GEP-NET patients enrolled in the non-randomized LUX (NCT02736500) and LUNET (NCT02489604) clinical trials prior to PRRT (screening set, n= 24). Differential expression analysis was performed between 18F-FDG positive (n=12) and negative (n=12) patients. Validation was conducted by Real Time quantitative PCR in two distinct well-differentiated GEP-NET validation cohorts, considering the primary site of origin (PanNETs n=38 and SINETs n=30). The Cox regression was applied to assess independent clinical parameters and imaging for progression-free survival (PFS) in PanNETs. In situ RNA hybridization combined with immunohistochemistry was performed to simultaneously detect miR and protein expression in the same tissue specimens. This novel semiautomated miR-protein protocol was applied in PanNET FFPE specimens (n=9). In vitro functional experiments were performed in PanNET models. Results: While no miRNAs emerged to be deregulated in SINETs, hsa-miR-5096, hsa-let-7i-3p and hsa-miR-4311 were found to correlate with 18F-FDG-PET/CT in PanNETs (p-value:<0.005). Statistical analysis has shown that, hsa-miR-5096 can predict 6-month PFS (p-value:<0.001) and 12-month Overall Survival upon PRRT treatment (p-value:<0.05), as well as identify 18F-FDG-PET/CT positive PanNETs with worse prognosis after PRRT (p-value:<0.005). In addition, hsamiR-5096 inversely correlated with both SSTR2 expression in PanNET tissue and with the 68Gallium-DOTATOC captation values (p-value:<0.05), and accordingly it was able to decrease SSTR2 when ectopically expressed in PanNET cells (pvalue:< 0.01). Conclusions: hsa-miR-5096 well performs as a biomarker for 18F-FDG-PET/CT and as independent predictor of PFS. Moreover, exosome-mediated delivery of hsa-miR-5096 may promote SSTR2 heterogeneity and thus resistance to PRRT. [ABSTRACT FROM AUTHOR]

The knowledge, skills, and abilities that human capital offers create tangible and intangible assets that equip organizations to thrive. In particular, in today’s Industry 4.0 environment, training, recruiting, and retaining highly qualified ICT-ready professionals remains a problem for many organizations including educational, governmental, healthcare, and business organizations. The COVID-19 pandemic revealed the importance of digital assets to our economies, and it is also demonstrating that there is potentially a new digital divide with even worse implications for companies, economies, and society, which is threatening the resilience of business, governance, and society. In this paper, we respond to the question “how can we develop ICT human capital in our global economy in an equitable, inclusive, and purposeful manner such that not organizations thrive, but also to promote social justice and equity in our global economy?” [ABSTRACT FROM AUTHOR]

In a clinical contest, it is common to use dedicated phantoms to perform quality assurance test to check the performance of a SPECT system. Some of these phantoms are also used to calibrate the system for dosimetric evaluation of patients undergoing radiometabolic cancer therapy. In this work, a 3D-OSEM reconstructed 177Lu SPECT dataset of a homogeneous cylindrical phantom is described. This dataset was acquired to investigate the variation of the SPECT calibration factor, counts convergence, noise and uniformity by varying the number of subsets and iterations. In particular, the dataset is composed of images reconstructed using five different numbers of subsets and sixteen different numbers of iterations, for a total of 80 different configurations. The dataset is suitable for comparison with other reconstruction algorithms (e.g. FBP, MLEM, etc.) and radionuclides (e.g. technetium, yttrium). In regards to the uniformity issue, the same dataset allows the user to perform radiomic investigations on the influence of the border effect on the reconstructed images. Measurement(s) Calibration Factor • coefficient of variation • Texture Descriptor Technology Type(s) Single Photon Emission Computed Tomography • Image Feature [ABSTRACT FROM AUTHOR]