University of Rijeka

About: University of Rijeka is a education organization based out in Rijeka, Croatia. It is known for research contribution in the topics: Population & Tourism. The organization has 3471 authors who have published 7993 publications receiving 110386 citations. The organization is also known as: Rijeka University & Sveučilište u Rijeci.

Mammalian plasma membrane proteins as potential biomarkers and drug targets

Abstract: Defining the plasma membrane proteome is crucial to understand the role of plasma membrane in fundamental biological processes. Change in membrane proteins is one of the first events that take place under pathological conditions, making plasma membrane proteins a likely source of potential disease biomarkers with prognostic or diagnostic potential. Membrane proteins are also potential targets for monoclonal antibodies and other drugs that block receptors or inhibit enzymes essential to the disease progress. Despite several advanced methods recently developed for the analysis of hydrophobic proteins and proteins with posttranslational modifications, integral membrane proteins are still under-represented in plasma membrane proteome. Recent advances in proteomic investigation of plasma membrane proteins, defining their roles as diagnostic and prognostic disease biomarkers and as target molecules in disease treatment, are presented.

Identification of rare genetic variation of NLRP1 gene in familial multiple sclerosis

Abstract: The genetic etiology and the contribution of rare genetic variation in multiple sclerosis (MS) has not yet been elucidated. Although familial forms of MS have been described, no convincing rare and penetrant variants have been reported to date. We aimed to characterize the contribution of rare genetic variation in familial and sporadic MS and have identified a family with two sibs affected by concomitant MS and malignant melanoma (MM). We performed whole exome sequencing in this primary family and 38 multiplex MS families and 44 sporadic MS cases and performed transcriptional and immunologic assessment of the identified variants. We identified a potentially causative homozygous missense variant in NLRP1 gene (Gly587Ser) in the primary family. Further possibly pathogenic NLRP1 variants were identified in the expanded cohort of patients. Stimulation of peripheral blood mononuclear cells from MS patients with putatively pathogenic NLRP1 variants showed an increase in IL-1B gene expression and active cytokine IL-1β production, as well as global activation of NLRP1-driven immunologic pathways. We report a novel familial association of MS and MM, and propose a possible underlying genetic basis in NLRP1 gene. Furthermore, we provide initial evidence of the broader implications of NLRP1-related pathway dysfunction in MS.

Rapid prediction of earthquake ground shaking intensity using raw waveform data and a convolutional neural network

Abstract: This study describes a deep convolutional neural network (CNN) based technique for the prediction of intensity measurements (IMs) of ground shaking. The input data to the CNN model consists of multistation 3C broadband and accelerometric waveforms recorded during the 2016 Central Italy earthquake sequence for M $\ge$ 3.0. We find that the CNN is capable of predicting accurately the IMs at stations far from the epicenter and that have not yet recorded the maximum ground shaking when using a 10 s window starting at the earthquake origin time. The CNN IM predictions do not require previous knowledge of the earthquake source (location and magnitude). Comparison between the CNN model predictions and the predictions obtained with Bindi et al. (2011) GMPE (which require location and magnitude) has shown that the CNN model features similar error variance but smaller bias. Although the technique is not strictly designed for earthquake early warning, we found that it can provide useful estimates of ground motions within 15-20 sec after earthquake origin time depending on various setup elements (e.g., times for data transmission, computation, latencies). The technique has been tested on raw data without any initial data pre-selection in order to closely replicate real-time data streaming. When noise examples were included with the earthquake data, the CNN was found to be stable predicting accurately the ground shaking intensity corresponding to the noise amplitude.

Cell death and cell proliferation in mouse submandibular gland during early post-irradiation phase.

Abstract: The effects of irradiation on different cell compartments in the submandibular gland were analyzed in adult C57BL/6 mice exposed to X-ray irradiation and followed up for 10 days. Apoptosis was quantified using the terminal deoxynucleotidyl transferase (TdT)-mediated dUTP-digoxigenin nick end labeling method (TUNEL). Cell proliferation was detected using immunohistochemistry for proliferating cell nuclear antigen (PCNA). Radiation-induced apoptosis occurred rapidly, reaching a maximum 3 days post-irradiation. The percentage of apoptotic cells increased with the irradiation dose. At day 1 post-irradiation, cell proliferation was significantly reduced in comparison to sham-irradiated controls. After post-irradiation arrest of the cell cycle, proliferation increased in all gland compartments, reaching a maximum at day 6 post-irradiation. The proliferation response corresponded to the dose of irradiation. We suggest that the reason for gland dysfunction could be the coexistence of high apoptotic and proliferative activity in the irradiated gland.