Relationship between Gd-IgA1 and TNFR1 in IgA nephropathy and IgA vasculitis nephritis in children - multicenter study.

AIM OF THE STUDY: To evaluate the relationship between serum Gd-IgA1 (sGd-IgA1) and serum and urine TNFR1 (sTNFR1, uTNFR1) levels as possible prognostic factors in IgA nephropathy (IgAN) and IgA vasculitis nephritis (IgAVN). MATERIAL AND METHODS: From 299 patients from the Polish Registry of Pediatric IgAN and IgAVN, 60 children (24 IgAN and 36 IgAVN) were included in the study. The control group consisted of 20 healthy children. Proteinuria, haematuria, serum creatinine as well as IgA and C3 levels were measured and glomerular filtration rate (GFR) was calculated at onset and at the end of the follow-up. Kidney biopsy findings were evaluated using the Oxford classification. Serum Gd-IgA1 and serum and urine TNFR1 levels were measured at the end of follow-up. RESULTS: Serum Gd-IgA1 level was significantly higher in IgAN and IgAVN patients in comparison to the control group. Urine TNFR1 was significantly higher in IgAN than in IgAVN and the control group. We did not observe any differences in sTNFR1 level between IgAN, IgAVN and control groups. We found a positive correlation between Gd-IgA1 and creatinine (r = 0.34), and negative between Gd-IgA1 and GFR (r = -0.35) at the end of follow-up. We observed a negative correlation between uTNFR1/creatinine log and albumin level and protein/creatinine ratio. We did not find any correlations between Gd-IgA1 and TNFR1. CONCLUSIONS: The prognostic value of sGd-IgA1 in children with IgAN and IgAVN has been confirmed. TNFR1 is not associated with Gd-IgA1 and is not a useful prognostic marker in children with IgAN/IgAVN and normal kidney function.

Serum GDIgA1 levels in children with IgA nephropathy and Henoch-Schönlein nephritis.

INTRODUCTION: GDIgA1 (galactose deficient IgA1) plays a significant role in the pathogenesis of IgA nephropathy (IgAN) and Henoch-Schönlein nephritis (HSN). AIM OF THE STUDY: The aim of this study was to assess the relevance of serum GDIgA1 level as a prognostic marker in children with IgAN and HSN. MATERIAL AND METHODS: 41 children were included to the study group (15 IgAN, 26 HSN) and 22 to the control group. The following parameters were evaluated at baseline and endpoint: proteinuria, erythrocyturia, serum creatinine, serum IgA, GFR. A kidney biopsy was performed in all patients and evaluated according to the Oxford Classification (1 - present, 0 - absent: M - mesangial hypercellularity; E- endocapillary hypercellularity; S - segmental sclerosis/adhesion; T - tubular atrophy/interstitial fibrosis), and was calculated as the total score (sum of M, E, S, T). At the end of follow-up, the serum GDIgA1 concentration was measured. RESULTS: The serum GDIgA1 concentration in patients with IgAN and HSN was significantly higher than in the control group. No significant differences in mean proteinuria, erythrocyturia, GFR, MEST score, or GDIgA1 in serum, as well as the duration of follow-up between IgAN and HSN were observed. Baseline serum IgA concentration and time to kidney biopsy were significantly higher in children with IgAN than in children with HSN. We observed a positive correlation between GDIgA1 and IgA levels (r = 0.53), and GDIgA1 and serum creatinine levels (r = 0.5), as well as negative correlation between GDIgA1 and GFR (r = -0.37). CONCLUSIONS: Serum GDIgA1 level may have a prognostic value in children with IgAN and HSN; however, to fully elucidate its clinical potential further studies performed in larger patient cohorts are required.

High resolution optical and acoustic remote sensing datasets of the Puck Lagoon.

The very shallow marine basin of Puck Lagoon in the southern Baltic Sea, on the Northern coast of Poland, hosts valuable benthic habitats and cultural heritage sites. These include, among others, protected Zostera marina meadows, one of the Baltic's major medieval harbours, a ship graveyard, and likely other submerged features that are yet to be discovered. Prior to this project, no comprehensive high-resolution remote sensing data were available for this area. This article describes the first Digital Elevation Models (DEMs) derived from a combination of airborne bathymetric LiDAR, multibeam echosounder, airborne photogrammetry and satellite imagery. These datasets also include multibeam echosounder backscatter and LiDAR intensity, allowing determination of the character and properties of the seafloor. Combined, these datasets are a vital resource for assessing and understanding seafloor morphology, benthic habitats, cultural heritage, and submerged landscapes. Given the significance of Puck Lagoon's hydrographical, ecological, geological, and archaeological environs, the high-resolution bathymetry, acquired by our project, can provide the foundation for sustainable management and informed decision-making for this area of interest.

Transitions between polarization and radicalization in a temporal bilayer echo-chamber model.

Echo chambers and polarization dynamics are, as of late, a very prominent topic in scientific communities around the world. As these phenomena directly affect our lives, seemingly more and more as our societies and communication channels evolve, it becomes ever so important for us to understand the intricacies of opinion dynamics in the modern era. Here we extend an existing echo-chamber model with activity-driven agents to a bilayer topology and study the dynamics of the polarized state as a function of interlayer couplings. Different cases of such couplings are presented: unidirectional coupling that can be reduced to a monolayer facing an external bias and symmetric and nonsymmetric couplings. We have assumed that initial conditions impose system polarization and agent opinions are different for both layers. Such a preconditioned polarized state can persist without explicit homophilic interactions provided the coupling strength between agents belonging to different layers is weak enough. For a strong unidirectional or attractive coupling between two layers a discontinuous transition to a radicalized state takes place when mean opinions in both layers are the same. When coupling constants between the layers are of different signs, the system exhibits sustained or decaying oscillations. Transitions between these states are analyzed using a mean field approximation and classified in the framework of bifurcation theory.

Comparison of observer based methods for source localisation in complex networks.

In recent years, research on methods for locating a source of spreading phenomena in complex networks has seen numerous advances. Such methods can be applied not only to searching for the "patient zero" in epidemics, but also finding the true sources of false or malicious messages circulating in the online social networks. Many methods for solving this problem have been established and tested in various circumstances. Yet, we still lack reviews that would include a direct comparison of efficiency of these methods. In this paper, we provide a thorough comparison of several observer-based methods for source localisation on complex networks. All methods use information about the exact time of spread arrival at a pre-selected group of vertices called observers. We investigate how the precision of the studied methods depends on the network topology, density of observers, infection rate, and observers' placement strategy. The direct comparison between methods allows for an informed choice of the methods for applications or further research. We find that the Pearson correlation based method and the method based on the analysis of multiple paths are the most effective in networks with synthetic or real topologies. The former method dominates when the infection rate is low; otherwise, the latter method takes over.

Detecting hidden layers from spreading dynamics on complex networks.

When dealing with spreading processes on networks it can be of the utmost importance to test the reliability of data and identify potential unobserved spreading paths. In this paper we address these problems and propose methods for hidden layer identification and reconstruction. We also explore the interplay between difficulty of the task and the structure of the multilayer network describing the whole system where the spreading process occurs. Our methods stem from an exact expression for the likelihood of a cascade in the susceptible-infected model on an arbitrary graph. We then show that by imploring statistical properties of unimodal distributions and simple heuristics describing joint likelihood of a series of cascades one can obtain an estimate of both existence of a hidden layer and its content with success rates far exceeding those of a null model. We conduct our analyses on both synthetic and real-world networks providing evidence for the viability of the approach presented.

Discovering hidden layers in quantum graphs.

Finding hidden layers in complex networks is an important and a nontrivial problem in modern science. We explore the framework of quantum graphs to determine whether concealed parts of a multilayer system exist and if so then what is their extent, i.e., how many unknown layers are there. Assuming that the only information available is the time evolution of a wave propagation on a single layer of a network it is indeed possible to uncover that which is hidden by merely observing the dynamics. We present evidence on both synthetic and real-world networks that the frequency spectrum of the wave dynamics can express distinct features in the form of additional frequency peaks. These peaks exhibit dependence on the number of layers taking part in the propagation and thus allowing for the extraction of said number. We show that, in fact, with sufficient observation time, one can fully reconstruct the row-normalized adjacency matrix spectrum. We compare our propositions to a machine learning approach using a wave packet signature method modified for the purposes of multilayer systems.