Iron-induced Complement Dysregulation in the Retinal Pigment Epithelium: Implications for Age-Related Macular Degeneration

Age-related macular degeneration (AMD), typically manifesting as a loss of central vision in elderly persons, is a leading cause of blindness in highly developed nations. AMD is a multifactorial disease associated with aging, oxidative stress, complement dysregulation, dsRNA toxicity, among many other possible factors. The formation of extracellular deposits, termed drusen, below the retinal pigment epithelial (RPE) cell layer in the outer retina is a pathognomonic hallmark of AMD. The composition of drusen is complex, but identified elements include iron, complement components, and amyloid protein derivatives. This suggests that iron may be involved in the pathophysiology of AMD. Further support for this hypothesis comes from mice lacking ferroxidases Ceruloplasmin (Cp) and Hephaestin (Heph), which have a primary genetic defect in iron homeostasis. These mice develop some AMD-like morphological features and a telling molecular feature: activated complement component 3 (C3) fragment deposition at the basolateral aspect of the RPE (the location of drusen in AMD). In our studies, we investigated the molecular mechanisms by which C3 is up-regulated by iron in RPE cells. ERK1/2, SMAD3, and CCAAT/enhancer-binding protein-δ (C/EBP-δ) are part of a non-canonical TGF-β signaling pathway that is responsible for iron-induced C3 expression. Pharmacologic inhibition of either ERK1/2 or SMAD3 phosphorylation decreased iron-induced C3 expression levels. Knockdown of SMAD3 blocked the iron-induced up-regulation and nuclear accumulation of C/EBP-δ, a transcription factor known to promote C3 expression by binding to the basic leucine zipper (bZIP1) domain of the gene promoter. We show herein that mutation of this domain reduced iron-induced C3 promoter activity. The molecular events in the iron-C3 pathway represent therapeutic targets for AMD. To better understand the relative contribution of systemic iron and local dysregulation of iron homeostasis to RPE iron accumulation, we used Bmp6 KO mice and WT mice and found that retinal hepcidin levels are not changed, but in fact may be slightly greater in KO compared to WT mice. As such, systemic iron overload by genetic KO or intravenous supplementation in WT mice resulted in increased RPE labile iron and oxidative stress, suggesting that systemic iron overload may lead to retinal iron overload despite the presence of an intact blood retinal barrier. Systemic iron status appears to be a leading determinant of retinal iron status

Preparation and Photocatalytic Activity of Fe3+ - doped TiO2 Modified

Photocatalytic materials of TiO2 / coarse silica gel microspheres were prepared by sol-gel impregnation method and doped with transition metal ions Fe3+ to form Fe3+ modified TiO2 / coarse silica gel microspheres. The surface of the Fe modified photocatalytic materials were analyzed by XRD and surface scanning. Degradation of reactive dyes using X-3B dye wastewater water samples for validation tests. The results show that the photocatalytic activity of the photocatalytic material reaches the best when the Fe doping amount is 0.5%, and the most photocatalyst amount is 10g / L. The degradation rate of COD under this dosage was 72.32%. At the same time, according to the characterization analysis, it was found that Fe did not react with the photocatalytic material of TiO2 / coarse silica microspheres in the reaction, Fe increased the defect structure of TiO2 surface and promoted the formation of anatase, A red shift occurred and the corresponding range of the spectrum expanded toward the visible region. The results show that the doping of Fe affects the formation of TiO2 crystals, but increases the photocatalytic efficiency of TiO2

Landscape of intestinal microbiota in patients with IgA nephropathy, IgA vasculitis and Kawasaki disease

ObjectiveTo explore the common differential flora of IgAN, Kawasaki disease and IgA vasculitis by screening and analyzing the differential intestinal flora between the three disease groups of IgAN, Kawasaki disease and IgA vasculitis and their healthy controls.MethodsPapers on 16srRNA sequencing-related intestinal flora of IgAN, Kawasaki disease and IgA vasculitis were searched in databases, the literature was systematically collated and analysed, the original data was download from the relevant databases, and then the operational taxonomic unit and species classification analysis were performed. Besides, Alpha diversity analysis and Beta diversity analysis were performed to screen for IgAN, Kawasaki disease and I1gA vasculitis groups and finally compare the common intestinal differential flora among the three groups.ResultsAmong the common differential flora screened, Lachnospiracea_incertae_sedis was lower in both the IgAN and Kawasaki disease groups than in the respective healthy controls; Coprococcus was low in the IgAN group but high in the IgA vasculitis group. Fusicatenibacter was lower in both the Kawasaki disease and IgA vasculitis groups than in their respective healthy controls, and Intestinibacter was low in the Kawasaki disease group, but its expression was high in the IgA vasculitis group.ConclusionThe dysbiosis of the intestinal flora in the three groups of patients with IgAN, Kawasaki disease and IgA vasculitis, its effect on the immunity of the organism and its role in the development of each disease group remain unclear, and the presence of their common differential flora may further provide new ideas for the association of the pathogenesis of the three diseases

Using random forest algorithm for glomerular and tubular injury diagnosis

ObjectivesChronic kidney disease (CKD) is a common chronic condition with high incidence and insidious onset. Glomerular injury (GI) and tubular injury (TI) represent early manifestations of CKD and could indicate the risk of its development. In this study, we aimed to classify GI and TI using three machine learning algorithms to promote their early diagnosis and slow the progression of CKD.MethodsDemographic information, physical examination, blood, and morning urine samples were first collected from 13,550 subjects in 10 counties in Shanxi province for classification of GI and TI. Besides, LASSO regression was employed for feature selection of explanatory variables, and the SMOTE (synthetic minority over-sampling technique) algorithm was used to balance target datasets, i.e., GI and TI. Afterward, Random Forest (RF), Naive Bayes (NB), and logistic regression (LR) were constructed to achieve classification of GI and TI, respectively.ResultsA total of 12,330 participants enrolled in this study, with 20 explanatory variables. The number of patients with GI, and TI were 1,587 (12.8%) and 1,456 (11.8%), respectively. After feature selection by LASSO, 14 and 15 explanatory variables remained in these two datasets. Besides, after SMOTE, the number of patients and normal ones were 6,165, 6,165 for GI, and 6,165, 6,164 for TI, respectively. RF outperformed NB and LR in terms of accuracy (78.14, 80.49%), sensitivity (82.00, 84.60%), specificity (74.29, 76.09%), and AUC (0.868, 0.885) for both GI and TI; the four variables contributing most to the classification of GI and TI represented SBP, DBP, sex, age and age, SBP, FPG, and GHb, respectively.ConclusionRF boasts good performance in classifying GI and TI, which allows for early auxiliary diagnosis of GI and TI, thus facilitating to help alleviate the progression of CKD, and enjoying great prospects in clinical practice

Iron Metabolism and Brain Development in Premature Infants

Iron is important for a remarkable array of essential functions during brain development, and it needs to be provided in adequate amounts, especially to preterm infants. In this review article, we provide an overview of iron metabolism and homeostasis at the cellular level, as well as its regulation at the mRNA translation level, and we emphasize the importance of iron for brain development in fetal and early life in preterm infants. We also review the risk factors for disrupted iron metabolism that lead to high risk of developing iron deficiency and subsequent adverse effects on neurodevelopment in preterm infants. At the other extreme, iron overload, which is usually caused by excess iron supplementation in iron-replete preterm infants, might negatively impact brain development or even induce brain injury. Maintaining the balance of iron during the fetal and neonatal periods is important, and thus iron status should be monitored routinely and evaluated thoroughly during the neonatal period or before discharge of preterm infants so that iron supplementation can be individualized

Multidimensional sound propagation in 3D high-order topological sonic insulator

High-order topological insulators (TIs) develop the conventional bulk-boundary correspondence theory and rise the interest in searching innovative topological materials. To realize a high-order TI with a wide passband of 1D and 2D transportation modes, we design non-trivial and trivial 3D sonic crystals whose combination mimics the Su-Schrieffer-Heeger model. The high-order topological boundary states can be found at the interfaces, including 0D corner state, 1D hinge state, and 2D surface state. The fabricated sample with the bent two-dimensional and one-dimensional acoustic channels exhibits the multidimensional sound propagation in space, and also verifies the transition between the complete band gap, hinge states, and surface states within the bulk band gap. Among them, the bandwidth of the single-mode hinge state achieves a large relative bandwidth 9.1%, in which sound transports one-dimensionally without significant leak into the surfaces or the bulk. The high-order topological states in the study pave the way for multidimensional sound manipulation in space.Comment: 21 pages, 7 figure

MBrain: A Multi-channel Self-Supervised Learning Framework for Brain Signals

Brain signals are important quantitative data for understanding physiological activities and diseases of human brain. Most existing studies pay attention to supervised learning methods, which, however, require high-cost clinical labels. In addition, the huge difference in the clinical patterns of brain signals measured by invasive (e.g., SEEG) and non-invasive (e.g., EEG) methods leads to the lack of a unified method. To handle the above issues, we propose to study the self-supervised learning (SSL) framework for brain signals that can be applied to pre-train either SEEG or EEG data. Intuitively, brain signals, generated by the firing of neurons, are transmitted among different connecting structures in human brain. Inspired by this, we propose MBrain to learn implicit spatial and temporal correlations between different channels (i.e., contacts of the electrode, corresponding to different brain areas) as the cornerstone for uniformly modeling different types of brain signals. Specifically, we represent the spatial correlation by a graph structure, which is built with proposed multi-channel CPC. We theoretically prove that optimizing the goal of multi-channel CPC can lead to a better predictive representation and apply the instantaneou-time-shift prediction task based on it. Then we capture the temporal correlation by designing the delayed-time-shift prediction task. Finally, replace-discriminative-learning task is proposed to preserve the characteristics of each channel. Extensive experiments of seizure detection on both EEG and SEEG large-scale real-world datasets demonstrate that our model outperforms several state-of-the-art time series SSL and unsupervised models, and has the ability to be deployed to clinical practice