Lipodystrophy - A Rare Condition with Serious Metabolic Abnormalities

Lipodystrophy is a rare lipid storage disorder that is characterized by a loss of adipose tissue. It can be inherited due to monogenic mutation or acquired by medication and autoimmune illness. Two primary forms of inherited lipodystrophy are congenital generalized lipodystrophy manifested as a near-complete loss of fat tissue since birth and familial partial lipodystrophy with progressive, partial loss of fat tissue during late childhood and puberty. Lipodystrophy results in severe metabolic conditions, including insulin resistance, type 2 diabetes, hepatosteatosis, polycystic ovary syndrome, acanthosis nigricans, and hypertension. This chapter summarizes the symptoms, causes, and treatments of inherited and acquired lipodystrophy

Crossover between Weak Antilocalization and Weak Localization of Bulk States in Ultrathin Bi2Se3 Films

We report transport studies on the 5 nm thick Bi2Se3 topological insulator films which are grown via molecular beam epitaxy technique. The angle-resolved photoemission spectroscopy data show that the Fermi level of the system lies in the bulk conduction band above the Dirac point, suggesting important contribution of bulk states to the transport results. In particular, the crossover from weak antilocalization to weak localization in the bulk states is observed in the parallel magnetic field measurements up to 50 Tesla. The measured magneto-resistance exhibits interesting anisotropy with respect to the orientation of B// and I, signifying intrinsic spin-orbit coupling in the Bi2Se3 films. Our work directly shows the crossover of quantum interference effect in the bulk states from weak antilocalization to weak localization. It presents an important step toward a better understanding of the existing three-dimensional topological insulators and the potential applications of nano-scale topological insulator devices

The Ayurvedic Medicine Salacia oblonga Attenuates Diabetic Renal Fibrosis in Rats: Suppression of Angiotensin II/AT1 Signaling

In human diabetic nephropathy, the extent of tubulointerstitial fibrosis is the leading cause of end-stage renal disease; fibrosis is closely correlated with renal dysfunction. Although a wide array of medicinal plants play a role in the prevention and treatment of diabetes, there are few reports of the application of herbal medicines in amelioration of renal fibrosis, or the underlying mechanisms by which such benefits are mediated. The efficacy of the Ayurvedic antidiabetic medicine Salacia oblonga (SO) root on rat renal fibrosis was investigated. An aqueous extract from SO (100 mg/kg, p.o., 6 weeks) diminished renal glomerulosclerosis and interstitial fibrosis in Zucker diabetic fatty (ZDF) rats, as revealed by van Giesen-staining. SO also reduced renal salt-soluble, acid-soluble and salt-insoluble collagen contents. These changes were accompanied by normalization of hypoalbuminemia and BUN. Gene profiling revealed that the increase in transcripts encoding the glomerulosclerotic mediators collagen I, collagen IV, fibronectin, angiotensin II type 1 receptor (AT1), transforming growth factor (TGF)-β1, plasminogen activator inhibitor (PAI)-1 observed in ZDF rat kidney was suppressed by SO. In rat-derived mesangial cells, similar to the effect of the AT1 antagonist telmisartan, SO and its major component mangiferin suppressed the stimulatory effect of angiotensin II on proliferation and increased mRNA expression and/or activities of collagen I, collagen IV, fibronectin, AT1, TGF-β1 and PAI-1. Considered together the present findings demonstrate that SO attenuates diabetic renal fibrosis, at least in part by suppressing anigiotensin II/AT1 signaling. Further, it now emerges that mangiferin is an effective antifibrogenic agent

Research on the cascading mechanism of “urban built environment-air pollution-respiratory diseases”: a case of Wuhan city

BackgroundMost existing studies have only investigated the direct effects of the built environment on respiratory diseases. However, there is mounting evidence that the built environment of cities has an indirect influence on public health via influencing air pollution. Exploring the “urban built environment-air pollution-respiratory diseases” cascade mechanism is important for creating a healthy respiratory environment, which is the aim of this study.MethodsThe study gathered clinical data from 2015 to 2017 on patients with respiratory diseases from Tongji Hospital in Wuhan. Additionally, daily air pollution levels (sulfur dioxide (SO2), nitrogen dioxide (NO2), particulate matter (PM2.5, PM10), and ozone (O3)), meteorological data (average temperature and relative humidity), and data on urban built environment were gathered. We used Spearman correlation to investigate the connection between air pollution and meteorological variables; distributed lag non-linear model (DLNM) was used to investigate the short-term relationships between respiratory diseases, air pollutants, and meteorological factors; the impacts of spatial heterogeneity in the built environment on air pollution were examined using the multiscale geographically weighted regression model (MGWR).ResultsDuring the study period, the mean level of respiratory diseases (average age 54) was 15.97 persons per day, of which 9.519 for males (average age 57) and 6.451 for females (average age 48); the 24 h mean levels of PM10, PM2.5, NO2, SO2 and O3 were 78.056 μg/m3, 71.962 μg/m3, 54.468 μg/m3, 12.898 μg/m3, and 46.904 μg/m3, respectively; highest association was investigated between PM10 and SO2 (r = 0.762, p < 0.01), followed by NO2 and PM2.5 (r = 0.73, p < 0.01), and PM10 and PM2.5 (r = 0.704, p < 0.01). We observed a significant lag effect of NO2 on respiratory diseases, for lag 0 day and lag 1 day, a 10 μg/m3 increase in NO2 concentration corresponded to 1.009% (95% CI: 1.001, 1.017%) and 1.005% (95% CI: 1.001, 1.011%) increase of respiratory diseases. The spatial distribution of NO2 was significantly influenced by high-density urban development (population density, building density, number of shopping service facilities, and construction land, the bandwidth of these four factors are 43), while green space and parks can effectively reduce air pollution (R2 = 0.649).ConclusionPrevious studies have focused on the effects of air pollution on respiratory diseases and the effects of built environment on air pollution, while this study combines these three aspects and explores the relationship between them. Furthermore, the theory of the “built environment-air pollution-respiratory diseases” cascading mechanism is practically investigated and broken down into specific experimental steps, which has not been found in previous studies. Additionally, we observed a lag effect of NO2 on respiratory diseases and spatial heterogeneity of built environment in the distribution of NO2

Memristive Non-Volatile Memory Based on Graphene Materials

Resistive random access memory (RRAM), which is considered as one of the most promising next-generation non-volatile memory (NVM) devices and a representative of memristor technologies, demonstrated great potential in acting as an artificial synapse in the industry of neuromorphic systems and artificial intelligence (AI), due its advantages such as fast operation speed, low power consumption, and high device density. Graphene and related materials (GRMs), especially graphene oxide (GO), acting as active materials for RRAM devices, are considered as a promising alternative to other materials including metal oxides and perovskite materials. Herein, an overview of GRM-based RRAM devices is provided, with discussion about the properties of GRMs, main operation mechanisms for resistive switching (RS) behavior, figure of merit (FoM) summary, and prospect extension of GRM-based RRAM devices. With excellent physical and chemical advantages like intrinsic Young’s modulus (1.0 TPa), good tensile strength (130 GPa), excellent carrier mobility (2.0 × 105 cm2∙V−1∙s−1), and high thermal (5000 Wm−1∙K−1) and superior electrical conductivity (1.0 × 106 S∙m−1), GRMs can act as electrodes and resistive switching media in RRAM devices. In addition, the GRM-based interface between electrode and dielectric can have an effect on atomic diffusion limitation in dielectric and surface effect suppression. Immense amounts of concrete research indicate that GRMs might play a significant role in promoting the large-scale commercialization possibility of RRAM devices

High temperature superconducting FeSe films on SrTiO3 substrates

Interface enhanced superconductivity at two dimensional limit has become one of most intriguing research directions in condensed matter physics. Here, we report the superconducting properties of ultra-thin FeSe films with the thickness of one unit cell (1-UC) grown on conductive and insulating SrTiO3 (STO) substrates. For the 1-UC FeSe on conductive STO substrate (Nb-STO), the magnetization versus temperature (M-T) measurement shows a diamagnetic signal at 85 K, suggesting the possibility of superconductivity appears at this high temperature. For the FeSe films on insulating STO substrate, systematic transport measurements were carried out and the sheet resistance of FeSe films exhibits Arrhenius TAFF behavior with a crossover from a single-vortex pinning region to a collective creep region. More intriguing, sign reversal of Hall resistance with temperature is observed, demonstrating a crossover from hole conduction to electron conduction above Tc in 1-UC FeSe films

Hybrid Self-Adaptive Algorithm for Community Detection in Complex Networks

The study of community detection algorithms in complex networks has been very active in the past several years. In this paper, a Hybrid Self-adaptive Community Detection Algorithm (HSCDA) based on modularity is put forward first. In HSCDA, three different crossover and two different mutation operators for community detection are designed and then combined to form a strategy pool, in which the strategies will be selected probabilistically based on statistical self-adaptive learning framework. Then, by adopting the best evolving strategy in HSCDA, a Multiobjective Community Detection Algorithm (MCDA) based on kernel k-means (KKM) and ratio cut (RC) objective functions is proposed which efficiently make use of recommendation of strategy by statistical self-adaptive learning framework, thus assisting the process of community detection. Experimental results on artificial and real networks show that the proposed algorithms achieve a better performance compared with similar state-ofthe-art approaches