Animal models of NASH: getting both pathology and metabolic context right

Non-alcoholic fatty liver disease (NAFLD) is the most common cause of referral to liver clinics, and its progressive form, non-alcoholic steatohepatitis (NASH), can lead to cirrhosis and end-stage liver disease. The main risk factors for NAFLD/NASH are the metabolic abnormalities commonly observed in metabolic syndrome: insulin resistance, visceral obesity, dyslipidemia and altered adipokine profile. At present, the causes of progression from NAFLD to NASH remain poorly defined, and research in this area has been limited by the availability of suitable animal models of this disease. In the past, the main models used to investigate the pathogenesis of steatohepatitis have either failed to reproduce the full spectrum of liver pathology that characterizes human NASH, or the liver pathology has developed in a metabolic context that is not representative of the human condition. In the last few years, a number of models have been described in which the full spectrum of liver pathology develops in an appropriate metabolic context. In general, the underlying cause of metabolic defects in these models is chronic caloric overconsumption, also known as overnutrition. Overnutrition has been achieved in a number of different ways, including forced feeding, administration of high-fat diets, the use of genetically hyperphagic animals, or a combination of these approaches. The purpose of the present review is to critique the liver pathology and metabolic abnormalities present in currently available animal models of NASH, with particular focus on models described in approximately the last 5 years.This research was funded through a grant. - Research in the authors' laboratory is supported by program grant 358398 from the Australian National Health and Medical Research Council (NHMRC)

Spin-polarized quasiparticle transport in cuprate superconductors

The effects of spin-polarized quasiparticle transport in superconducting YBa2Cu3O7-delta (YBCO) epitaxial films are investigated by means of current injection into perovskite ferromagnet-insulator-superconductor (F-I-S) heterostructures. These effects are compared with the injection of simple quasiparticles into control samples of perovskite nonmagnetic metal-insulator-superconductor (N-I-S). Systematic studies of the critical current density (J(c)) as a function of the injection current density (J(inj)), temperature (T), and the thickness (d) of the superconductor reveal drastic differences between the F-I-S and N-I-S heterostructures, with strong suppression of J(c) and a rapidly increasing characteristic transport length near the superconducting transition temperature T-c only in the F-I-S samples. The temperature dependence of the efficiency (etaequivalent toDeltaJ(c)/J(inj); DeltaJ(c): the suppression of critical current due to finite J(inj)) in the F-I-S samples is also in sharp contrast to that in the N-I-S samples, suggesting significant redistribution of quasiparticles in F-I-S due to the longer lifetime of spin-polarized quasiparticles. Application of conventional theory for nonequilibrium superconductivity to these data further reveal that a substantial chemical potential shift mu(*) in F-I-S samples must be invoked to account for the experimental observation, whereas no discernible chemical potential shift exists in the N-I-S samples, suggesting strong effects of spin-polarized quasiparticles on cuprate superconductivity. The characteristic times estimated from our studies are suggestive of anisotropic spin relaxation processes, possibly with spin-orbit interaction dominating the c-axis spin transport and exchange interaction prevailing within the CuO2 planes. Several alternative scenarios attempted to account for the suppression of critical currents in F-I-S samples are also critically examined, and are found to be neither compatible with experimental data nor with the established theory of nonequilibrium superconductivity

Early intervention screening involves more than “simple” screening

From the perspective of preventive medical discourse, early interventional screening is one of the most important ways to intervene with developmentally delayed children and an important service for children’s public health. This paper details an historical perspective of early developmental screening in the United States and reexamines the concepts of early screening in widespread use in children’s populations to expose undisclosed facts. These facts remind us that early developmental screening might not be totally based on children’s needs, but on both the government’s desires and political activities. As a result, a certain population was identified as subjects that required developmental screening resulting in very large numbers studies. Under these circumstances, developmental screening instruments were used in conjunction with pediatricians’ individualized appraisals. The approach to developmental surveillance was to expand from the children to the parents’ concern. The instrument not only legitimately recruited parents and facilitated parents’ surveillance of their children’s development, but was also was a platform for highlighting parent-child interactions. This discussion may help community health nurses further understand different perspectives of early interventional screening in practice

Robustness of One-Dimensional Photonic Bandgaps Under Random Variations of Geometrical Parameters

The supercell method is used to study the variation of the photonic bandgaps in one-dimensional photonic crystals under random perturbations to thicknesses of the layers. The results of both plane wave and analytical band structure and density of states calculations are presented along with the transmission cofficient as the level of randomness and the supercell size is increased. It is found that higher bandgaps disappear first as the randomness is gradually increased. The lowest bandgap is found to persist up to a randomness level of 55 percent.Comment: Submitted to Physical Review B on April 8 200

Scanning Tunneling Spectroscopic Studies of the Low-Energy Quasiparticle Excitations in Cuprate Superconductors

We report scanning tunneling spectroscopic (STS) studies of the low-energy quasiparticle excitations of cuprate superconductors as a function of magnetic field and doping level. Our studies suggest that the origin of the pseudogap (PG) is associated with competing orders (COs), and that the occurrence (absence) of PG above the superconducting (SC) transition T_c is associated with a CO energy Δ_(CO) larger (smaller) than the SC gap Δ_(SC). Moreover, the spatial homogeneity of Δ_(SC) and Δ_(CO) depends on the type of disorder in different cuprates: For optimally and under-doped YBa_2Cu_3O_(7−δ) (Y-123), we find that Δ_(SC) < Δ_(CO) and that both Δ_(SC) and Δ(CO) exhibit long-range spatial homogeneity, in contrast to the highly inhomogeneous STS in Bi_2Sr_2CaCu_2O_(8+x) (Bi-2212). We attribute this contrast to the stoichiometric cations and ordered apical oxygen in Y-123, which differs from the non-stoichiometric Bi-to-Sr ratio in Bi-2212 with disordered Sr and apical oxygen in the SrO planes. For Ca-doped Y-123, the substitution of Y by Ca contributes to excess holes and disorder in the CuO_2 planes, giving rise to increasing inhomogeneity, decreasing Δ_(SC) and Δ_(CO), and a suppressed vortex-solid phase. For electron-type cuprate Sr_(0.9)La_(0.1)CuO_2 (La-112), the homogeneous Δ_(SC) and Δ_(CO) distributions may be attributed to stoichiometric cations and the absence of apical oxygen, with Δ_(CO) < Δ_(SC) revealed only inside the vortex cores. Finally, the vortex-core radius (ξ_(halo)) in electron-type cuprates is comparable to the SC coherence length ξ_(SC), whereas ξ_(halo) ∼ 10ξ_(SC) in hole-type cuprates, suggesting that ξ_(halo) may be correlated with the CO strength. The vortex-state irreversibility line in the magnetic field versus temperature phase diagram also reveals doping dependence, indicating the relevance of competing orders to vortex pinning

Troubleshooting Arterial-Phase MR Images of Gadoxetate Disodium-Enhanced Liver.

Gadoxetate disodium is a widely used magnetic resonance (MR) contrast agent for liver MR imaging, and it provides both dynamic and hepatobiliary phase images. However, acquiring optimal arterial phase images at liver MR using gadoxetate disodium is more challenging than using conventional extracellular MR contrast agent because of the small volume administered, the gadolinium content of the agent, and the common occurrence of transient severe motion. In this article, we identify the challenges in obtaining high-quality arterial-phase images of gadoxetate disodium-enhanced liver MR imaging and present strategies for optimizing arterial-phase imaging based on the thorough review of recent research in this field

Quantitative analysis and subcellular distribution of mRNA and protein expression of the hyperpolarization-activated cyclic nucleotide-gated channels throughout development in rat hippocampus.

The properties of the hyperpolarization-activated current (I(h)) and its roles in hippocampal network function evolve radically during development. Because I(h) is conducted by the hyperpolarization-activated cyclic nucleotide-gated (HCN) cation channels, we tested the hypothesis that understanding the quantitative developmental profiles of HCN1, HCN2, and HCN4 expression, and the isoform- and age-specific progression of their subcellular distribution, should shed light on the established modifications of the properties of I(h) throughout development. Combined quantitative in situ hybridization, regional western blots, and high-resolution, dual-label immunocytochemistry revealed striking and novel information about the expression and distribution of the HCN channel isoforms in the developing hippocampal formation. In cornus ammon 1 (CA) pyramidal cell layer, a robust increase of HCN1 mRNA and protein expression occurred with age, with reciprocal reduction of HCN4 and relatively stable HCN2 levels. These distinct expression patterns raised the contribution of HCN1 to the total HCN channel pool from 33% to 65% consonant with acceleration and reduced cyclic adenosine mono phosphate (cAMP) sensitivity of I(h) in this region with age. In CA3, strong expression of HCN1 already neonatally supports the recently established role of this conductance in neonatal, age-specific, hippocampal oscillations (giant depolarizing potentials). Notably, HCN1 channels were present and probably transported to dendritic compartments already on postnatal day (P) 2, whereas HCN2 channel protein was not evident in dendrites for the first 2 weeks of life. HCN2 mRNA and protein expression remained fairly constant subsequent to the first week of life in all hippocampal subfields examined, whereas HCN4 mRNA and protein expression declined after maximal neonatal expression, so that the contribution of this isoform to the total HCN channel pool dropped from 43% (CA1) and 34% (CA3) on P11 to 8% (CA1) and 19% (CA3) on P90. Interneuronal expression of all HCN channel isoforms in stratum pyramidale was robust in parvalbumin-but not in cholecystokinin-expressing populations and with a subunit-specific subcellular distribution. Taken together, these data suggest that early in life, HCN4 may contribute significantly to the functions of I(h) in specific hippocampal regions. In addition, these evolving, differential quantitative, and subcellular expression patterns of the HCN channel isoforms support age-specific properties and functions of I(h) within the developing hippocampal formation

Regression with Linear Factored Functions

Many applications that use empirically estimated functions face a curse of dimensionality, because the integrals over most function classes must be approximated by sampling. This paper introduces a novel regression-algorithm that learns linear factored functions (LFF). This class of functions has structural properties that allow to analytically solve certain integrals and to calculate point-wise products. Applications like belief propagation and reinforcement learning can exploit these properties to break the curse and speed up computation. We derive a regularized greedy optimization scheme, that learns factored basis functions during training. The novel regression algorithm performs competitively to Gaussian processes on benchmark tasks, and the learned LFF functions are with 4-9 factored basis functions on average very compact.Comment: Under review as conference paper at ECML/PKDD 201