Эффективность применения препарата "Диаскинтест" в диагностике и дифференциальной диагностике туберкулеза легких

ТУБЕРКУЛЕЗ ЛЕГКИХ /ДИАГНДИАГНОСТИКА ДИФФЕРЕНЦИАЛЬНАЯБИОТЕХНОЛОГИЯИННОВАЦИЙ РАСПРОСТРАНЕНИЕДИАСКИНТЕС

Leucine-rich repeat kinase 2 mutations and Parkinson’s disease: three questions

Mutations in the gene encoding LRRK2 (leucine-rich repeat kinase 2) were first identified in 2004 and have since been shown to be the single most common cause of inherited Parkinson’s disease. The protein is a large GTP-regulated serine/threonine kinase that additionally contains several protein–protein interaction domains. In the present review, we discuss three important, but unresolved, questions concerning LRRK2. We first ask: what is the normal function of LRRK2? Related to this, we discuss the evidence of LRRK2 activity as a GTPase and as a kinase and the available data on protein–protein interactions. Next we raise the question of how mutations affect LRRK2 function, focusing on some slightly controversial results related to the kinase activity of the protein in a variety of in vitro systems. Finally, we discuss what the possible mechanisms are for LRRK2-mediated neurotoxicity, in the context of known activities of the protein

Hybridising heuristics within an estimation distribution algorithm for examination timetabling

This paper presents a hybrid hyper-heuristic approach based on estimation distribution algorithms. The main motivation is to raise the level of generality for search methodologies. The objective of the hyper-heuristic is to produce solutions of acceptable quality for a number of optimisation problems. In this work, we demonstrate the generality through experimental results for different variants of exam timetabling problems. The hyper-heuristic represents an automated constructive method that searches for heuristic choices from a given set of low-level heuristics based only on non-domain-specific knowledge. The high-level search methodology is based on a simple estimation distribution algorithm. It is capable of guiding the search to select appropriate heuristics in different problem solving situations. The probability distribution of low-level heuristics at different stages of solution construction can be used to measure their effectiveness and possibly help to facilitate more intelligent hyper-heuristic search methods

Finite Simulation Budget Allocation for Ranking and Selection

We consider a simulation-based ranking and selection (R&S) problem under a fixed budget setting. Existing budget allocation procedures focus either on asymptotic optimality or on one-step-ahead allocation efficiency. Neither of them depends on the fixed simulation budget, the ignorance of which could lead to an inefficient allocation, especially when the simulation budget is finite. In light of this, we develop a finite-budget allocation rule that is adaptive to the simulation budget. Theoretical results show that the budget allocation strategies are distinctively different between a finite budget and a sufficiently large budget. Our proposed allocation rule can dynamically determine the ratio of budget allocated to designs according to different simulation budget and is optimal when the simulation budget goes to infinity, indicating it not only possesses desirable finite-budget properties but also achieves asymptotic optimality. Based on the proposed allocation rule, two efficient finite simulation budget allocation algorithms are developed. In the numerical experiments, we use both synthetic examples and a case study to show the superior efficiency of our proposed allocation rule

Systematic review:genetic associations for prognostic factors of urinary bladder cancer

Introduction: Many germline associations have been reported for urinary bladder cancer (UBC) outcomes and prognostic characteristics. It is unclear whether there are overlapping genetic patterns for various prognostic endpoints. We aimed to review contemporary literature on genetic associations with UBC prognostic outcomes and to identify potential overlap in reported genes. Methods: EMBASE, MEDLINE, and PubMed databases were queried for relevant articles in English language without date restrictions. The initial search identified 1346 articles. After exclusions, 112 studies have been summarized. Cumulatively, 316 single-nucleotide polymorphisms (SNPs) were reported across prognostic outcomes (recurrence, progression, death) and characteristics (tumor stage, grade, size, age, risk group). There were considerable differences between studied outcomes in the context of genetic associations. The most commonly reported SNPs were located in OGG1, TP53, and MDM2. For outcomes with the highest number of reported associations (ie, recurrence and death), functional enrichment annotation yields different terms, potentially indicating separate biological mechanisms. Conclusions: Our study suggests that all UBC prognostic outcomes may have different biological origins with limited overlap. Further validation of these observations is essential to target a phenotype that could best predict patient outcome and advance current management practices

Electrochemical upgrading of biomass-derived 5-hydroxymethylfurfural and furfural over oxygen vacancy-rich NiCoMn-layered double hydroxides nanosheets

Rational design of low-cost and active electrocatalysts is crucial for upgrading of biomass-derived chemicals. Here, we report highly efficient catalysts ternary NiCoMn-layered double hydroxides (NiCoMn-LDHs) nanosheets which are oxygen vacancy-rich, produced under controllable conditions for the electrooxidation of both 5-hydroxymethylfurfural (HMF) to 2,5-furandicarboxylic acid (FDCA) and furfural to furoic acid (FurAc) under mild conditions, respectively. Electrochemical tests showed that the oxidation of HMF and furfural occurred prior to the oxidation of water at lower applied potentials with NiCoMn-LDHs catalysts. High yields of FDCA (91.7%) and FurAc (92.4%) were achieved in 2.5 h using 1.15 nm thick NiCoMn-LDHs nanosheets under the optimal conditions. The mechanism for the superior performance, high durability, and good faradaic efficiency has been elucidated by comprehensive characterization, which confirmed that ultrathin nanosheets expose more Co-NiOOH active sites with oxygen vacancies, facilitating the synergistic effect between HMF and furfural oxidation reaction on Co–Ni and Mn2+ states. The oxygen vacancy-rich NiCoMn-LDHs nanosheet catalysts present a novel and energy-efficient solution to obtain upgraded biochemicals

Systematic Analysis and Biomarker Study for Alzheimer's Disease.

Revealing the relationship between dysfunctional genes in blood and brain tissues from patients with Alzheimer's Disease (AD) will help us to understand the pathology of this disease. In this study, we conducted the first such large systematic analysis to identify differentially expressed genes (DEGs) in blood samples from 245 AD cases, 143 mild cognitive impairment (MCI) cases, and 182 healthy control subjects, and then compare these with DEGs in brain samples. We evaluated our findings using two independent AD blood datasets and performed a gene-based genome-wide association study to identify potential novel risk genes. We identified 789 and 998 DEGs common to both blood and brain of AD and MCI subjects respectively, over 77% of which had the same regulation directions across tissues and disease status, including the known ABCA7, and the novel TYK2 and TCIRG1. A machine learning classification model containing NDUFA1, MRPL51, and RPL36AL, implicating mitochondrial and ribosomal function, was discovered which discriminated between AD patients and controls with 85.9% of area under the curve and 78.1% accuracy (sensitivity = 77.6%, specificity = 78.9%). Moreover, our findings strongly suggest that mitochondrial dysfunction, NF-κB signalling and iNOS signalling are important dysregulated pathways in AD pathogenesis

Dynamic biclustering of microarray data by multi-objective immune optimization

Abstract Background Newly microarray technologies yield large-scale datasets. The microarray datasets are usually presented in 2D matrices, where rows represent genes and columns represent experimental conditions. Systematic analysis of those datasets provides the increasing amount of information, which is urgently needed in the post-genomic era. Biclustering, which is a technique developed to allow simultaneous clustering of rows and columns of a dataset, might be useful to extract more accurate information from those datasets. Biclustering requires the optimization of two conflicting objectives (residue and volume), and a multi-objective artificial immune system capable of performing a multi-population search. As a heuristic search technique, artificial immune systems (AISs) can be considered a new computational paradigm inspired by the immunological system of vertebrates and designed to solve a wide range of optimization problems. During biclustering several objectives in conflict with each other have to be optimized simultaneously, so multi-objective optimization model is suitable for solving biclustering problem. Results Based on dynamic population, this paper proposes a novel dynamic multi-objective immune optimization biclustering (DMOIOB) algorithm to mine coherent patterns from microarray data. Experimental results on two common and public datasets of gene expression profiles show that our approach can effectively find significant localized structures related to sets of genes that show consistent expression patterns across subsets of experimental conditions. The mined patterns present a significant biological relevance in terms of related biological processes, components and molecular functions in a species-independent manner. Conclusions The proposed DMOIOB algorithm is an efficient tool to analyze large microarray datasets. It achieves a good diversity and rapid convergence

Bridging the Mid-Infrared-to-Telecom Gap with Silicon Nanophotonic Spectral Translation

Expanding far beyond traditional applications in optical interconnects at telecommunications wavelengths, the silicon nanophotonic integrated circuit platform has recently proven its merits for working with mid-infrared (mid-IR) optical signals in the 2-8 {\mu}m range. Mid-IR integrated optical systems are capable of addressing applications including industrial process and environmental monitoring, threat detection, medical diagnostics, and free-space communication. Rapid progress has led to the demonstration of various silicon components designed for the on-chip processing of mid-IR signals, including waveguides, vertical grating couplers, microcavities, and electrooptic modulators. Even so, a notable obstacle to the continued advancement of chip-scale systems is imposed by the narrow-bandgap semiconductors, such as InSb and HgCdTe, traditionally used to convert mid-IR photons to electrical currents. The cryogenic or multi-stage thermo-electric cooling required to suppress dark current noise, exponentially dependent upon the ratio Eg/kT, can limit the development of small, low-power, and low-cost integrated optical systems for the mid-IR. However, if the mid-IR optical signal could be spectrally translated to shorter wavelengths, for example within the near-infrared telecom band, photodetectors using wider bandgap semiconductors such as InGaAs or Ge could be used to eliminate prohibitive cooling requirements. Moreover, telecom band detectors typically perform with higher detectivity and faster response times when compared with their mid-IR counterparts. Here we address these challenges with a silicon-integrated approach to spectral translation, by employing efficient four-wave mixing (FWM) and large optical parametric gain in silicon nanophotonic wires

Cover to Volume 3

The fibroblast mitogen platelet-derived growth factor -BB (PDGF-BB) induces a transient expression of the orphan nuclear receptor NR4A1 (also named Nur77, TR3 or NGFIB). The aim of the present study was to investigate the pathways through which NR4A1 is induced by PDGF-BB and its functional role. We demonstrate that in PDGF-BB stimulated NIH3T3 cells, the MEK1/2 inhibitor CI-1040 strongly represses NR4A1 expression, whereas Erk5 downregulation delays the expression, but does not block it. Moreover, we report that treatment with the NF-κB inhibitor BAY11-7082 suppresses NR4A1 mRNA and protein expression. The majority of NR4A1 in NIH3T3 was found to be localized in the cytoplasm and only a fraction was translocated to the nucleus after continued PDGF-BB treatment. Silencing NR4A1 slightly increased the proliferation rate of NIH3T3 cells; however, it did not affect the chemotactic or survival abilities conferred by PDGF-BB. Moreover, overexpression of NR4A1 promoted anchorage-independent growth of NIH3T3 cells and the glioblastoma cell lines U-105MG and U-251MG. Thus, whereas NR4A1, induced by PDGF-BB, suppresses cell growth on a solid surface, it increases anchorage-independent growth