The Impact of Hyperglycemia on VEGF Secretion in Retinal Endothelial Cells

AbstractBackground:Diabetic retinopathy is a serious sight-threatening complication which is manifested by excessive angiogenesis in diabetic patients.Aim:We hypothesize that cultured Rhesus monkey retinal endothelial cells (RhRECs) respond to high glucose with a change in cell proliferation and vascular endothelial growth factor (VEGF) secretion.Materials and methods:In our study, 20 000 cells per well were treated without glucose or with 5.5 mM, 18.5 mM and 30 mM glucose for 24 hours. Viable cells were counted using trypan blue dye exclusion method. VEGF concentrations were measured in cell media by ELISA method.Results:The number of viable cells incubated with 5.5 mM glucose increased significantly by 53.7% after 24 hours. In comparison, the number of viable cells decreased by 2.8% at 18.5 mM of glucose and by 20.4% at 30 mM of glucose after 24 hours of incubation. In contrast to this effect of glucose on the number of viable cells, a significant increase in VEGF levels (pg/mL) in the cell media with a glucose concentration of 0 mM compared to 5.5 mM of glucose was found. VEGF secretion in cell medium with 18.5 and 30 mM of glucose increased non-significantly in comparison with euglycemic levels.Conclusion:Our results show that viability of retinal endothelial cells and VEGF release are highly responsive to changes in glucose concentration. Such glucose-induced changes in retinal endothelial cells may negatively impact the integrity of the microvasculature in the diabetic retina leading to angiogenesis and microaneursym.</jats:p

Identification of Pathogenicity-Related Genes in the Vascular Wilt Fungus Verticillium dahliae by Agrobacterium tumefaciens-Mediated T-DNA Insertional Mutagenesis

Verticillium dahliae is the causal agent of vascular wilt in many economically important crops worldwide. Identification of genes that control pathogenicity or virulence may suggest targets for alternative control methods for this fungus. In this study, Agrobacteriumtumefaciens-mediated transformation (ATMT) was applied for insertional mutagenesis of V. dahliae conidia. Southern blot analysis indicated that T-DNAs were inserted randomly into the V. dahliae genome and that 69% of the transformants were the result of single copy T-DNA insertion. DNA sequences flanking T-DNA insertion were isolated through inverse PCR (iPCR), and these sequences were aligned to the genome sequence to identify the genomic position of insertion. V. dahliae mutants of particular interest selected based on culture phenotypes included those that had lost the ability to form microsclerotia and subsequently used for virulence assay. Based on the virulence assay of 181 transformants, we identified several mutant strains of V. dahliae that did not cause symptoms on lettuce plants. Among these mutants, T-DNA was inserted in genes encoding an endoglucanase 1 (VdEg-1), a hydroxyl-methyl glutaryl-CoA synthase (VdHMGS), a major facilitator superfamily 1 (VdMFS1), and a glycosylphosphatidylinositol (GPI) mannosyltransferase 3 (VdGPIM3). These results suggest that ATMT can effectively be used to identify genes associated with pathogenicity and other functions in V. dahliae

MAIA, Fc receptor–like 3, supersedes JUNO as IZUMO1 receptor during human fertilization

Gamete fusion is a critical event of mammalian fertilization. A random one-bead one-compound combinatorial peptide library represented synthetic human egg mimics and identified a previously unidentified ligand as Fc receptor–like 3, named MAIA after the mythological goddess intertwined with JUNO. This immunoglobulin super family receptor was expressed on human oolemma and played a major role during sperm-egg adhesion and fusion. MAIA forms a highly stable interaction with the known IZUMO1/JUNO sperm-egg complex, permitting specific gamete fusion. The complexity of the MAIA isotype may offer a cryptic sexual selection mechanism to avoid genetic incompatibility and achieve favorable fitness outcomes

Characterization of Trapped Lignin-Degrading Microbes in Tropical Forest Soil

Lignin is often the most difficult portion of plant biomass to degrade, with fungi generally thought to dominate during late stage decomposition. Lignin in feedstock plant material represents a barrier to more efficient plant biomass conversion and can also hinder enzymatic access to cellulose, which is critical for biofuels production. Tropical rain forest soils in Puerto Rico are characterized by frequent anoxic conditions and fluctuating redox, suggesting the presence of lignin-degrading organisms and mechanisms that are different from known fungal decomposers and oxygen-dependent enzyme activities. We explored microbial lignin-degraders by burying bio-traps containing lignin-amended and unamended biosep beads in the soil for 1, 4, 13 and 30 weeks. At each time point, phenol oxidase and peroxidase enzyme activity was found to be elevated in the lignin-amended versus the unamended beads, while cellulolytic enzyme activities were significantly depressed in lignin-amended beads. Quantitative PCR of bacterial communities showed more bacterial colonization in the lignin-amended compared to the unamended beads after one and four weeks, suggesting that the lignin supported increased bacterial abundance. The microbial community was analyzed by small subunit 16S ribosomal RNA genes using microarray (PhyloChip) and by high-throughput amplicon pyrosequencing based on universal primers targeting bacterial, archaeal, and eukaryotic communities. Community trends were significantly affected by time and the presence of lignin on the beads. Lignin-amended beads have higher relative abundances of representatives from the phyla Actinobacteria, Firmicutes, Acidobacteria and Proteobacteria compared to unamended beads. This study suggests that in low and fluctuating redox soils, bacteria could play a role in anaerobic lignin decomposition

Інноваційні підходи в управлінні банкрутством: приклад країн Вишеградської четвірки

Since the first bankruptcy prediction models developed in the 60th of 20th century numerous different models have been constructed through the world. These individual models for bankruptcy prediction have been created in different time and space using different methods and variables. During this period various statistical methods have been used starting with the most popular univariate, linear and multivariate discriminant analysis, logistic regression, probit regression, decision trees, neural networks, rough sets, linear programming, principal component analysis, data envelopment analysis, survival analysis and so on. Therefore, we aim to provide deep insight and analyse the bankruptcy prediction models developed in countries of Visegrad four, with the emphasis on methods applied and explanatory variables used in these models, and evaluate them through appropriate statistical methods. Specifically, cluster analysis to explore the differences between basic groups of financial indicators and designed clusters of explanatory variables. Based on the analysis of more than one hundred bankruptcy prediction models we can conclude the most used variables, which serves as a basis for further research and development of prediction models in Visegrad group countries. Three clusters were developed which representing various explanatory variables while these clusters differ from basic groups of financial indicators. According to detected clusters we recommend to choose the most frequently used variables from each created cluster. From the cluster one revenues from sales/total assets ratio; from the cluster two the construction of models should contain current ratio, and from the cluster three we recommend to use ROE. Also if we take into consideration the total frequency together with the constructed clusters we advise to use more variables from clusters two and three. Results of the provided study may be used not only by researchers and enterprises but also by investors during the construction of bankruptcy prediction models in conditions of an individual country.Авторами визначено, що перші моделі діагностики банкротства були розроблені у 60-х роках XX століття. При цьому набір змінних, що були характерними для конкретного випадку діагностики банкрутства. Так, використовувались статистичні методи одновимірного, лінійного та багатовимірного дискримінантного аналізу, моделі логістичної регресії, пробіт-регресії, дерева рішень, нейронних мереж, неточних множин, лінійного програмування, метод основних компонентів, аналізу зведених даних тощо. У статті проаналізовано та систематизовано основні моделі діагностики ймовірності банкрутства, що застосовувались в країнах Вишеградської групи, з виокремленням основних пояснювальних змінних. Авторами здійснено кластерний аналіз з метою вивчення відмінностей між основними групами фінансових показників та розробленими кластерами пояснювальних змінних. На основі результатів аналізу понад ста моделей прогнозування банкрутства сформовано систему змінних, що стало основою для подальших досліджень та розробок моделей прогнозування в країнах Вишеградської групи. У статті сформовано три кластери, що включають різні пояснювальні змінні, що відрізняються від основних груп фінансових показників. Відповідно до виявлених кластерів, запропоновано систему змінних. Так, з першого кластеру виокремлено наступні показники: коефіцієнт доходу від продажу/загальних активів. З другого кластеру: показники співвідношення, а відповідно з третього кластеру показники ROE. Крім цього встановлено, що найчастіше на практиці використовують показники з кластеру два та три. Авторами наголошено, що отримані результати дослідження можуть бути використані не тільки дослідниками та підприємствами, але й інвесторами при побудові моделі прогнозування банкрутства в умовах окремої країни

Modele upadłości : weryfikowanie ich ważności jako czynnik prognostyczny niepowodzenia korporacyjnego

Although the issue of corporate failure analysis is a hot topic for business research since the last century, even nowadays there are numerous researches focusing on assessing the financial health of companies. Within increasing internationalization and globalization the demand for bankruptcy prediction is important not only for owners of the companies, but also for other interested groups. We aim to test the validity of prediction models developed as partial results of our research project. Bankruptcy prediction models were constructed on the data set of Slovak companies covering the year 2015 and based on the various statistical methodologies. We provided the validity of these models and their prediction accuracy on the data set of Slovak companies covering the following year 2016.Chociaż kwestia analizy niepowodzenia korporacyjnego jest gorącym tematem badań biznesowych od zeszłego wieku, nawet obecnie prowadzone są liczne badania skupiające się na ocenie kondycji finansowej firm. W warunkach rosnącej internacjonalizacji i globalizacji zapotrzebowanie na prognozy bankructwa jest ważne nie tylko dla właścicieli firm, ale także dla innych zainteresowanych grup. Celem artykułu jest sprawdzenie ważności modeli prognostycznych opracowanych jako częściowe wyniki projektu badawczego przez autorów. Modele przewidywania bankructwa zostały zbudowane na zbiorze danych słowackich firm w roku 2015 na podstawie różnych metodologii statystycznych. Zapewniona została poprawność tych modeli i dokładność ich prognozowania na zbiorze danych słowackich firm obejmująca rok 2016

Detection of coding variants for each NGS platform.

<p>Detection of coding variants for each NGS platform.</p