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Purpose: To report a case of foveolitis associated with dengue fever. Methods: Retrospective, observational case report. Case description: A 12-year-old male child, presented to the retina department with complaints of sudden blurring of vision in left eye since 1 week following dengue fever. His presenting visual acuity in left eye was 20/60, N8. Fundus showed wellcircumscribed, pale yellow-orange lesion at the foveal center in LE. Retinal imaging findings were consistent with the diagnosis of foveolitis. Results: Foveolitis and macular swelling were found to resolve over time on serial OCT, with visual acuity improving to 20/20,N6 with a resolution of scotoma. Conclusion: Infection with dengue virus might be associated with foveolitis. We suggest that serial OCT be performed to monitor maculopathy.

Neuraminidase inhibitors are a type of medication that inhibits the neuraminidase enzyme. They are type of antiviral medication that is often used to treat influenza. In this paper we have taken 53 Compounds which are derivatives of Thiourea and Thiazolidine-4-Carboxylic acid derivatives. We modelled the pIC50 activity using RDF115p, E2s, R1i Parameters. The excellent value of r2 = 0.725 Shows that following model is best suitable. We also Performed docking study and the best docking score is -28.1891, and the best predicted activity is 8.36.

Quantitative structure–activity relationship (QSAR), Density functional theory and molecular docking studies have been performed on a series of 49 1-[2-Hydroxyethoxy) methyl]-6-(phenylthio)-thymine (HEPT) derivatives as anti-HIV agents. A genetic algorithm multiple linear regression (GA-MLR) analysis has revealed that the five variable model comprises of 2D autocorrelation, 2D atom pairs and constitutional descriptors of these compounds are the governing factors for their biological activity. Using this GA-MLR model some new compounds have been proposed that have higher potency than the existing ones. The molecular docking study has shown that these new compounds can form hydrogen bonds with the receptor and have effective steric interactions.

Cloud computing provides organisations with the ability to quickly change infrastructure, provider and service levels. The study looks at factors affecting the usage of cloud computing by organisations in the manufacturing and service sectors in the United States. Extrinsic motivation was found to be a significant factor for change in cloud computing budget allocation in manufacturing, whereas intrinsic motivation was a significant factor in the service sector. The study also shows that organisations’ future budgets include internal and external sources to fulfil their IT needs.

The study projects long-term trends in the IT outsourcing in the manufacturing and service sectors of the United States. The findings suggest that the respondents perceive that there is approximately an equal usage of IT outsourcing in each of manufacturing and service sectors. Furthermore, the Desired Characteristics of Outsourcing – enabler of organisational flexibility, dynamics, and adaptability, redirection of resources, and increased control of operating costs – are positively related to the change in the requirement of IT outsourcing in the case of manufacturing. Meanwhile for the service sector, the Administrative Motivation for Outsourcing – lack of expertise, promising service offerings to subscribe, shrinkage in system life cycle – are positively associated with growth/no-growth in IT outsourcing.

United States based companies have invested more heavily in cloud services than companies in other nations. Despite numerous benefits claimed by cloud service providers, many organisations are still uncertain about the implementation of cloud computing. The aim of this study is to gain a better understanding of the factors that are important to the usage adoption decision of this information technology delivery model and the implications for organisations in the United States. Decision makers were surveyed to discover which attributes were important in their determination of cloud computing utilisation. Using a sample of executives from manufacturing and service sectors in the United States, multiple factors contributing to the recent growth in cloud computing have been identified using a factor analysis. Four factors emerged from the data analysis include extrinsic motivation, intrinsic motivation, perceived risks, and resource constraint.

In the present study we have performed Quantitative structure activity relationship QSAR analysis for 43bisbenzofuran derivatives to estimate the antimalarial activity using some 2D descriptors. Several significant QSAR models has been calculated for predicting the antimalarial activity -logIC50 of these molecules by using the multiple linear regression MLR technique. Among the obtained QSAR models, a four parametric model was most significant having R2=0.9502. An external set was used for confirming the predictive power of the models. High correlation between experimental and predicted antimalarial activity values, was obtained in the validation approach that displayed the good modality of the derived QSAR models. Tripti Kaushal | Anita K | Bashirulla Shaik | Vijay K. Agrawal "QSAR Modeling of Bisbenzofuran Compounds using 2D-Descriptors as Antimalarial Agents" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-2 | Issue-2 , February 2018, URL: https://www.ijtsrd.com/papers/ijtsrd9497.pdf

The prediction of maximum steady state flux values of a chemical compound from its structural features plays an important role in design of transdermal drug delivery systems. In this study, we developed the quantitative structure property relationship (QSPR) models to estimate the maximum steady state flux of 245 drugs-like compounds through the polydimethylsiloxane membranes. A correlation-based feature selection was used for descriptor selection. The selected descriptors, surface tension, polarity, and count of hydrogen accept sites, which are interpretable and can be used to explain the permeability of chemicals. These descriptors are used for developing the QSPR prediction models by multiple linear regression, artificial neural network, support vector machine (SVM) and Instance-Based Learning algorithms using K nearest neighbor machine learning approaches. The models were assessed by internal and external validation. All four approaches yield the QSPR models with good statistics. The models developed by SVM have better prediction performance. These models can be useful for predicting the permeability new untested compounds.

Quantitative Structure-Activity Relationship studies has been performed on some anilinoquinolines . A variety of parameters including 2D- autocorelation, RDF, 3D- MoRSE, WHIM and GETAWAY parameters have been chosen for modeling the antitumor activity of these compounds. The multiple regression analysis reveals that the seven -parametric model is the best for modeling the activity of the compounds under present study. This model has been tested by using cross validated parameters. The results are also discussed on the basis of ridge regression.

Several sterols were isolated from Calotropis procera . Analysis of their spectral data (UV, IR, MS, 1 H, 13 C NMR experiments) confirmed their structures as urs-19(29)-en-3-yl acetate, β-sitosterol and stigmasterol, multiflorenol, urs-19(29)-en-3-β-ol and 3β,27-dihydroxy-urs-18-en-13,28-olide.

Recently Balaban has introduced a J-type G index for modeling those properties/activities where not only the "shape" but also size of the graph influences the property/activity of organic compounds acting as drugs. Here, for the first time G index has been used for modeling the pancreatic β-cell K(ATP) channel openers viz R/S-3,4-dihydro-2,2-dimethyl-6-halo-4-(substituted phenylaminocarbonylamino)-2H-1-benzopyrans. Modeling technology used in this paper has established that the referred activity is excellently modeled by using G as one of the correlating descriptors. The results are critically discussed using Ridge statistics.

This study is based on modeling the anti-malarial activity (log IC50/nm) of 20 cyclic peroxy ketals using topological and physicochemical parameters. The multiple regression analysis reveals that the five-parametric model containing MW, ST, χ eq, 0 χ v, IP2 as correlating parameters is the best for modeling the activity of the compounds under present study. The cross-validated parameters have been calculated by LOO cross-validation procedure for validation of proposed model.

We formally review the Author Affiliation Index (AAI) method as originally conceived by David Harless and Robert J. Reilly from the Economics Department at the Virginia Commonwealth University School of Business and as subsequently developed and interpreted by Gorman and Kanet in their 2005 article. Through this formal review, we first highlight and discuss two important informational inputs that can impact the stability of the AAI scores for journals in any given set of to-be-evaluated journals. We then identify and challenge interpretations related to these scores (one theoretical, one statistical) offered by Gorman and Kanet that result in misleading conclusions about journal quality and that may potentially motivate inappropriate editorial behavior. For important professional decisions of hiring, performance evaluation, promotion, and tenure, we conclude by cautioning against sole reliance on the AAI method for ranking journals and against exclusive interpretation of the score computed via the AAI method as an indicator of journal quality.

Quantitative Structure-Activity Relationship (2D-QSAR) models for binding affinity constants (log Ki) of 78 flavanoid ligands towards the benzodiazepine site of GABA (A) receptor complex were estimated using the PRECLAV (Property-Evaluation by Class Variables) program. The best MLR equation with nine PRECLAV descriptors has R 2 = 0.843 and C = 0.782. Attempt is also made for obtaining 2D-QSAR model using NCSS software. The comparison of the results indicated that the PRECLAV method is very efficient in detecting structure-activity correlation with good predictive power. 2 R

A quantitative structure–activity relationship (QSAR) study has been performed on 5-hydroxy-1H-pyrazol-3(2H)—ones analogue-based Mur B inhibitors—to understand the structural features influencing the affinity of these inhibitors toward the enzyme. The QSAR results show that antibacterial activity could be modeled using steric parameters such as molecular weight (MW), surface tension (ST), index of refraction (IOR) and electronic parameters such as equalized electro-negativity (χeq). The predictive ability of the models was checked by cross-validation method.

The machine learning methods artificial neural network (ANN) and support vector machine (SVM) techniques were used to model intrinsic solubility of 74 generic drugs. The models obtained were compared with those obtained using multiple linear regression (MLR) analysis. Cluster analysis was used to split the data into a training set and test set. The appropriate descriptors were selected using a wrapper approach with multiple linear regressions as target learning algorithm. The descriptor selection and model building were performed with 10 fold cross validation using the training data set. The linear model fits the training set (n = 60) with R(2) = 0.814, while ANN and SVM higher values of R(2) = 0.823 and 0.835, respectively. Though the SVM model shows improvement of training set fitting, the ANN model was slightly superior to SVM and MLR in predicting the test set. The quantitative structure-property relationship study suggests that the theoretically calculated descriptors log P, first-order valence connectivity index ((1)chi(v)), delta chi (Delta(2)chi) and information content ((2)IC) have relevant relationships with intrinsic solubility of generic drugs studied.

I analyzed the contributions made by members of the following constituencies in terms of the number of articles published in the Journal of Operations Management (JOM), Manufacturing and Service Operations Management (MS 9 of which are also in the list of top 20 U.S. business schools. Authors from the schools with top 10 programs in production and operations management contributed 20.2% of the articles published in POM and 3.5% of the articles published in JOM during 1997-2001. They contributed 24.6% of the articles in the eight issues (from July 2000 through June 2002) of M&SOM. During these periods, authors from the top 27 schools contributed 29.3% of the articles published in POM, 9.4% of the articles published in JOM, and 37.4% of the articles to the eight issues of M&SOM. Also during the same periods, authors from business and government organizations contributed 6.6% of the articles published in POM, 1.9% of the articles published in JOM, and 5.8% of the articles published in the eight issues of M&SOM. The findings reported in this paper reflect the reach of the three journals and the perceptions of various constituencies regarding journal quality. The findings about POM and JOM have a very high degree of validity because both journals are more than 10 years old and have well-established constituencies. The conclusions about the M&SOM constituencies are tentative because it is about 3 years old, and it is still developing its constituencies.

Quantitative structure-activity relationship studies were performed to describe and predict the mutagenic activity of a set of 48 nitrated polycyclic aromatic hydrocarbons. From a larger pool of molecular descriptors (topological indices) we arrived at much a smaller set consisting of three correlating parameters. Such a variable selection is made using ncss software in that successive regressions were attempted using maximum-R(2) method. The results are critically discussed using a variety of statistical parameters. Our results have shown that connectivity and shape type indices together with the distance-based Wiener index (W) play a dominating role in modelling of mutagenicity (logTA100). The predictive ability of the models is discussed on the basis of cross-validated parameters.

This review describes topology of the molecule and lipophilicity in that several examples of topological estimation of lipophilicity of the molecule are discussed critically. In doing so, the earlier non-topology methods used to estimate lipophilicity was first discussed critically. Usually, lipophilicity is referred to as partition of organic compound between octanol-water systems, however, some other solvent-systems were also examined topologically. All the proposed models were confirmed by variety of statistical analyses.