Co-authorship patterns and networks in the scientific publications of Shahid Beheshti university of medical sciences

The increasing amount of scientific collaboration has led to an increase in the quality of scientific publications. The goal of this article is to analyze the co-authorship patterns and networks in SBUMS's scientific publications in WOS from 2009 to 2013. Hence, the co-authorship indicators and ratio of national to international collaborations are determined. This research is a descriptive survey including scientometric approaches. HistCite and NWB software is used to draw and analyze the networks. The findings show that the scientific publications of SBUMS are 6633 publications. The most productive authors are Fereidoun Azizi and Mohamad Reza Zali. The maximum number of scientific publications is allocated to the articles written by more than 5 authors. Collaborative Index [CI], Degree of Collaboration [DC], and Collaborative Coefficient [CC] are 4.27, 0.96, and 0.71 respectively. The ratio of national to international collaborations is 4.78. In co-authorship networks, there are 12825 nodes [authors] that are linked with 97618 edges. The highest degree and betweenness centrality belongs to Masjedi showing the communication paths of other nodes overtaking them. The most international collaboration belongs to the USA. The collaboration of authors in SBUMS is almost good but the international collaboration is not so. Encouraging policies for the further collaboration of authors surely leads to increasing the quality and quantity of the scientific publications. It is recommended to repeat the network analysis in different periods of time

Anti -MBP autoantibody changes as a predictor of response to treatment in MS patients

Myelin basic protein [MBP] is one of the most important constituents of the CNS myelin sheaths. It is supposed that an autoimmune response directed against MBP is crucial in the demyelination process in patients with multiple sclerosis. Studies have proved that free anti-MBP level in CSF of MS patients is declined when the patient entered into clinical remission. Some researchers evaluate the changes in serum or CSF level of this antibody during immunomodulatory therapy; the results are different and the relation between the changes in this antibody and response to treatment is poorly investigated

The objective of this study was to assess the relation between the changes in serum level of anti-MBP and clinical remission in patients during treatment with fingolimod. 37 MS patients that were non responder to interferon and glatiramer acetate and were candidates to receive fingolimod were nominated for this study. In this study, the serum level of anti-MBP was evaluated before and after 3 and 6 months of therapy and clinical remission was assessed by changes in Expanded Disability Status Scale [EDSS] scores

The result of this study showed that MS patients, after treatment with interferon, have lower serum anti-MBP level than healthy control group and this difference is statistically significant [p =0.03]

The present study demonstrated that the serum anti-MBP level in MS patient during 6 months of treatment with fingolimod significantly decreased [p<0.001]. However, there was no significant difference in EDSS of MS patients during 6 months of treatment with fingolimod [p < 0.001]

Using support vector machines in predicting and classifying factors affecting preterm delivery

Various statistical methods have been proposed in terms of predicting the outcomes of facing special factors. In the classical approaches, making the probability distribution or known probability density functions is ordinarily necessary to predict the desired outcome. However, most of the times enough information about the probability distribution of studied variables is not available to the researcher in practice. In such circumstances, we need that the predictors function well without knowing the probability distribution or probability density. It means that with the minimum assumptions, we obtain predictors with high precision. Support vector machine [SVM] is a good statistical method of prediction. The aim of this study is to compare two statistical methods, SVM and logistic regression. To that end, the data on premature infants born at Tehran Milad Hospital is collected and used

effect of capital structure on the profitability of pharmaceutical companies the case of Iran

Funding combination is the most important issue for the companies while they know the amount of required capital. Companies should be careful regarding the appliance of financial providing methods compatible with the investment strategy of company and profitability. This study seeks to examine the relationship between the capital structure and the profitability of pharmaceutical companies in Iran. For this purpose, top 30 Iranian pharmaceutical companies defined as study samples and their financial data were gathered for the period of 2001-2010. In this study, the net margin profit and debts to asset ratio were used as indicators of profitability and capital structure, respectively and sales growth was used as a control variable. Results showed that there was significant negative relationship between the profitability and the capital structure which means that the pharmaceutical companies have established a Pecking Order Theory and the internal financing has led to more profitability