As a part of an international study on the molecular analysis of Diffuse Large B-cell Lymphoma (DLBCL), a robust protocol for gene expression analysis from RNA extraction to qRT-PCR using Formalin Fixed Paraffin Embedded tissues was developed. Here a study was conducted to define a strategy to validate the previously reported 6-gene (LMO2, BCL6, FN1, CCND2, SCYA3 and BCL2) model as predictor of prognosis in DLBCL. To avoid variation, all samples were tested in a single centre and single platform. This study comprised 8 countries (Brazil, Chile, Hungary, India, Philippines, S. Korea, Thailand and Turkey). Using the Kaplan-Meier and log rank test on patients (n=162) and two mortality risk groups (with those above and below the mean representing high and low risk groups) confirmed that the 6-gene predictor score correlates significantly with overall survival (OS, p<0.01) but not with event free survival (EFS, p=0.18). Adding the International Prognostic Index (IPI) shows that the 6-gene predictor score correlates significantly with high IPI scores for OS (p<0.05), whereas those with low IPI scores show a trend not reaching significance (p=0.08). This study defined an effective and economical qRT-PCR strategy and validated the 6-gene score as a predictor of OS in an international setting.
Addressing the global burden of cancer, understanding its diverse biology, and promoting appropriate prevention and treatment strategies around the world has become a priority for the United Nations and International Atomic Energy Agency (IAEA), the WHO, and International Agency for Research on Cancer (IARC). The IAEA sponsored an international prospective cohort study to better understand biology, treatment response, and outcomes of diffuse large B-cell lymphoma (DLBCL) in low and middle-income countries across five UN-defined geographical regions. We report an analysis of biological variation in DLBCL across seven ethnic and environmentally diverse populations. In this cohort of 136 patients treated to a common protocol, we demonstrate significant biological differences between countries, characterized by a validated prognostic gene expression score (p < .0001), but International Prognostic Index (IPI)-adjusted survivals in all participating countries were similar. We conclude that DLBCL treatment outcomes in these populations can be benchmarked to international standards, despite biological heterogeneity.
This article tries to understand the relationship between agency cost, debt financing and Indian real estate companies’ performance. The study attempts to document the effect of debt on the firm’s profitability and then explores the reason behind such an impact by introducing the agency cost as a parameter. The study is conducted in two phases. Phase I is carried out to establish the relationship between debt financing and the firm’s financial performance. In Phase II, the study is conducted to understand the impact of agency cost on debt financing. Firms from the BSE Realty Index were selected for the period 2011–2018. Profitability is measured through return on equity (ROE), whereas debt financing is measured through the firm’s leverage ratio. The agency cost is measured through the asset utilisation ratio and general expense to sales ratio. Panel regression method is used to understand the impact of debt financing and agency cost on the firms’ profitability. The result of Phase I suggests a significant negative relationship between debt financing and the ROE and the result of Phase II suggests a positive relationship between the agency cost and debt financing. This means that reduction in agency cost will lead to lesser amount of debt financing thereby improving the firm’s financial performance.
the systematic risk that is the market risk which cannot be eliminated and it thus remains the major Stock market return is one of the most crucial factors in component influencing the volatility. The market the stock market which is influenced by many factors.risk is influenced by various macroeconomic The volatility in the market return can lead to severe losses even if the portfolio is diversified. Thus it is very factors like the Exchange Rate, Index of Industrial important to track and determine the trend of the Production, GDP growth rate, Interest Rate, macroeconomic factors on the stock market return.This Inflation Rate and many more. investigation takes into account various factors like The investigation of this research revolves on Inflation Rate, Interest Rate, GDP Growth Rate, studying the impact of the above mentioned Exchange Rate, and Index of Industrial Production. factors on NIFTY. The research is mainly done in These variables are analysed along with the movement two phases; the first phase includes the check on of Nifty. The data is taken from December 2008the stationarity of the variables both dependent December 2018 and is taken in yearly format. The study and independent. This is done because before uses two techniques, the Augmented Dickey Fuller test doing any multiple regressions it becomes (ADF Test) and Multiple Regression Test. Multiple mandatory to check the stationarity of the data. regression tests conducted in this project showed that The dependent variable is NIFTY while the GDP and Exchange Rate are having negative impact on explanatory variables are Exchange Rate, Index of the stock market which indicated that, with the Industrial Production, GDP growth rate, Interest appreciation of the USD and depreciation of the IndianRate and Inflation Rate. In order to check the currency the perception of the investor towards stock stationarity of data the Augmented Dickey Fuller market is negative. However, the GDP when tested (ADF) test is preferred. individually showed a positive relation with Nifty. ThisThe second phase mainly checks the significance showed that all the variables are lined to each other and of these factors on NIFTY and also the relationship the movement is not caused by a single variable. However, IIP has showed a positive impact on between them, i.e. whether the relation is positive movement of Nifty.or negative. To check this multiple regression technique is been used. From the multiple Key Words:regression tests it has been found that except the Volatility, Macroeconomic Factors, Augmented inflation rate all the other independent factors Dickey Fuller Test, Multiple Regression have a positive or negative impact on NIFTY.
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