RESUMO
The paper models investor sentiments (IS) to attract investments for Health Sector and Growth in emerging markets, viz., India, Mainland China, and the UAE, by asking questions such as: What specific healthcare sector opportunities are available in the three markets? Are the USA-IS key IS predictors in the three economies? How important are macroeconomic and sociocultural factors in predicting IS in these markets? How important are economic crises and pandemic events in predicting IS in these markets? Is there contemporaneous relation in predicting IS across the three countries in terms of USA-IS, and, if yes, is the magnitude of the impact of USA-IS uniform across the three countries' IS? The artificial neural network (ANN) model is applied to weekly time-series data from January 2003 to December 2020 to capture behavioral elements in the investors' decision-making in these emerging economies. The empirical findings confirmed the superiority of the ANN framework over the traditional logistic model in capturing the cognitive behavior of investors. Health predictor-current health expenditure as a percentage of GDP, USA IS predictor-spread, and Macro-factor GDP-annual growth % are the common predictors across the 3 economies that positively impacted the emerging markets' IS behavior. USA (S&P 500) return is the only common predictor across the three economies that negatively impacted the emerging markets' IS behavior. However, the magnitude of both positive and negative impacts varies across the countries, signifying unique, diverse socioeconomic, cultural, and market features in each of the 3 economies. The results have four key implications: Firstly, US market sentiments are an essential factor influencing stock markets in these countries. Secondly, there is a need for developing a robust sentiment proxy on similar lines to the USA in the three countries. Thirdly, investment opportunities in the healthcare sector in these economies have been identified for potential investments by the investors. Fourthly, this study is the first study to investigate investors' sentiments in these three fast-emerging economies to attract investments in the Health Sector and Growth in the backdrop of UN's 2030 SDG 3 and SDG 8 targets to be achieved by these economies.
RESUMO
The early growth response 1 (Egr1) gene is a transcription factor that acts as both a tumor suppressor and a tumor promoter. Egr1-null mouse embryo fibroblasts bypass replicative senescence and exhibit a loss of DNA damage response and an apparent immortal growth, suggesting loss of p53 functions. Stringent expression analysis revealed 266 transcripts with >2-fold differential expression in Egr1-null mouse embryo fibroblasts, including 143 known genes. Of the 143 genes, program-assisted searching revealed 66 informative genes linked to Egr1. All 66 genes could be placed on a single regulatory network consisting of three branch points of known Egr1 target genes: TGFbeta1, IL6, and IGFI. Moreover, 19 additional genes that are known targets of p53 were identified, indicating that p53 is a fourth branch point. Electrophoretic mobility shift assay as well as chromatin immunoprecipitation confirmed that p53 is a direct target of Egr1. Because deficient p53 expression causes tumors in mice, we tested the role of Egr1 in a two-step skin carcinogenesis study (144 mice) that revealed a uniformly accelerated development of skin tumors in Egr1-null mice (P < 0.005). These studies reveal a new role for Egr1 as an in vivo tumor suppressor.
