Institutional quality and foreign direct investment inflows : evidence from cross-country data with policy implication

Purpose: The study examines the impact of institutional quality on Foreign Direct Investment (FDI) inflows for emerging economies from South Asiain the period 2002-2016. Other economic factors such as globalisation, financial development, and GDP are also considered. Design/Methodology/Approach: The study uses Im-Pesaran-Shin (IPS) panel unit root test to check stationarity property. It uses cross dependency (CD) and cross-sectional augments IPS tests to check cross-sectional dependency and heterogeneity across the group countries. Next, it uses panel ARDL-PMG tests to check the existence of long-relationship among variables. Then, we apply the panel Granger causality test to check the direction of causality. Finally, for the robustness of results, we use the Pedroni co-integration technique. Findings: The study finds the existence of a long-run relationship between institutional quality and FDI inflows. Other economic factors such as globalization and financial development show long-run and strong causality with FDI inflows. However, the short-run unidirectional causality from institutional quality to FDI inflows is not found for all the countries. Finally, institutional quality strongly causes FDI inflows provided paired with either globalisation or financial development. Practical Implications: Institutional quality increases the FDI inflows. Therefore, policymakers should focus on institutional quality along with globalization and financial development for higher inflows of FDI in emerging countries. Originality/Value: The study considers institutional quality as one of the inputs for FDI inflows in selected emerging economies from South Asia. Further, it creates an institutional quality index for the emerging countries to examine the impact on FDI inflows.peer-reviewe

Prevalence and determinants of unprotected sex in intimate partnerships of men who inject drugs: findings from a prospective intervention study

Unprotected sex, common among people who inject drugs, puts them and their partners at risk of sexually transmitted infections including human immunodeficiency virus (HIV). This analysis assesses the changes in sexual risk behavior with regular female partners (RFPs), among married men who inject drugs, before and after implementation of a HIV prevention intervention, and identifies correlates of unprotected sex. People who inject drugs (PWID) were assessed at three points: baseline, preintervention follow-up visit (FV)1, and postintervention FV2. Descriptive analysis was used for reporting changes in sexual behavior over time. Generalized estimating equation assessed the population-averaged change in self-reported unprotected sex with an RFP, attributable to intervention uptake. Multivariable logistic regression determined correlates of self-reported unprotected sex with an RFP at FV2. Findings suggest that the proportion of men reporting any unprotected sex remained high (baseline = 46.0%, FV1 = 43.5%, FV2 = 37.0%). A reduction was observed in unprotected sex after the intervention phase, but this could not be attributed to uptake of the intervention. Higher odds of self-reported unprotected sex with an RFP in the past three months at FV2 were associated with self-reported unprotected sex at baseline, living with family, and being HIV-negative. Married male PWID should receive counseling for safe sex with RFPs, especially those who are HIV-negative and live with their families

The prospects of microphysiological systems in modeling platelet pathophysiology in cancer

The contribution of platelets is well recognized in thrombosis and hemostasis. However, platelets also promote tumor progression and metastasis through their crosstalk with various cells of the tumor microenvironment (TME). For example, several cancer models continue to show that platelet functions are readily altered by cancer cells upon activation leading to the formation of platelet-tumor aggregates, triggering release of soluble factors from platelet granules and altering platelet turnover. Further, activated platelets protect tumor cells from shear forces in circulation and assault of cytotoxic natural killer (NK) cells. Platelet-secreted factors promote proliferation of malignant cells, metastasis, and chemoresistance. Much of our knowledge of platelet biology in cancer has been achieved with animal models, particularly murine. However, this preclinical understanding of the complex pathophysiology is yet to be fully realized and translated to clinical trials in terms of new approaches to treat cancer via controlling the platelet function. In this review, we summarize the current state of knowledge of platelet physiology obtained through existing in vivo and in vitro cancer models, the complex interactions of platelets with cancer cells in TME and the pathways by which platelets may confer chemoresistance. Since the FDA Modernization Act recently passed by the US government has made animal models optional in drug approvals, we critically examine the existing and futuristic value of employing bioengineered microphysiological systems and organ-chips to understand the mechanistic role of platelets in cancer metastasis and exploring novel therapeutic targets for cancer prevention and treatment

Effect of oral administration of Bacillus coagulans B37 and Bacillus pumilus B9 strains on fecal coliforms, Lactobacillus and Bacillus spp. in rat animal model

Aim: To investigate the effect of oral administration of two Bacillus strains on fecal coliforms, Lactobacillus and Bacillus spp. in rat animal model. Materials and Methods: An in vivo experiment was conducted for 49-day period on 36 adult male albino Wister rats divided equally into to four groups. After 7-day adaptation period, one group (T1) was fed on sterile skim milk along with basal diet for the next 28 days. Second (T2) and (T3) groups received spore biomass of Bacillus coagulans B37 and Bacillus pumilus B9, respectively, suspended in sterilized skim milk at 8-9 log colony-forming units/ml plus basal diet for 28 days, while control group (T4) was supplied with clean water along with basal diet. There was a 14-day post-treatment period. A total of 288 fecal samples (8 fecal collections per rat) were collected at every 7-day interval starting from 0 to 49 days and subjected to the enumeration of the counts of coliforms and lactobacilli and Bacillus spores using respective agar media. In vitro acid and bile tolerance tests on both the strains were performed. Results: The rats those (T2 and T3) received either B. coagulans B37 or B. pumilus B9 spore along with non-fermented skim milk showed decrease (p<0.01) in fecal coliform counts and increase (p<0.05) in both fecal lactobacilli and Bacillus spore counts as compared to the control group (T4) and the group fed only skim milk (T1). In vitro study indicated that both the strains were found to survive at pH 2.0 and 3.0 even up to 3 h and tolerate bile up to 2.0% concentration even after 12 h of exposure. Conclusions: This study revealed that oral administration of either B. coagulans B37 or B. pumilus B9 strains might be useful in reducing coliform counts accompanied by concurrent increase in lactobacilli counts in the intestinal flora in rats

Synchronicity Identification in Hippocampal Neurons using Artificial Neural Network assisted Fuzzy C-means Clustering

Neural synchronicity plays a vital role in monitoring the functions that are cognitive. Any disturbance identified in the neural synchrony might lead to a diseased state. In the case of in vitro cell recordings, the neurons demonstrate significant heterogeneity in the firing pattern. Thus, the task of automated identification of synchronous and asynchronous neurons from a large population of neuronal cells remains challenging. To address this issue, an efficient unsupervised machine learning approach has been proposed for a system of primary cultures of hippocampal neurons. Here, a confocal microscope is used for imaging of intracellular calcium using Fluo-4 as the fluorescent indicator. The obtained static images are transformed into time-varying data of cytosolic calcium. Subsequently, an intelligent artificial neural network (ANN) assisted fuzzy clustering algorithm is proposed for grouping the synchronous neurons from a heterogeneous set of calcium data that are spiking in nature. This novel algorithm enables a drastic variable reduction followed by the implementation of a global optimization algorithm to solve the problem in Fuzzy C-means (FCM) clustering. Additionally, the proposed technique computes the optimal cluster number and the hyper-parameters involved in ANNs. To validate the result obtained from ANN assisted FCM, a correlation coefficient, and a spiking pattern plot is analyzed for both the synchronous and asynchronous neuronal cells. Besides this, the proposed algorithm is compared with the traditional FCM, where the solution quality is found to be improved along-with an 88% reduction in decision variable count. The complete novel framework combines the aspects of calcium imaging, ANN-assisted FCM, validation, and comparison, which as a whole, can be used for quick and effective quantification of synchronicity

Smart Data Analytics approach to model Complex Biochemical Oscillations in Hippocampal Neurons

Calcium spiking can be used for drug screening studies in pharmaceutical industries. However, performing experiments for multiple drugs and doses are highly expensive. The oscillatory behavior of calcium spiking data demonstrates extreme nonlinearity and phase singularity. This makes it more challenging to construct physics-based models for the experimental observations. In this scenario, data based modelling, such as Artificial Neural Networks (ANN), and thereafter the model based prediction of calcium profiles may offer a cost-effective and time saving solution. Therefore, a novel ANN building algorithm is presented in the current work, where data based simultaneous estimation of ANN architecture and nonlinear activation function stands out as the main highlight. The resultant ANN was then used to learn the oscillatory behavior in calcium ion concentration data, obtained from hippocampal neurons of rats by fluorescent labelling and confocal imaging. The paper shows that the novel technique can be used in general for emulating biochemical oscillations (with or without drug injection) and can be implemented to predict the cell-drug responses for intermediated doses. The proposed algorithm can also be used for obtaining high resolution data from low resolution experimental measurements

Comparison of Calcium Dynamics and Specific Features for G Protein-Coupled Receptor-Targeting Drugs Using Live Cell Imaging and Automated Analysis

G protein-coupled receptors (GPCRs) are targets for designing a large fraction of the drugs in the pharmaceutical industry. For GPCR-targeting drug screening using cell-based assays, measurement of cytosolic calcium has been widely used to obtain dose-response profiles. However, it remains challenging to obtain drug-specific features due to cell-to-cell heterogeneity in drug-cell responses obtained from live cell imaging. Here, we present a framework combining live cell imaging of a cell population and a feature extraction method for classification of responses of drugs targeting GPCRs CXCR4 and α2AR. We measured the calcium dynamics using confocal microscopy and compared the responses for SDF-1α and norepinephrine. The results clearly show that the clustering patterns of responses for the two GPCRs are significantly different. Additionally, we show that different drugs targeting the same GPCR induce different calcium response signatures. We also implemented principal component analysis and k means for feature extraction and used nondominated (ND) sorting for ranking a group of drugs at various doses. The presented approach can be used to model a cell population as a mixture of subpopulations. It also offers specific advantages, such as higher spatial resolution, classification of responses, and ranking of drugs, potentially providing a platform for high-content drug screening