RNAI MEDIATED GENE SILENCING OF EIF3A: A POSSIBLE SOLUTION TO CONTROL BREAST CANCER

Objective: The eukaryotic translational initiation factor 3A (eIF3A) is reported to be over expressed in most breast cancer cells. In the present study, our aim is to suppress the over expression of eIF3A in human breast cancer MCF-7 cell line using gene silencing technique (RNA interference (RNAi)).Methods: The artificial microRNA (amiRNA) targeting eIF3A gene was constructed by incorporating short interference RNA (siRNA) sequences against eIF3A gene into endogenous microRNA30 (miR-30) and cloned into pcDNA3.1 vector. The amiRNA containing plasmid was then transfected into MCF-7 cell line and the expression of eIF3A was examined by RT-PCR. The cytotoxicity of plasmid with amiRNA targeting eIF3A on MCF–7 cells was evaluated by MTT assay.Results: The amiRNA construct significantly inhibited eIF3A gene expression and reduce the cell viability of MCF-7 cell line.Conclusion: The usage of modified endogenous amiRNA in vector based expression system with significant gene silencing efficiency suggests that RNAi based gene silencing method can be considered as one of the effective means to control cancer.Â

Evidence-based national vaccine policy

India has over a century old tradition of development and production of vaccines. The Government rightly adopted self-sufficiency in vaccine production and self-reliance in vaccine technology as its policy objectives in 1986. However, in the absence of a full-fledged vaccine policy, there have been concerns related to demand and supply, manufacture vs. import, role of public and private sectors, choice of vaccines, new and combination vaccines, universal vs. selective vaccination, routine immunization vs. special drives, cost-benefit aspects, regulatory issues, logistics etc. The need for a comprehensive and evidence based vaccine policy that enables informed decisions on all these aspects from the public health point of view brought together doctors, scientists, policy analysts, lawyers and civil society representatives to formulate this policy paper for the consideration of the Government. This paper evolved out of the first ever ICMR-NISTADS national brainstorming workshop on vaccine policy held during 4-5 June, 2009 in New Delhi, and subsequent discussions over email for several weeks, before being adopted unanimously in the present form

Recursive Cluster Elimination Based Support Vector Machine for Disease State Prediction Using Resting State Functional and Effective Brain Connectivity

Brain state classification has been accomplished using features such as voxel intensities, derived from functional magnetic resonance imaging (fMRI) data, as inputs to efficient classifiers such as support vector machines (SVM) and is based on the spatial localization model of brain function. With the advent of the connectionist model of brain function, features from brain networks may provide increased discriminatory power for brain state classification.In this study, we introduce a novel framework where in both functional connectivity (FC) based on instantaneous temporal correlation and effective connectivity (EC) based on causal influence in brain networks are used as features in an SVM classifier. In order to derive those features, we adopt a novel approach recently introduced by us called correlation-purged Granger causality (CPGC) in order to obtain both FC and EC from fMRI data simultaneously without the instantaneous correlation contaminating Granger causality. In addition, statistical learning is accelerated and performance accuracy is enhanced by combining recursive cluster elimination (RCE) algorithm with the SVM classifier. We demonstrate the efficacy of the CPGC-based RCE-SVM approach using a specific instance of brain state classification exemplified by disease state prediction. Accordingly, we show that this approach is capable of predicting with 90.3% accuracy whether any given human subject was prenatally exposed to cocaine or not, even when no significant behavioral differences were found between exposed and healthy subjects.The framework adopted in this work is quite general in nature with prenatal cocaine exposure being only an illustrative example of the power of this approach. In any brain state classification approach using neuroimaging data, including the directional connectivity information may prove to be a performance enhancer. When brain state classification is used for disease state prediction, our approach may aid the clinicians in performing more accurate diagnosis of diseases in situations where in non-neuroimaging biomarkers may be unable to perform differential diagnosis with certainty

Determining the bubble nucleation efficiency of low-energy nuclear recoils in superheated C3_3F8_8 dark matter detectors

The bubble nucleation efficiency of low-energy nuclear recoils in superheated liquids plays a crucial role in interpreting results from direct searches for weakly interacting massive particle (WIMP) dark matter. The PICO Collaboration presents the results of the efficiencies for bubble nucleation from carbon and fluorine recoils in superheated C$_3$F$_8$ from calibration data taken with 5 distinct neutron spectra at various thermodynamic thresholds ranging from 2.1 keV to 3.9 keV. Instead of assuming any particular functional forms for the nuclear recoil efficiency, a generalized piecewise linear model is proposed with systematic errors included as nuisance parameters to minimize model-introduced uncertainties. A Markov-Chain Monte-Carlo (MCMC) routine is applied to sample the nuclear recoil efficiency for fluorine and carbon at 2.45 keV and 3.29 keV thermodynamic thresholds simultaneously. The nucleation efficiency for fluorine was found to be $\geq 50\, \%$ for nuclear recoils of 3.3 keV (3.7 keV) at a thermodynamic Seitz threshold of 2.45 keV (3.29 keV), and for carbon the efficiency was found to be $\geq 50\, \%$ for recoils of 10.6 keV (11.1 keV) at a threshold of 2.45 keV (3.29 keV). Simulated data sets are used to calculate a p-value for the fit, confirming that the model used is compatible with the data. The fit paradigm is also assessed for potential systematic biases, which although small, are corrected for. Additional steps are performed to calculate the expected interaction rates of WIMPs in the PICO-60 detector, a requirement for calculating WIMP exclusion limits.Comment: 17 pages, 22 figures, 5 table

Search for inelastic dark matter-nucleus scattering with the PICO-60 CF3_{3}I and C3_{3}F8_{8} bubble chambers

PICO bubble chambers have exceptional sensitivity to inelastic dark matter-nucleus interactions due to a combination of their extended nuclear recoil energy detection window from a few keV to $O$(100 keV) or more and the use of iodine as a heavy target. Inelastic dark matter-nucleus scattering is interesting for studying the properties of dark matter, where many theoretical scenarios have been developed. This study reports the results of a search for dark matter inelastic scattering with the PICO-60 bubble chambers. The analysis reported here comprises physics runs from PICO-60 bubble chambers using CF$_{3}$I and C$_{3}$F$_{8}$. The CF$_{3}$I run consisted of 36.8 kg of CF$_{3}$I reaching an exposure of 3415 kg-day operating at thermodynamic thresholds between 7 and 20 keV. The C$_{3}$F$_{8}$ runs consisted of 52 kg of C$_{3}$F$_{8}$ reaching exposures of 1404 kg-day and 1167 kg-day running at thermodynamic thresholds of 2.45 keV and 3.29 keV, respectively. The analysis disfavors various scenarios, in a wide region of parameter space, that provide a feasible explanation of the signal observed by DAMA, assuming an inelastic interaction, considering that the PICO CF$_{3}$I bubble chamber used iodine as the target material.Comment: 7 pages, 3 figure