Mortality profile and incidence of deaths due to neonatal sepsis in an urban tertiary care center in South India: A retrospective study

Background: The neonatal mortality rate is a key outcome indicator for newborn care and directly reflects prenatal, intrapartum, and neonatal care. Objective: Primary objective was to assess the neonatal mortality profile, incidence of neonatal sepsis among total neonatal deaths and pattern of antimicrobial resistance. Methodology: This was a retrospective descriptive study done at a tertiary care center. All neonatal deaths from January 2014 to December 2014 were reviewed, and primary causes of mortality, incidence of sepsis among neonatal deaths and pattern of antimicrobial resistance were analyzed. Results: Common causes of neonatal mortality were sepsis, respiratory distress syndrome, congenital malformations, asphyxia, extreme preterm, meconium aspiration syndrome. Case fatality rate was high in extreme preterm neonates (82%), followed by respiratory distress syndrome (29%), congenital malformations (29%), sepsis (25%), asphyxia (25%). In our study incidence of neonatal sepsis among total neonatal deaths was about 20.5%. Staphylococcus aureus (60%) and Klebsiella pneumoniae (23%) were predominant organisms. Highest case fatality rate was associated with K. pneumoniae sepsis about 60%, followed by Escherichia coli sepsis (54%) and Acinetobacter sepsis (50%). Multidrug resistance is an emerging problem, especially in Acinetobacter sepsis. Conclusion: Sepsis still remains the leading cause of death in developing countries. S. aureus was the most common predominant organism; of this, two-thirds were methicillin-resistant S. aureus. About 90% of K. pneumoniae were resistant to extended-spectrum cephalosporins. Multidrug resistance is an emerging problem, especially in Acinetobacter sepsis

Combination of Woody and Grass type Biomass: Waste Management, Influence of Process Parameters, Yield of Bio-oil by Pyrolysis and its Chromatographic Characterization

172-180Due to looming demand for fossil fuels and environmental concerns over global warming, extensive attention has been given on the development of renewable energy. Biomass materials are used since millennia for meeting myriad human needs including energy and chemicals. In this study, co-pyrolysis characteristics of woody and grass type agricultural wastes, namely Borassus flabellifer and Cymbopogon flexuosus were studied in a fixed bed reactor to evaluate their potential use as source of bio-oil. The effects of operating parameters such as temperature, particle size and heating rate were investigated. In this co-pyrolysis process, the maximum yield of pyrolysis bio-oil 47.10 wt% can been obtained under the pyrolysis temperature of 500ºC, 1.0 mm particle size and at the heating rate of 30 ºC/min. The bio-oil product was analyzed for physical, elemental and chemical composition using Fourier transform infra-red (FTIR) spectroscopy and gas chromatography (GC)

Frequency of polymorphic variants in corticotropin releasing hormone receptor 1, glucocorticoid induced 1 and Fc fragment of IgE receptor II genes in healthy and asthmatic Tamilian population

Background: Asthma is a chronic airway inflammatory disease characterized by increased hyper-responsiveness and recurrent episodes of reversible obstructions. Asthma pharmacogenomic studies report significant association of single nucleotide polymorphisms (SNPs) in genes corticotropin releasing hormone receptor 1 (CRHR1), Fc fragment of IgE receptor II (FCER2) and glucocorticoid induced 1 (GLCCI1) with inhaled corticosteroid (ICS) response. The present study was aimed to establish the allelic and genotypic frequencies of polymorphisms rs242941, rs28364072 & rs37972 in CRHR1, FCER2 and GLCCI1 genes, respectively in Tamilian healthy population and asthma patients and to compare with established frequencies of global populations.Methods: The study groups consisted of healthy volunteers and persistent asthma patients who were drug naïve or without ICS treatment in the last ≥2 months, attending JIPMER hospital (n=111 and 78, respectively). SNP genotyping was done using PCR-RFLP (polymerase chain reaction-restriction fragment length polymorphism) and real time-PCR methods.Results: Allelic and genotypic frequencies for all the studied variants found to be in hardy-weinberg equilibrium with minor allele frequencies (MAF) of rs 242941, rs 28364072 and rs 37972 at 0.51, 0.33 and 0.38, respectively, in healthy population. No significant difference in gene frequencies was obtained between healthy control and asthma patient groups. Significant difference in allele frequencies was observed between Tamilian healthy and specific global populations. West African frequency was found to be significantly different for all 3 SNPs (p<0.0001).Conclusions: MAF of rs 242941, rs 28364072 and rs 37972 were 0.51, 0.33 and 0.38, respectively in Tamilian population which were significantly different from various global populations. The frequency distribution found helps to further with ICS response association studies in larger cohorts of asthma patients

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

Improved control strategy of DFIG-based wind turbines using direct torque and direct power control techniques

This paper presents different control strategies for a variable-speed wind energy conversion system (WECS), based on a doubly fed induction generator. Direct Torque Control (DTC) with Space-Vector Modulation is used on the rotor side converter. This control method is known to reduce the fluctuations of the torque and flux at low speeds in contrast to the classical DTC, where the frequency of switching is uncontrollable. The reference for torque is obtained from the maximum power point tracking technique of the wind turbine. For the grid-side converter, a fuzzy direct power control is proposed for the control of the instantaneous active and reactive power. Simulation results of the WECS are presented to compare the performance of the proposed and classical control approaches.Peer reviewedFinal Accepted Versio

Track A Basic Science

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/138319/1/jia218438.pd

The United States COVID-19 Forecast Hub dataset

Academic researchers, government agencies, industry groups, and individuals have produced forecasts at an unprecedented scale during the COVID-19 pandemic. To leverage these forecasts, the United States Centers for Disease Control and Prevention (CDC) partnered with an academic research lab at the University of Massachusetts Amherst to create the US COVID-19 Forecast Hub. Launched in April 2020, the Forecast Hub is a dataset with point and probabilistic forecasts of incident cases, incident hospitalizations, incident deaths, and cumulative deaths due to COVID-19 at county, state, and national, levels in the United States. Included forecasts represent a variety of modeling approaches, data sources, and assumptions regarding the spread of COVID-19. The goal of this dataset is to establish a standardized and comparable set of short-term forecasts from modeling teams. These data can be used to develop ensemble models, communicate forecasts to the public, create visualizations, compare models, and inform policies regarding COVID-19 mitigation. These open-source data are available via download from GitHub, through an online API, and through R packages