A Validated RP-HPLC Method Development for Amoxicillin in Pharmaceutical Dosage Forms

A rapid and simple Reverse Phase High Performance Liquid Chromatography (RP-HPLC) method has been developed for the quantification of Amoxicillin in tablet dosage form. Separation was achieved on Chromatopak-C18 (250mm×4.6×5micron) column in isocratic mode with mobile phase consisting of Acetonitrile: 0.2M Potassium dihydrogen phosphate buffer (pH 3) (22:78v/v) and conditions optimized with flow rate of 1 ml/minute and wavelength of detection at 283 nm. The retention time of Amoxicillin was found to be 6.4 min. Linearity was established for Amoxicillin in the range 10 100 μ g / ml with R2 value 0.999. This method was validated in accordance with ICH guidelines, the linearity, accuracy, precision, specificity, robustness, ruggedness, and system suitability results were within the acceptance criteria. Validation studies demonstrated that the proposed RP-HPLC method is simple, specific, rapid, reliable and reproducible for the determination of Amoxicillin for Quality Control level

Crystal structure of 1-methanesulfonyl-1, 2, 3, 4-tetrahydroquinoline

SJ thanks Vision Group on Science and Technology, Government of Karnataka, for awarding a major project under CISE scheme (reference No. VGST/CISE/GRD-192/ 2013–14). BSP thanks Rajegowda, Department of Studies and Research in Physics, UCS, Tumkur University, Karnataka 572103, India, for his support.Peer reviewedPublisher PD

Medical disease prediction using Grey Wolf optimization and auto encoder based recurrent neural network

Big data development in biomedical and medical service networks provides a research on medical data benefits, early ailment detection, patient care and network administrations.e-Health applications are particularly important for the patients who are unfit to see a specialist or any health expert. The objective is to encourage clinicians and families to predict disease using Machine Learning (ML) procedures. In addition, diverse regions show important qualities of certain provincial ailments, which may hinder the forecast of disease outbreaks. The objective of this work is to predict the different kinds of diseases using Grey Wolf optimization and auto encoder based Recurrent Neural Network (GWO+RNN). The features are selected using GWO and the diseases are predicted by using RNN method. Initially the GWO algorithm avoids the irrelevant and redundant attributes significantly, after the features are forwarded to the RNN classifier. The experimental result proved that the performance of GWO+RNN algorithm achieved better than existing method like Group Search Optimizer and Fuzzy Min-Max Neural Network (GFMMNN) approach. The GWO-RNN method used the medical UCI database based on various datasets such as Hungarian, Cleveland, PID, mammographic masses, Switzerland and performance was measured with the help of efficient metrics like accuracy, sensitivity and specificity. The proposed GWO+RNN method achieved 16.82% of improved prediction accuracy for Cleveland dataset

Data science: Identifying influencers in social networks

Data science is a "concept to unify statistics, data analysis and their related methods" in order to "understand and analyze actual phenomena" with data. The common use of Online Social Networks (OSN)[2] for networking communication which authorizes real-time multimedia capturing and sharing, have led to enormous amounts of user-generated content in online, and made publicly available for analysis and mining. The efforts have been made for more privacy awareness to protect personal data against privacy threats. The principal idea in designing different marketing strategies is to identify the influencers in the network communication. The individuals influential induce “word-of-mouth” that effects in the network are responsible for causing particular action of influence that convinces their peers (followers) to perform a similar action in buying a product. Targeting these influencers usually leads to a vast spread of the information across the network. Hence it is important to identify such individuals in a network, we use centrality measures to identify assign an influence score to each user. The user with higher score is considered as a better influencer

Environmental Response and Genomic Regions Correlated with Rice Root Growth and Yield under Drought in the OryzaSNP Panel across Multiple Study Systems

The rapid progress in rice genotyping must be matched by advances in phenotyping. A better understanding of genetic variation in rice for drought response, root traits, and practical methods for studying them are needed. In this study, the OryzaSNP set (20 diverse genotypes that have been genotyped for SNP markers) was phenotyped in a range of field and container studies to study the diversity of rice root growth and response to drought. Of the root traits measured across more than 20 root experiments, root dry weight showed the most stable genotypic performance across studies. The environment (E) component had the strongest effect on yield and root traits. We identified genomic regions correlated with root dry weight, percent deep roots, maximum root depth, and grain yield based on a correlation analysis with the phenotypes and aus, indica, or japonica introgression regions using the SNP data. Two genomic regions were identified as hot spots in which root traits and grain yield were co-located; on chromosome 1 (39.7–40.7 Mb) and on chromosome 8 (20.3–21.9 Mb). Across experiments, the soil type/ growth medium showed more correlations with plant growth than the container dimensions. Although the correlations among studies and genetic co-location of root traits from a range of study systems points to their potential utility to represent responses in field studies, the best correlations were observed when the two setups had some similar properties. Due to the co-location of the identified genomic regions (from introgression block analysis) with QTL for a number of previously reported root and drought traits, these regions are good candidates for detailed characterization to contribute to understanding rice improvement for response to drought. This study also highlights the utility of characterizing a small set of 20 genotypes for root growth, drought response, and related genomic regions

Reductive etherification of carbonyl compounds with alkyl trimethylsilylethers using polymethylhydrosiloxane (PMHS) and catalytic B(C<SUB>6</SUB>F<SUB>5</SUB>)<SUB>3</SUB>

A facile synthesis of symmetrical and unsymmetrical ethers is achieved by reductive coupling of carbonyl compounds with alkoxysilanes. This reaction is performed using inert polymethylhydrosiloxane as the hydride source and B(C6F5)3 as the catalytic activator of the PMHS. A facile synthesis of symmetrical and unsymmetrical ethers is achieved by reductive coupling of carbonyl compounds with alkoxysilanes. This reaction is performed using inert polymethylhydrosiloxane as the hydride source and B(C6F5)3 as the catalytic activator of the PMHS