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Lucknow, India

Integral University is a State Private University in Lucknow, the capital of Uttar Pradesh, India, which was established as a university from Institute of Integral Technology, Lucknow under Act Number 9 of 2004 by the Uttar Pradesh state government. The Institute of Integral Technology, Lucknow was established in 1998 by the then Prime Minister of India Mr. Atal Bihari Vajpayee. Wikipedia.


Singh R.,University of Lucknow | Rastogi S.,Integral University | Dwivedi U.N.,University of Lucknow
Comprehensive Reviews in Food Science and Food Safety | Year: 2010

Ripening of fleshy fruit is a differentiation process involving biochemical and biophysical changes that lead to the accumulation of sugars and subsequent changes in tissue texture. Also affected are phenolic compounds, which confer color, flavor/aroma, and resistance to pathogen invasion and adverse environmental conditions. These phenolic compounds, which are the products of branches of the phenylpropanoid pathway, appear to be closely linked to fruit ripening processes. Three key enzymes of the phenylpropanoid pathway, namely phenylalanine ammonia lyase, O-methyltransferase, and cinnamyl alcohol dehydrogenase (CAD) have been reported to modulate various end products including lignin and protect plants against adverse conditions. In addition, peroxidase, the enzyme following CAD in the phenylpropanoid pathway, has also been associated with injury, wound repair, and disease resistance. However, the role of these enzymes in fruit ripening is a matter of only recent investigation and information is lacking on the relationships between phenylpropanoid metabolism and fruit ripening processes. Understanding the role of these enzymes in fruit ripening and their manipulation may possibly be valuable for delineating the regulatory network that controls the expression of ripening genes in fruit. This review elucidates the functional characterization of these key phenylpropanoid biosynthetic enzymes/genes during fruit ripening processes. © 2010 Institute of Food Technologists®. Source


Kushwaha P.,Integral University
International Journal of Pharmaceutical Research | Year: 2010

Numerous solvents of diverse nature are frequently utilized in a variety of food manufacturing processes. Fats, oils, grains, coffee and tea are some of the food products in which various solvents are used for the extraction procedure. The quality and stability of these food products can be affected by the presence of residual solvents that remain after extraction procedure. Expression and solvent extraction are the two commonly employed methods for isolation of oils from oil bearing seeds. Latter method is more extensively used due its higher yield and cost benefit. Solvent extraction method requires solvent for the extraction procedure. The complete removal of residual levels of these solvents is impracticable and traces always remain in the final products. The presence of these residual solvents even in small amounts has a negative influence not only on the quality of food products but also on human health. ICH and various pharmacopoeias have published guidelines on the acceptable level of residual solvents. These guidelines prescribe their maximum permissible limit in the product. Currently food companies have adopted methodologies that will reduce the levels of solvent residues. Since, most of the solvents employed for extraction of edible oils are known for varying degree of toxicity, therefore, it will be highly desirable to reserve fixed oils obtained either by expression or by supercritical fluid technology for edible use and solvent extracted fixed oils for exclusive non-edible use. Such an approach will not only be industrially viable but also ensure safety of public health. Source


Kuddus M.,Integral University | Kuddus M.,Shaqra University | Ramteke P.W.,SHIATS
Critical Reviews in Microbiology | Year: 2012

Microbial proteases that occupy a pivotal position with respect to their commercial applications are most important hydrolytic enzymes and have been studied extensively since the advent of enzymology. Cold-adapted microorganisms are potential source of cold-active proteases and they have been isolated from the cold regions. Although there are many microbial sources available for producing proteases, only few are recognized as commercial producer. Cold-active proteases along with their producing microbes are of commercial value and find multiple applications in various industrial and biotechnological sectors such as additives in detergents, additives in food industries, environmental bioremediations, biotransformation and molecular biology applications. Therefore, cold-active proteases are the enzymes of choice for many biotechnologists, microbiologists, biochemists, environmentalists and biochemical engineers. In the present review, we discuss some novel sources along with recent developments in production and biotechnological applications of cold-active microbial proteases. © 2012 Informa Healthcare USA, Inc. Source


Hussain M.S.,Integral University
African Journal of Traditional, Complementary and Alternative Medicines | Year: 2011

Many people have the mistaken notion that, being natural, all herbs and foods are safe; this is not so. Very often, herbs and food may interact with medications you normally take, result in serious reactions. During the latter part of this century the practice of herbalism has become mainstream throughout the world. This is due remove to the recognition of the value of traditional medical systems in the world. Herbal medicines are mixtures of more than one active ingredient. The multitude of pharmacologically active compounds obviously increases the likelihood of interactions taking place. Hence, the likelihood of herb-drug interactions is theoretically higher than drug-drug interactions because synthetic drugs usually contain single chemical entity. Case reports and clinical studies have highlighted the existence of a number of clinically important interactions, although cause-and-effect relationships have not always been established. Herbs and drugs may interact either pharmacokinetically or pharmacodynamically. The predominant mechanism for this interaction is the inhibition of cytochrome P-450 3A4 in the small intestine; result in a significant reduction of drug presystemic metabolism. An additional mechanism is the inhibition of Pglycoprotein, a transporter that carries drug from the enterocyte back to the gut lumen, result in a further increase in the fraction of drug absorbed. Some herbal products (e.g. St. John's wort) have been shown to lower the plasma concentration (and/or the pharmacological effect) of a number of conventional drugs including cyclosporine, indinavir, irinotecan, nevirapine, oral contraceptives and digoxin. The data available so far, concerning this interaction and its clinical implications are reviewed in this article. It is likely that more information regarding such interaction would crop up in the future, awareness of which is necessary for achieving optimal drug therapy. © African Journal of Traditional, Complementary and Alternative Medicines. Source


Sarangi A.N.,Sanjay Gandhi Post Graduate Institute of Medical Sciences | Lohani M.,Integral University | Aggarwal R.,Sanjay Gandhi Post Graduate Institute of Medical Sciences
Protein and Peptide Letters | Year: 2013

Prediction of essential proteins of a pathogenic organism is the key for the potential drug target identification, because inhibition of these would be fatal for the pathogen. Identification of these proteins requires the use of complex experimental techniques which are quite expensive and time consuming. We implemented Support Vector Machine algorithm to develop a classifier model for in silico prediction of prokaryotic essential proteins based on the physico-chemical properties of the amino acid sequences. This classifier was designed based on a set of 10 physico-chemical descriptor vectors (DVs) and 4 hybrid DVs calculated from amino acid sequences using PROFEAT and PseAAC servers. The classifier was trained using data sets consisting of 500 known essential and 500 non-essential proteins (n=1,000) and evaluated using an external validation set consisting of 3,462 essential proteins and 5,538 non-essential proteins (n=9,000). The performances of individual DV sets were evaluated. DV set 13, which is the combination of composition, transition and distribution descriptor set and hybrid autocorrelation descriptor set, provided accuracy of 91.2% in 10-fold cross-validation of the training set and an accuracy of 89.7% in external validation set and of 91.8% and 88.1% using a different yeast protein dataset. Our result indicates that this classification model can be used for identification of novel prokaryotic essential proteins. © 2013 Bentham Science Publishers. Source

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