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The neuroprotective effects of W. somnifera were studied on stressed adult female Swiss albino rats. Experimental rats were subjected to immobilization stress for 14 h and were treated with a root powder extract of W. somnifera available as Stresscom capsules (Dabur India Ltd). Control rats were maintained in completely, non stressed conditions. Thionin stained serial coronal sections (7 microm) of brain passing through the hippocampal region of stressed rats (E(1) group) demonstrated 85% degenerating cells (dark cells and pyknotic cells) in the CA(2) and CA(3) sub-areas. Treatment with W. somnifera root powder extract significantly reduced (80%) the number of degenerating cells in both the areas. The study thus demonstrates the antistress neuroprotective effects of W. somnifera.

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Automated defect detection in medical imaging has become the emergent field in several medical diagnostic applications. Automated detection of tumor in Magnetic Resonance Imaging (MRI) is very crucial as it provides information about abnormal tissues which is necessary for planning treatment. The conventional method for defect detection in magnetic resonance brain images is human inspection. This method is impractical for large amount of data. So, automated tumor detection methods are developed as it would save radiologist time. The MRI brain tumor detection is complicated task due to complexity and variance of tumors. In this paper, tumor is detected in brain MRI using machine learning algorithms. The proposed work is divided into three parts: preprocessing steps are applied on brain MRI images, texture features are extracted using Gray Level Co-occurrence Matrix (GLCM) and then classification is done using machine learning algorithm.

Industrialization, population burst, and changing lifestyles have resulted in the genesis of non-degradable pollutants languishing the environment and human health. Biological approaches using microorganisms are gaining importance as an eco-friendly and cost-effective substitute to mitigate the pollution load. Microorganisms can survive in a divergent environment and produce metabolites that can degrade and transform pollutants making it possible to revive contaminated sites naturally. Modern omics technologies like metagenomics, transcriptomics, proteomics, etc. have been used nowadays design strategies to study ecology and diversity of microorganisms and their application in environmental monitoring and bioremediation. The present article will focus on the omics techniques reportedly used in environmental monitoring to tackle the pollution load.

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