Species belonging to the genus of Pseudomonas are known to possess metabolic versatility and are capable of adapting to various environments. One such strain BG, possessing urease activity, was isolated from marine water from the Gulf of Khambhat, Gujarat. 16S rRNA gene sequence analysis was performed to identify the isolate, which showed that it was the closest neighbor to be Pseudomonas aeruginosa strain BS8. Strain BG was accessed (meant to be assessed?) for multiple plant growth promoting and biocontrol traits. It tested positive for indole-3-acetic acid (IAA) production with 19 mg ml -1 of IAA yield, phosphate solubilization with 13 mg ml -1 solubilization of tri-calcium-phosphate and it showed maximum of 27 mg ml -1 of ammonia production. Further, BG isolate could produce hydrocyanic acid, siderophore, catalase and showed growth inhibition of Aspergillus flavus and Fusarium oxysporum f. sp. cubense confirming its potential antifungal activity. The marine isolate also showed 17.92 ± 1.79 unit min -1 ml -1 urease activity, and further, the presence of genes responsible for urease enzyme in strain BG was identified by amplification using gene specific primers. Talc based biofertilizer using strain BG was prepared and tested on seedling growth of Chickpea, where biofertilizer treated seeds showed enhanced growth. Thus, it was concluded that marine P. aeruginosa BG showed plant growth promotion and biocontrol abilities along with urease activity.

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Indole is known as a versatile heterocyclic building block for its multiple pharmacological activities and has a high probability of success in the race for drug candidates. Many natural products, alkaloids, and bioactive heterocycles contain indole as the active principle pharmacophore. These encourage the researchers to explore it as a lead in the drug development process. The current manuscript will serve as a torchbearer for understanding the structurally diverse class of indole derivatives with extensive pharmacological activity. The current manuscript describes the intermediates and their functional groups responsible for superior biological activity compared to the standard. The review is written to help researchers to choose leads against their target but also to provide crucial insight into the design of a hybrid pharmacophore-based approach in drug design with enhanced potential. The present reviews on the indole derivatives correlate the structures with biological activities as well as essential pharmacophores, which were highlighted. The discussion was explored under challenging targets like dengue, chikungunya (anti-viral), antihypertensive, diuretic, immunomodulator, CNS stimulant, antihyperlipidemic, antiarrhythmic, anti-Alzheimer’s, and neuroprotective, along with anticancer, antitubercular, antimicrobial, anti-HIV, antimalarial, anti-inflammatory, antileishmanial, anti-anthelmintic, and enzyme inhibitors. So, this review includes a discussion of 19 different pharmacological targets for indole derivatives that could be utilized to derive extensive information needed for ligand-based drug design. The article will guide the researchers in the selection, design of lead and pharmacophore, and ligand-based drug design using indole moiety.

Agriculture plays an essential role in the economies of developing countries such as India and contributes significantly to the gross domestic product (GDP). The escalation in population has led to an upsurge in food demand. Numerous challenges such as the selection of crops, fertilizers, and pesticides without considering the various parameters like types of soil, water requirement, temperature conditions, and profitability analysis of crops for a particular region may lead to degradation in the quality of crop, yield and profitability. With the advancement of Computational technologies, researchers are working on recommending crops according to soil condition, water requirement, and market profitability along with fertilizers recommendation, disease identification, and pesticide recommendation. Through this research, we propose a machine learning-based crop and fertilizer recommendation algorithm called AgriRec. We have utilized soil properties, water level, farm size, and minimum support price of crop and design a machine learning model which predicts crops for different seasons. Further, we propose another mechanism that processes the properties/details of soil, crop, and fertilizer to envisage a combination of fertilizer(s) for a given pair of soil and crop. Our algorithm is tested for 5000 land samples of Gujarat region with 24 different crop and it successfully recommends crops with 95.85% accuracy and fertilizer with 92.11% accuracy with 4 times better performance as compared to existing benchmark recommendation approaches.