Brijmohan Singh Bhau

Industrialization and urbanization increased the use of chemicals in agriculture, vehicular emissions, etc., and spoiled all environmental sectors. It causes various problems among living beings at multiple levels and concentrations. Phytoremediation and microbial association are emerging as a potential method for removing heavy metals and other contaminants from soil. The treatment uses plant physiology and metabolism to remove or clean up various soil contaminants efficiently. In recent years, omics and artificial intelligence have been seen as powerful techniques for phytobial remediation. Recently, AI and modeling are used to analyze large data generated by omics technologies. Machine learning algorithms can be used to develop predictive models that can help guide the selection of the most appropriate plant and plant growth–promoting rhizobacteria combination that is most effective at remediation. In this review, emphasis is given to the phytoremediation techniques being explored worldwide in soil contamination.

DNA barcoding is impending towards the generation of universal standards for species discrimination with a standard gene region that can be sequenced accurately and within short span of time. In this study, we were successful in developing efficient barcode locus in the Nepenthes genus. A total of 317 accessions were retrieved from GenBank of NCBI which represent 140 different species Nepenthes and evaluated the efficacy of ITS, rbcl and matK barcode candidates using barcode gap, applied distance similarity, and tree-based methods. Our result indicates that single-locus ITS or combined with plastid regions (matK) showed the best species discrimination with distinctive barcoding gaps. Therefore, we tentatively proposed the combination of ITS+matK as a core barcode for Nepenthes genus. This study provides a report on DNA barcoding for unique insectivores’ Nepenthes genus. As the different species of Nepenthes are higly endemic and endangered, it would be a useful study to understand the evolutionary relationship, sketched in emigration, mislabeling and can be a probable assessment for its biodiversity.

Plant-derived natural products (NPs) play a vital role in the discovery of new drug molecules and these are used for development of novel therapeutic drugs for a specific disease target. Literature review suggests that natural products possess strong inhibitory efficacy against various types of cancer cells. Clerodendrum indicum and Clerodendrum serratum are reported to have anticancer activity; therefore a study was carried out to identify selective anticancer agents from these plants species. In this report, we employed a docking weighted network pharmacological approach to understand the multi-therapeutics potentiality of C. indicum and C. serratum against various types of cancer. A library of 53 natural products derived from these plants was compiled from the literature and three dimensional space analyses were performed in order to establish the drug-likeness of the NPs library. Further, an NPs–cancer network was built based on docking. We predicted five compounds, namely apigenin 7-glucoside, hispidulin, scutellarein-7-O-beta-D-glucuronate, acteoside and verbascoside, to be potential binding therapeutics for cancer target proteins. Apigenin 7-glucoside and hispidulin were found to have maximum binding interactions (relationship) with 17 cancer drug targets in terms of docking weighted network pharmacological analysis. Hence, we used an integrative approach obtained from network pharmacology for identifying combinatorial drug actions against the cancer targets. We believe that our present study may provide important clues for finding novel drug inhibitors for cancer.