Parthenolide, a naturally occurring sesquiterpene lactone derived from feverfew (Tanacetum parthenium), exhibits exceptional anti-cancer and anti-inflammatory properties, making it a prominent candidate for further studies and drug development. In this review, we briefly investigate molecular events and cell-specific activities of this chemical in relation to cytochrome c, nuclear factor kappa-light-chain enhancer of activated B cells (NF-κB), signal transduction and activation of transcription (STAT), reactive oxygen species (ROS), TCP, HDACs, microtubules, and inflammasomes. This paper reports that parthenolide shows strong NF-κB- and STAT-inhibition-mediated transcriptional suppression of pro-apoptotic genes. This compound acts both at the transcriptional level and by direct inhibition of associated kinases (IKK-β). Similarly, this review discusses parthenolide-induced ROS-mediated apoptosis of tumor cells via the intrinsic apoptotic signaling pathway. The unique ability of this compound to not harm normal cells but at the same time induce sensitization to extrinsic as well as intrinsic apoptosis signaling in cancer cells provides an important, novel therapeutic strategy for treatment of cancer and inflammation-related disorders.

Mercury pollution has emerged as a major problem in industrialized zones and presents a serious threat to environment and health of local communities. Effectiveness and wide distribution of mer operon by horizontal and vertical gene transfer in its various forms among large community of microbe reflect importance and compatibility of this mechanism in nature. This review specifically describes mer operon and its generic molecular mechanism with reference to the central role played by merA gene and its related gene products. The combinatorial action of merA and merB together maintains broad spectrum mercury detoxification system for substantial detoxification of mercurial compounds. Feasibility of mer operon to coexist with antibiotic resistance gene (ampr, kanr, tetr) clusters enables extensive adaptation of bacterial species to adverse environment. Flexibility of the mer genes to exist as intricate part of chromosome, plasmids, transposons, and integrons enables high distribution of these genes in wider microbial gene pool. Unique ability of this system to manipulate oligodynamic property of mercurial compounds for volatilization of mercuric ions (Hg2+) makes it possible for a wide range of microbes to tolerate mercury-mediated toxicity.

Deep learning (DL), an emerging area of investigation in the fields of machine learning and artificial intelligence, has markedly advanced over the past years. DL techniques are being applied to assist medical professionals and researchers in improving clinical diagnosis, disease prediction and drug discovery. It is expected that DL will help to provide actionable knowledge from a variety of ‘big data’, including metabolomics data. In this review, we discuss the applicability of DL to metabolomics, while presenting and discussing several examples from recent research. We emphasize the use of DL in tackling bottlenecks in metabolomics data acquisition, processing, metabolite identification, as well as in metabolic phenotyping and biomarker discovery. Finally, we discuss how DL is used in genome-scale metabolic modelling and in interpretation of metabolomics data. The DL-based approaches discussed here may assist computational biologists with the integration, prediction and drawing of statistical inference about biological outcomes, based on metabolomics data.