Benchmark data set for breast cancer associated genes.

Breast cancer is one of the leading causes of death in women worldwide. The main reason could be inheritance, change in environmental conditions or the mutation in certain genes that cause cancer. These genes are not negligible, on the contrary, a wide range of genes have their involvement in the development and progression of different stages of breast cancer. In this article, we are going to explore the association of breast cancer genes and classify them into different association classes viz. positive, negative and neutral. Among all the available biomedical literature resources for a disease, HuGE Navigator is a major resource comprising continually updated human genome epidemiology data controlled by the Centers for Disease Control and Prevention. However the literature finder module of HuGE Navigator only yields PubMed IDs for a specific disease, which are explored further to retrieve abstract data from PubMed. These abstracts are filtered out to include those reference sentences which have at least one gene and disease term. This reference sentence data has been taken as a reference to apply double-fold cross-validation to compile the most comprehensive list and then classify them into different association classes viz, positive, negative or neutral along with the reference sentences confirming the association of the disease with the gene. The positively associated data generated here can be used for breast cancer modelling or meta-analysis of breast cancer. The data generated in the present work can be used as standard reference data for the training of text mining-based biological literature classifiers to predict the class of published literature not only in breast cancer but in other diseases as well.

Drug-food Interactions in the Era of Molecular Big Data, Machine Intelligence, and Personalized Health.

The drug-food interaction brings forth changes in the clinical effects of drugs. While favourable interactions bring positive clinical outcomes, unfavourable interactions may lead to toxicity. This article reviews the impact of food intake on drug-food interactions, the clinical effects of drugs, and the effect of drug-food in correlation with diet and precision medicine. Emerging areas in drug-food interactions are the food-genome interface (nutrigenomics) and nutrigenetics. Understanding the molecular basis of food ingredients, including genomic sequencing and pharmacological implications of food molecules, helps to reduce the impact of drug-food interactions. Various strategies are being leveraged to alleviate drug-food interactions; measures including patient engagement, digital health, approaches involving machine intelligence, and big data are a few of them. Furthermore, delineating the molecular communications across dietmicrobiome- drug-food-drug interactions in a pharmacomicrobiome framework may also play a vital role in personalized nutrition. Determining nutrient-gene interactions aids in making nutrition deeply personalized and helps mitigate unwanted drug-food interactions, chronic diseases, and adverse events from their onset. Translational bioinformatics approaches could play an essential role in the next generation of drug-food interaction research. In this landscape review, we discuss important tools, databases, and approaches along with key challenges and opportunities in drug-food interaction and its immediate impact on precision medicine.

Functionalized Phosphorene Quantum Dots as Efficient Electrocatalyst for Oxygen Evolution Reaction.

Phosphorene has attracted great interest in the rapidly emerging field of two-dimensional layered nanomaterials. Recent studies show promising electrocatalytic activity of few-layered phosphorene sheets toward the oxygen evolution reaction (OER). However, controllable synthesis of mono/few-layered phosphorene nanostructures with a large number of electrocatalytically active sites and exposed surface area is important to achieve significant enhancement in OER activity. Here, a novel strategy for controlled synthesis and in situ surface functionalization of phosphorene quantum dots (PQDs) using a single-step electrochemical exfoliation process is demonstrated. Phosphorene quantum dots functionalized with nitrogen-containing groups (FPQDs) exhibit efficient and stable electrocatalytic activity for OER with an overpotential of 1.66 V @ 10 mA cm-2, a low Tafel slope of 48 mV dec-1, and excellent stability. Further, we observe enhanced electron transfer kinetics for FPQDs toward the Fe2+/Fe3+ redox probe in comparison with pristine PQDs. The results demonstrate the promising potential of phosphorene as technologically viable OER electrodes for water-splitting devices.

A Single-Step Electrochemical Synthesis of Luminescent WS2 Quantum Dots.

Transition-metal dichalcogenide quantum dots (TMDQDs) with few layers are in the forefront of recent research on tailored 2D layered materials owing to their unique band structure. Such quantum dots (QDs) draw wide interest as potential candidates for components in optoelectronic devices. Although a few attempts towards single step synthesis of MoS2 QDs have been demonstrated, limited methods are available for WS2 QDs. Herein, we demonstrate a one-step electrochemical synthesis of luminescent WS2 QDs from their bulk material. This is achieved by a synergistic effect of perchlorate intercalation in non-aqueous electrolyte and the applied electric field. The average size of the WS2 QDs is 3  ±1 nm (N=102) with few layers. The QDs show a higher photoluminescence (PL) quantum efficiency (5 %) and exhibit an excitation wavelength-dependent photoluminescence. This unprecedented electrochemical avenue offers a strategy to synthesize size tunable WS2 nanostructures, which have been systematically investigated by various characterization techniques such as transmission electron microscopy (TEM), photoluminescence and UV/Vis spectroscopies, and X-ray diffraction (XRD). Time-dependent TEM investigations revealed that time plays a vital role in this electrochemical transformation. This electrochemical transformation provides a facile method to obtain WS2 QDs from their bulk counterpart, which is expected to have a greater impact on the design and development of nanostructures derived from 2D materials.