People to policy: The promise and challenges of big data for India

Big data holds great promise to help unravel insights to bridge the gap in human understanding. There has to be an emphasis on the quality of the data points being collected to ensure meaningful analysis. India has made significant strides to lay down a strong framework through the National Digital Health Blueprint and the National Health Stack for the future. There is a need to focus on the first important step of collection of a “good quality” data point through the implementation of electronic medical records by the health care providers. In India, 60 million individuals move below the poverty line every year because of the expenses related to unforeseen illness that adversely affects the individual''s welfare and the nation''s economic growth. With an out-of-pocket expense rate currently at 70% and the government''s health budget at a mere 1.3% of its GDP (gross domestic product), data-driven decisions are the need of the hour for policy making and to ensure equitable, efficient, and excellent delivery of health care. There is a huge potential to harness the power of big data to generate insights to address the four big challenges of health care in India – availability, accessibility, affordability, and acceptability.     

Live single cell mass spectrometry reveals cancer‐specific metabolic profiles of circulating tumor cells

Recently, there has been increased attention on the analysis of circulating tumor cells (CTCs), also known as liquid biopsy, owing to its potential benefits in cancer diagnosis and treatment. Circulating tumor cells are released from primary tumor lesions into the blood stream and eventually metastasize to distant body organs. However, a major hurdle with CTC analysis is their natural scarcity. Existing methods lack sensitivity, specificity, or reproducibility required in CTC characterization and detection. Here, we report untargeted molecular profiling of single CTCs obtained from gastric cancer and colorectal cancer patients, using live single cell mass spectrometry integrated with microfluidics‐based cell enrichment techniques. Using this approach, we showed the difference in the metabolomic profile between CTCs originating from different cancer groups. Moreover, potential biomarkers were putatively annotated to be specific to each cancer type.     

Pharmacological studies on seeds of Alangium salvifolium Linn

The seeds of Alangium salvifolium Linn. have been traditionally reported to exhibit a variety of biological activities, including antidiabetic, anticancer, diuretic, anti-inflammatory, antimicrobial, laxative, and antiepileptic activities. The objective of this study was to verify the traditional claims and to evaluate the seeds of Alangium salvifolium in various organic extracts to screen the antidiabetic, antiepileptic, analgesic and antiinflammatory activities. The chloroform, ethanol, and water extracts of Alangium salvifolium seeds were obtained and subjected for phytochemical screening and evaluated for their pharmacological activities. From the acute toxicity study it was observed that chloroform, ethanol, and aqueous extracts of Alangium salvifolium seeds are non-toxic at a fixed dose of 2000 mg/kg. Among all three extracts ethanol extracts exhibited significant (p < 0.01) antidiabetic, antiepileptic, analgesic and anti-inflammatory activities. The phytochemical analysis revealed the presence of alkaloids, glycosides, terpenoids, steroids and tannins. The results of present study verified the traditional claims made by ayurvedic practitioner. However, the chemical constituents responsible for the pharmacological activities remain to be investigated.     

Putting genetic interactions in context through a global modular decomposition

Genetic interactions provide a powerful perspective into gene function, but our knowledge of the specific mechanisms that give rise to these interactions is still relatively limited. The availability of a global genetic interaction map in Saccharomyces cerevisiae, covering ～30% of all possible double mutant combinations, provides an unprecedented opportunity for an unbiased assessment of the native structure within genetic interaction networks and how it relates to gene function and modular organization. Toward this end, we developed a data mining approach to exhaustively discover all block structures within this network, which allowed for its complete modular decomposition. The resulting modular structures revealed the importance of the context of individual genetic interactions in their interpretation and revealed distinct trends among genetic interaction hubs as well as insights into the evolution of duplicate genes. Block membership also revealed a surprising degree of multifunctionality across the yeast genome and enabled a novel association of VIP1 and IPK1 with DNA replication and repair, which is supported by experimental evidence. Our modular decomposition also provided a basis for testing the between-pathway model of negative genetic interactions and within-pathway model of positive genetic interactions. While we find that most modular structures involving negative genetic interactions fit the between-pathway model, we found that current models for positive genetic interactions fail to explain 80% of the modular structures detected. We also find differences between the modular structures of essential and nonessential genes.