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, help 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 diet-microbiome-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 its 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.

medicines. Some studies pointed out that many facilities lay weight on the frequency of prescription although they consider recommendations from clinical guidelines. In addition, several researches suggest the tendencies which they are more sensitive to medical cost, whereas quarter of them do not perform any pharmacoeconomic analysis. ConClusions: The paucity of research on development of HF suggests a lack of interest in the topic in Japan. In order to have a fair allocation of health and monetary resources, we need to review the role and making process of HF. Minimum requirements to develop Japanese HF should also be considered.

Diabetes type II is a complex disease with unclear pathophysiology. Lack of adherence and high cost of medicines invariably make the management of diabetes type II highly challenging. Newer fixed drug combinations (FDC) are cost effective and can improve the medication adherence thereby prevent the complications of diabetes. Safety and efficacy of newer FDCs are not well established in all populations. Moreover, extrapolating the efficacy and safety data globally may not be pragmatic. Our review will discuss newer chemical combinations available for the treatment of diabetes type II. Areas covered: In the present review, the authors discussed the newer FDCs available as add on therapy to the existing pharmacological interventions of diabetes type II that have shown promising results in various randomised trials with regard to efficacy and safety. Expert opinion: Safety and efficacy data of newer FDCs available as an adjuvant therapy to conventional pharmacological interventions in diabetes type II revealed that fewer new FDCs are promising with their high efficacy and low adverse effect. However, there is a need to explore the place in therapy to establish the utility of FDC in diabetes type II management.

Tracking and early identification of suspected cases are essential to control and prevent potential COVID-19 outbreaks. One of the most popular techniques used to track this disease is the use of Infrared cameras to identify individuals with elevated body temperatures. However, they are limited by their inability to be implemented in open public settings such as public parks or even outdoor recreational centers. This limits their ability to effectively track possible COVID-19 patients as open public recreational places such as parks, concert venues and other public venues are hotspots for the spreading of the virus. Other technological solutions such as thermal scanners require an individual to perform the actual testing as they are not individual standalone technologies. This method of testing can potentially cause the transmission of the virus between the tester and the individual getting tested. As can be seen, an alternative solution is essential to solving this issue. In this study, we aim to present the system, design and potential scope of a non-invasive system that can diagnose and identify potential COVID-19 patients using thermal and optical images of the individual using drone technology. The proposed system (COVIDRONE) combines multi-modal machine intelligence, computer vision and real-time monitoring to enable scalable monitoring. The system will also involve the use of machine learning algorithms for better and more accurate diagnosis. We envisage that development of such technologies may help in developing technological solutions to combat infectious disease threats in the future pandemics.