The application of nanomedicines is increasing rapidly with the promise of targeted and efficient drug delivery. Nanomedicines address the shortcomings of conventional therapy, as evidenced by several preclinical and clinical investigations indicating sitespecific drug delivery, reduced side effects, and better treatment outcome. The development of suitable and biocompatible drug delivery vehicles is a prerequisite that has been successfully achieved by using simple and functionalized liposomes, nanoparticles, hydrogels, micelles, dendrimers, and mesoporous particles. A variety of drug delivery vehicles have been established for the targeted and controlled delivery of therapeutic agents in a wide range of chronic diseases, such as diabetes, cancer, atherosclerosis, myocardial ischemia, asthma, pulmonary tuberculosis, Parkinson's disease, and Alzheimer's disease. After successful outcomes in preclinical and clinical trials, many of these drugs have been marketed for human use, such as Abraxane®, Caelyx®, Mepact®, Myocet®, Emend®, and Rapamune®. Apart from drugs/compounds, novel therapeutic agents, such as peptides, nucleic acids (DNA and RNA), and genes have also shown potential to be used as nanomedicines for the treatment of several chronic ailments. However, a large number of extensive clinical trials are still needed to ensure the short-term and long-term effects of nanomedicines in humans. This review discusses the advantages of various drug delivery vehicles for better understanding of their utility in terms of current medical needs. Furthermore, the application of a wide range of nanomedicines is also described in the context of major chronic diseases.

The Internet of Things (IoT) is an emerging technology paradigm where millions of sensors and actuators help monitor and manage, physical, environmental and human systems in real-time. The inherent closedloop responsiveness and decision making of IoT applications make them ideal candidates for using low latency and scalable stream processing platforms. Distributed Stream Processing Systems (DSPS) hosted on Cloud data-centers are becoming the vital engine for real-time data processing and analytics in any IoT software architecture. But the efficacy and performance of contemporary DSPS have not been rigorously studied for IoT applications and data streams. Here, we develop RIoTBench, a Realtime IoT Benchmark suite, along with performance metrics, to evaluate DSPS for streaming IoT applications. The benchmark includes 27 common IoT tasks classified across various functional categories and implemented as reusable micro-benchmarks. Further, we propose four IoT application benchmarks composed from these tasks, and that leverage various dataflow semantics of DSPS. The applications are based on common IoT patterns for data pre-processing, statistical summarization and predictive analytics. These are coupled with four stream workloads sourced from real IoT observations on smart cities and fitness, with peak streams rates that range from 500 − 10, 000 messages/sec and diverse frequency distributions. We validate the RIoTBench suite for the popular Apache Storm DSPS on the Microsoft Azure public Cloud, and present empirical observations. This suite can be used by DSPS researchers for performance analysis and resource scheduling, and by IoT practitioners to evaluate DSPS platforms. arXiv:1701.08530v1 [cs.DC] 30 Jan 2017 1. We classify different characteristics of streaming applications, their composition semantics, and their data sources, in § 3.2. Then, in § 4, we propose categories of tasks that are essential for IoT applications and the key features of input data streams they operate upon.3. We identify performance metrics of DSPS that are necessary to meet the latency and scalability needs of streaming IoT applications, in § 5.4. We propose the RIoTBench real-time IoT benchmark for DSPS based on representative micro-benchmark tasks, drawn from the above categories, in § 6. We design four reference IoT applications that span Data preprocessing, Statistical analytics and Predictive Analytics, and are composed from these tasks. We also identify four real-world streams with different distributions as workloads on which to evaluate them.5. Lastly, we validate the proposed benchmark suite for the popular Apache Storm DSPS, and present empirical results for the same in § 7.Our contributions benefit two classes of audience. One, for developers and users in IoT domains, RIoTBench offers a set of realistic IoT tasks and applications that they can customize and configure to help evaluate candidate DSPS platforms for their performance and scalability needs. Two, for researchers on Big Data, it provides a reference micro and applicat...

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This feature article provides an overview of different kinds of futuristic biomaterials which have the potential to be used for fluorescent imaging and drug delivery, often simultaneously. The synthesis route or preparation process, fluorescence property, release profile, biocompatibility, bioimaging, and mechanistic approaches are vividly discussed. These include bioimaging with fluorescently doped quantum dots, mesoporous silica, noble metals, metal clusters, hydrophilic/hydrophobic polymers, semiconducting polymer dots, carbon/graphene dots, dendrimers, fluorescent proteins, and other nanobiomaterials. Another section discusses the controlled and targeted drug, gene, or biologically active material delivery using various vehicles such as micelles, 2D nanomaterials, organic nanoparticles, polymeric nanohybrids, and chemically modified polymers. In the last section, we discuss biomaterials, which can deliver biologically active molecules, and imaging the cell/tissue.