In modern-day medicine, nanotechnology and nanoparticles are some of the indispensable tools in disease monitoring and therapy. The term “nanomaterials” describes materials with nanoscale dimensions (< 100 nm) and are broadly classified into natural and synthetic nanomaterials. However, “engineered” nanomaterials have received significant attention due to their versatility. Although enormous strides have been made in research and development in the field of nanotechnology, it is often confusing for beginners to make an informed choice regarding the nanocarrier system and its potential applications. Hence, in this review, we have endeavored to briefly explain the most commonly used nanomaterials, their core properties and how surface functionalization would facilitate competent delivery of drugs or therapeutic molecules. Similarly, the suitability of carbon-based nanomaterials like CNT and QD has been discussed for targeted drug delivery and siRNA therapy. One of the biggest challenges in the formulation of drug delivery systems is fulfilling targeted/specific drug delivery, controlling drug release and preventing opsonization. Thus, a different mechanism of drug targeting, the role of suitable drug-laden nanocarrier fabrication and methods to augment drug solubility and bioavailability are discussed. Additionally, different routes of nanocarrier administration are discussed to provide greater understanding of the biological and other barriers and their impact on drug transport. The overall aim of this article is to facilitate straightforward perception of nanocarrier design, routes of various nanoparticle administration and the challenges associated with each drug delivery method.

No abstract

No abstract

The removal of nitrogen oxides (NO and NO2) from dry nitrogen gas and dry air with and without ethylene using a pulsed streamer corona discharge reactor (PSCDR) was investigated. A kinetic model was developed to characterize the chemical reactions taking place in a PSCDR using a combination of the CHEMKIN and KINEMA programs. Concurrently, an experimental program to determine NO removal from a gas stream was conducted. The electron density for the reactions of N2 and O2 by electron−molecule collisions was obtained by fitting the model to experimental data on NO removal as functions of the applied electric field and the reactor residence times. The model was then used to predict the concentrations of various other species, including O3, NO2, N2O, and byproducts of ethylene decomposition, to test the model and to guide the experimental analysis of byproduct characterization.

Pattern recognition, machine learning and artificial intelligence approaches play an increasingly important role in rational drug design, screening and identification of candidate molecules and studies on quantitative structure-activity relationships (QSAR). In this review, we present an overview of basic concepts and methodology in the fields of machine learning and artificial intelligence (AI). An emphasis is put on methods that enable an intuitive interpretation of the results and facilitate gaining an insight into the structure of the problem at hand. We also discuss representative applications of AI methods to docking, screening and QSAR studies. The growing trend to integrate computational and experimental efforts in that regard and some future developments are discussed. In addition, we comment on a broader role of machine learning and artificial intelligence approaches in biomedical research.

BackgroundTechnological advances have enabled transcriptome characterization of cell types at the single-cell level providing new biological insights. New methods that enable simple yet high-throughput single-cell expression profiling are highly desirable.ResultsHere we report a novel nanowell-based single-cell RNA sequencing system, ICELL8, which enables processing of thousands of cells per sample. The system employs a 5,184-nanowell-containing microchip to capture ~1,300 single cells and process them. Each nanowell contains preprinted oligonucleotides encoding poly-d(T), a unique well barcode, and a unique molecular identifier. The ICELL8 system uses imaging software to identify nanowells containing viable single cells and only wells with single cells are processed into sequencing libraries. Here, we report the performance and utility of ICELL8 using samples of increasing complexity from cultured cells to mouse solid tissue samples. Our assessment of the system to discriminate between mixed human and mouse cells showed that ICELL8 has a low cell multiplet rate (< 3%) and low cross-cell contamination. We characterized single-cell transcriptomes of more than a thousand cultured human and mouse cells as well as 468 mouse pancreatic islets cells. We were able to identify distinct cell types in pancreatic islets, including alpha, beta, delta and gamma cells.ConclusionsOverall, ICELL8 provides efficient and cost-effective single-cell expression profiling of thousands of cells, allowing researchers to decipher single-cell transcriptomes within complex biological samples.Electronic supplementary materialThe online version of this article (doi:10.1186/s12864-017-3893-1) contains supplementary material, which is available to authorized users.

No abstract

No abstract