Important Part Of Current Research Research Articles

Effects of Dietary Components on Mast Cells: Possible Use as Nutraceuticals for Allergies?

Allergic diseases affect an estimated 30 percent of the world's population. Mast cells (MC) are the key effector cells of allergic reactions by releasing pro-inflammatory mediators such as histamine, lipid mediators, and cytokines/chemokines. Components of the daily diet, including certain fatty acids, amino acids, and vitamins, as well as secondary plant components, may have effects on MC and thus may be of interest as nutraceuticals for the prevention and treatment of allergies. This review summarizes the anti-inflammatory effects of dietary components on MC, including the signaling pathways involved, in in vitro and in vivo models. Butyrate, calcitriol, kaempferol, quercetin, luteolin, resveratrol, curcumin, and cinnamon extract were the most effective in suppressing the release of preformed and de novo synthesized mediators from MC or in animal models. In randomized controlled trials (RCT), vitamin D, quercetin, O-methylated epigallocatechin gallate (EGCG), resveratrol, curcumin, and cinnamon extract improved symptoms of allergic rhinitis (AR) and reduced the number of inflammatory cells in patients. However, strategies to overcome the poor bioavailability of these nutrients are an important part of current research.

UV/Vis spectroscopy as an in-line monitoring tool for tablet content uniformity

Continuous manufacturing provides advantages compared to batch manufacturing and is increasingly gaining importance in the pharmaceutical industry. In particular, the implementation of tablet processes in continuous plants is an important part of current research. For this, in-line real-time monitoring of product quality through process analytical technology (PAT) tools is crucial. This study focuses on an in-line UV/Vis spectroscopy method for monitoring the active pharmaceutical ingredient (API) content in tablets. UV/Vis spectroscopy is particularly advantageous here, because it allows univariate data analysis without complex data processing. Experiments were conducted on a rotary tablet press. The tablets consisted of 7– 13 wt% theophylline monohydrate as API, lactose monohydrate and magnesium stearate. Two tablet production rates were investigated, 7200 and 20000 tablets per hour. The UV/Vis probe was mounted at the ejection position and measurements were taken on the tablet sidewall. Validation was according to ICH Q2 with respect to specificity, linearity, precision, accuracy and range. The specificity for this formulation was proven and linearity was sufficient with coefficients of determination of 0.9891 for the low throughput and 0.9936 for the high throughput. Repeatability and intermediate precision were investigated. Both were sufficient, indicated by coefficients of variations with a maximum of 6.46% and 6.34%, respectively. The accuracy was evaluated by mean percent recovery. This showed a higher accuracy at 20000 tablets per hour than 7200 tablets per hour. However, both throughputs demonstrate sufficient accuracy. Finally, UV/Vis spectroscopy is a promising alternative to the common NIR and Raman Spectroscopy.

Ex Vivo Liver Machine Perfusion: Comprehensive Review of Common Animal Models.

The most common preservation technique for liver grafts is static cold storage. Due to the organ shortage for liver transplantation (LT), extended criteria donor (ECD) allografts are increasingly used-despite the higher risk of inferior outcome after transplantation. Ex vivo liver machine perfusion (MP) has been developed to improve the outcome of transplantation, especially with ECD grafts, and is currently under evaluation in clinical trials. We performed a literature search on PubMed and ISI Web of Science to assemble an overview of rodent and porcine animal models of ex vivo liver MP for transplantation, which is essential for the present and future development of clinical liver MP. Hypothermic, subnormothermic, and normothermic MP systems have been successfully used for rat and pig LT. In comparison with hypothermic systems, normothermic perfusion often incorporates a dialysis unit. Moreover, it enables metabolic assessment of liver grafts. Allografts experiencing warm ischemic time have a superior survival rate after MP compared with cold storage alone, irrespective of the temperature used for perfusion. Furthermore, ex vivo MP improves the outcome of regular and ECD liver grafts in animal models. Small and large animal models of ex vivo liver MP are available to foster the further development of this new technology. Impact Statement Ex vivo machine perfusion is an important part of current research in the field of liver transplantation. While evidence for improve storage is constantly rising, the development of future applications such as quality assessment and therapeutic interventions necessitates robust animal models. This review is intended to provide an overview of this technology in common large and small animal models and to give an outlook on future applications. Moreover, we describe developmental steps that can be followed by others, and which can help to decrease the number of animals used for experiments based on the replace, reduce, refine concept.

Influence of seed layers on the reflectance of sputtered aluminum thin films.

The fabrication of highly reflective aluminum coatings is still an important part of current research due to their high intrinsic reflectivity in a broad spectral range. By using thin seed layers of Cu, CuOx, Cr, CrOx, Au, and Ag, the morphology of sputtered (unprotected) aluminum layers and, consequently, their reflectance can be influenced. In this long-term study, the reflectance behavior was measured continuously using spectrophotometry. Particular seed layer materials enhance the reflectance of aluminum coatings significantly and reduce their long-term degradation. Combining such seed layers with evaporation processes and suitable protective layers could further increase the reflectance of aluminum coatings.

Infant nutrition and lifelong health: current perspectives and future challenges.

Understanding the role and importance of nutrition in early postnatal life, as an influence on lifelong vulnerability to poor health, is an important part of current research. We need to be able to define optimal patterns of infant feeding, not just to support growth and development in infancy, but also as determinants of later health. To date, much of the focus on the long-term effects of infant nutrition has been on milk feeding, to compare breast and formula feeding and to evaluate the effects of exclusivity, timing and duration of feeding different types of milk in infancy; other aspects of infant feeding such as age at introduction of solid foods and type of weaning diet have received less attention, and relatively little is known about their links to later health. Contemporary data are needed to enable us to move beyond explanation of historical infant feeding data in order to understand and predict health outcomes in future generations. Ongoing and new population studies, that include infants from diverse settings, will be key to providing generalizable data that can be used to define optimal feeding practice. There are some methodological challenges ahead, although significant progress has already been made, and further progress is envisaged in the future. In particular, the opportunity to bring together epidemiological studies and new mechanistic insights that will help identify key aspects of infant nutrition and their causal effects offer great promise both in moving this field forward as well as the potential for health benefits for future generations.

Semantic labeling for indoor topological mapping using a wearable catadioptric system

An important part of current research on appearance based mapping works towards semantic representations of the environment, which may allow autonomous systems to perform higher level tasks and provide better human–robot interaction. This work presents a new omnidirectional vision based scene labeling approach for augmented indoor topological mapping. Omnidirectional vision systems are of particular interest because they allow us to have more compact and efficient representation of the environment. Our approach includes novel ideas in order to augment the semantic information of a typical indoor topological map: we pay special attention to the semantic labels of the different types of transitions between places, and propose a simple way to include this semantic information as part of the criteria to segment the environment into topological regions. This work is built on efficient catadioptric image representation based on the Gist descriptor, which is used to classify the acquired views into types of indoor regions. The basic types of indoor regions considered are Place and Transition, further divided into more specific subclasses, e.g., Transition into door, stairs and elevator. Besides using the result of this labeling, the proposed mapping approach includes a probabilistic model to account for spatio-temporal consistency. All the proposed ideas have been evaluated in a new indoor dataset also presented in this paper. This dataset has been acquired with our wearable catadioptric vision system,11http://robots.unizar.es/omnicam/. showing promising results in a realistic prototype.

Data Integration in Genetics and Genomics: Methods and Challenges

Due to rapid technological advances, various types of genomic and proteomic data with different sizes, formats, and structures have become available. Among them are gene expression, single nucleotide polymorphism, copy number variation, and protein-protein/gene-gene interactions. Each of these distinct data types provides a different, partly independent and complementary, view of the whole genome. However, understanding functions of genes, proteins, and other aspects of the genome requires more information than provided by each of the datasets. Integrating data from different sources is, therefore, an important part of current research in genomics and proteomics. Data integration also plays important roles in combining clinical, environmental, and demographic data with high-throughput genomic data. Nevertheless, the concept of data integration is not well defined in the literature and it may mean different things to different researchers. In this paper, we first propose a conceptual framework for integrating genetic, genomic, and proteomic data. The framework captures fundamental aspects of data integration and is developed taking the key steps in genetic, genomic, and proteomic data fusion. Secondly, we provide a review of some of the most commonly used current methods and approaches for combining genomic data with focus on the statistical aspects.

Novel knowledge-based mean force potential at the profile level.

BackgroundThe development and testing of functions for the modeling of protein energetics is an important part of current research aimed at understanding protein structure and function. Knowledge-based mean force potentials are derived from statistical analyses of interacting groups in experimentally determined protein structures. Current knowledge-based mean force potentials are developed at the atom or amino acid level. The evolutionary information contained in the profiles is not investigated. Based on these observations, a class of novel knowledge-based mean force potentials at the profile level has been presented, which uses the evolutionary information of profiles for developing more powerful statistical potentials.ResultsThe frequency profiles are directly calculated from the multiple sequence alignments outputted by PSI-BLAST and converted into binary profiles with a probability threshold. As a result, the protein sequences are represented as sequences of binary profiles rather than sequences of amino acids. Similar to the knowledge-based potentials at the residue level, a class of novel potentials at the profile level is introduced. We develop four types of profile-level statistical potentials including distance-dependent, contact, Φ/Ψ dihedral angle and accessible surface statistical potentials. These potentials are first evaluated by the fold assessment between the correct and incorrect models generated by comparative modeling from our own and other groups. They are then used to recognize the native structures from well-constructed decoy sets. Experimental results show that all the knowledge-base mean force potentials at the profile level outperform those at the residue level. Significant improvements are obtained for the distance-dependent and accessible surface potentials (5–6%). The contact and Φ/Ψ dihedral angle potential only get a slight improvement (1–2%). Decoy set evaluation results show that the distance-dependent profile-level potentials even outperform other atom-level potentials. We also demonstrate that profile-level statistical potentials can improve the performance of threading.ConclusionThe knowledge-base mean force potentials at the profile level can provide better discriminatory ability than those at the residue level, so they will be useful for protein structure prediction and model refinement.