Incidence of arthropods in dried fish products

Arthropods have been recorded from various types of insect infested dried fish products stored in the laboratory. They have been identified as Suidesia nesbetti Hughes (Acaridae) infesting dried anchovies and dried mussel, Dermestes ater Dermestidae coleoptera) attacking wet cured sardines and smoked catfish and Stegobium panicium infesting smoked catfish and dried mussel. Incidences of Stegobium panicium in dry fish products and Suidesia nesbetti in dried mussel has been recorded for the first time

Smoking cessation therapy with varenicline

Smoking cessation is the only available intervention proven to halt progression of chronic obstructive pulmonary disease (COPD). The authors discuss the current existing treatment modalities and the role of a newly approved agent, varenicline, in promotion of smoking cessation. Varenicline is a novel agent that is a centrally acting partial nicotinic acetylcholine receptor agonist. It has both agonistic and antagonistic properties that together are believed to account for reduction of craving and withdrawal as well as blocking the rewarding effects of smoking. Its targeted mechanism of action, better efficacy and tolerability makes varenicline a useful therapeutic option for smoking cessation. In this article, we discuss presently available options for smoking cessation and review the literature on efficacy of varenicline

Identification of novel therapeutics for complex diseases from genome-wide association data

Background: Human genome sequencing has enabled the association of phenotypes with genetic loci, but our ability to effectively translate this data to the clinic has not kept pace. Over the past 60 years, pharmaceutical companies have successfully demonstrated the safety and efficacy of over 1,200 novel therapeutic drugs via costly clinical studies. While this process must continue, better use can be made of the existing valuable data. In silico tools such as candidate gene prediction systems allow rapid identification of disease genes by identifying the most probable candidate genes linked to genetic markers of the disease or phenotype under investigation. Integration of drug-target data with candidate gene prediction systems can identify novel phenotypes which may benefit from current therapeutics. Such a drug repositioning tool can save valuable time and money spent on preclinical studies and phase I clinical trials. Methods. We previously used Gentrepid (http://www.gentrepid.org) as a platform to predict 1,497 candidate genes for the seven complex diseases considered in the Wellcome Trust Case-Control Consortium genome-wide association study; namely Type 2 Diabetes, Bipolar Disorder, Crohn's Disease, Hypertension, Type 1 Diabetes, Coronary Artery Disease and Rheumatoid Arthritis. Here, we adopted a simple approach to integrate drug data from three publicly available drug databases: the Therapeutic Target Database, the Pharmacogenomics Knowledgebase and DrugBank; with candidate gene predictions from Gentrepid at the systems level. Results: Using the publicly available drug databases as sources of drug-target association data, we identified a total of 428 candidate genes as novel therapeutic targets for the seven phenotypes of interest, and 2,130 drugs feasible for repositioning against the predicted novel targets. Conclusions: By integrating genetic, bioinformatic and drug data, we have demonstrated that currently available drugs may be repositioned as novel therapeutics for the seven diseases studied here, quickly taking advantage of prior work in pharmaceutics to translate ground-breaking results in genetics to clinical treatments. © 2014 Grover et al.; licensee BioMed Central Ltd

Novel therapeutics for coronary artery disease from genome-wide association study data

BACKGROUND: Coronary artery disease (CAD), one of the leading causes of death globally, is influenced by both environmental and genetic risk factors. Gene-centric genome-wide association studies (GWAS) involving cases and controls have been remarkably successful in identifying genetic loci contributing to CAD. Modern in silico platforms, such as candidate gene prediction tools, permit a systematic analysis of GWAS data to identify candidate genes for complex diseases like CAD. Subsequent integration of drug-target data from drug databases with the predicted candidate genes can potentially identify novel therapeutics suitable for repositioning towards treatment of CAD. METHODS: Previously, we were able to predict 264 candidate genes and 104 potential therapeutic targets for CAD using Gentrepid (http://www.gentrepid.org), a candidate gene prediction platform with two bioinformatic modules to reanalyze Wellcome Trust Case-Control Consortium GWAS data. In an expanded study, using five bioinformatic modules on the same data, Gentrepid predicted 647 candidate genes and successfully replicated 55% of the candidate genes identified by the more powerful CARDIoGRAMplusC4D consortium meta-analysis. Hence, Gentrepid was capable of enhancing lower quality genotype-phenotype data, using an independent knowledgebase of existing biological data. Here, we used our methodology to integrate drug data from three drug databases: the Therapeutic Target Database, PharmGKB and Drug Bank, with the 647 candidate gene predictions from Gentrepid. We utilized known CAD targets, the scientific literature, existing drug data and the CARDIoGRAMplusC4D meta-analysis study as benchmarks to validate Gentrepid predictions for CAD. RESULTS: Our analysis identified a total of 184 predicted candidate genes as novel therapeutic targets for CAD, and 981 novel therapeutics feasible for repositioning in clinical trials towards treatment of CAD. The benchmarks based on known CAD targets and the scientific literature showed that our results were significant (p < 0.05). CONCLUSIONS: We have demonstrated that available drugs may potentially be repositioned as novel therapeutics for the treatment of CAD. Drug repositioning can save valuable time and money spent on preclinical and phase I clinical studies

A comparative study on process optimization of betalain pigment extraction from Beta vulgaris subsp. vulgaris: RSM, ANN, and hybrid RSM-GA methods

Beta vulgaris subsp. vulgaris (red Swiss chard) leaf stalks offer a rich source of betalains, natural pigments with promising applications in the food industry. This study employed response surface methodology (RSM) in conjunction with a 3-level Box-Behnken design to optimize independent extraction variables, including temperature, extraction time, and solid-to-liquid ratio, for maximizing betalain extraction from red Swiss chard. Betacyanins and betaxanthins, the key natural pigments, were targeted as response variables. Statistical analysis revealed the optimal conditions for extraction: 21.14 min of extraction time, 52.98°C temperature, and a solid-to-liquid ratio of 21.61 mg/mL, resulting in the maximum extraction of betacyanins (15.53 mg/100g) and betaxanthins (9.5 mg/100g). To enhance prediction accuracy, an artificial neural network (ANN) model was employed, outperforming RSM predictions. Moreover, incorporating a genetic algorithm (GA) into the RSM regression equation predicted even higher betalain contents, with betacyanins reaching 16.53 mg/100g and betaxanthins 10.52 mg/100g. Confirmation experiments conducted under RSM-GA predicted optimum conditions demonstrated mean betacyanin and betaxanthin contents of 16.54 mg/100g and 10.49 mg/100g, respectively. The superior predictive capabilities of the ANN model and the synergistic integration of GA with RSM highlight innovative approaches for enhancing extraction efficiency. Furthermore, the characterized extract exhibit attributes such as aggregated morphology, amorphous nature, and high thermal stability

Extraction of pectin from Ethiopian prickly pear fruit peel and its potency for preparing of cellulose-reinforced biofilm

The objective of this research was to extract and characterize the pectin from the fruit peels of Ethiopian prickly pears (EPP) (Opuntia ficus-indica) using microwave assisted method. Solution pH and microwave potential were optimized using different pH values (1, 3, and 4) and power (300, 400, and 500 W), respectively, to extract ameliorated pectin yield. The pectin yield for EPP varied between 2.3 and 10.0 %. At a pH of 1.0 with 400 microwave intensity, the highest yield was seen. The extracted pectin from EPP had a 25.16 % ash content; however, the pectin sample contained less water and weighed less than the control sample. Further, transforming the acquired pectin from EPP was used to prepare biofilm reinforced by cellulose. Film was prepared using the casting method. It was aimed to provide a new function to EPP waste for preparing the biofilms by developing with the use of cellulose-reinforced modification to ameliorate the mechanical property Therefore, with further optimization and improvements, EPP-F could be used for nonstructural applications, such as a sustainable food packaging material

Vimentin Suppresses Inflammation and Tumorigenesis in the Mouse Intestine

Vimentin has been implicated in wound healing, inflammation, and cancer, but its functional contribution to intestinal diseases is poorly understood. To study how vimentin is involved during tissue injury and repair of simple epithelium, we induced colonic epithelial cell damage in the vimentin null (Vim(-/-)) mouse model. Vim(-/-) mice challenged with dextran sodium sulfate (DSS) had worse colitis manifestations than wild-type (WT) mice. Vim(-/-) colons also produced more reactive oxygen and nitrogen species, possibly contributing to the pathogenesis of gut inflammation and tumorigenesis than in WT mice. We subsequently describe that CD11b(+) macrophages served as the mainly cellular source of reactive oxygen species (ROS) production via vimentin-ROS-pSTAT3-interleukin-6 inflammatory pathways. Further, we demonstrated that Vim(-/-) mice did not develop colitis-associated cancer model upon DSS treatment spontaneously but increased tumor numbers and size in the distal colon in the azoxymethane/DSS model comparing with WT mice. Thus, vimentin has a crucial role in protection from colitis induction and tumorigenesis of the colon

Use of varenicline for smoking cessation treatment in UK primary care: an association rule mining analysis

BACKGROUND: Varenicline is probably the most effective smoking cessation pharmacotherapy, but is less widely used than nicotine replacement therapy. We therefore set out to identify the characteristics of numerically important groups of patients who typically do, or do not, receive varenicline in the UK. METHODS: We used association rule mining to analyse data on prescribing of smoking cessation pharmacotherapy in relation to age, sex, comorbidity and other variables from 477,620 people aged 16 years and over, registered as patients throughout 2011 with one of 559 UK general practices in The Health Improvement Network (THIN) database, and recorded to be current smokers. RESULTS: 46,685 participants (9.8% of all current smokers) were prescribed any smoking cessation treatment during 2011, and 19,316 of these (4% of current smokers, 41% of those who received any therapy) were prescribed varenicline. Prescription of varenicline was most common among heavy smokers aged 31–60, and in those with a diagnosis of COPD. Varenicline was rarely used among smokers who were otherwise in good health, or were aged over 60, were lighter smokers, or had psychotic disorders or dementia. CONCLUSIONS: Varenicline is being underused in healthy smokers, or in older smokers, and in those with psychotic disorders or dementia. Since varenicline is probably the most effective available single cessation therapy, this study identifies under-treatment of substantial public health significance

Privacy preservation for the health care sector in a cloud environment by advanced hybridization mechanism

Cloud computing is a very popular computing model, which grants a manageable infrastructure for various kinds of functions, like storage of data, application realization and presenting, and delivery of information. The concept is therefore very dynamically advancing in all kinds of organisations, including, in particular, the health care sector. However, effective analysis and extraction of information is a challenging issue that must find adequate solutions as soon as possible, since the medical scenarios are heavily dependent on such computing aspects as data security, computing standards and compliance, governance, and so on. In order to contribute to the resolution of the issues, associated with these aspects, this paper proposes a privacy-preserving algorithm for both data sanitization and restoration processes. Even though a high number of researchers contributed to the enhancement of the restoration process, the joint sanitization and restoration process still faces some problems, such as high cost. To attain better results with a possibly low cost, this paper proposes a hybrid algorithm, referred to as GlowWorm Swarm Employed Bee (GWOSEB) for realization of both data sanitization and data restoration process. The proposed GWOSEB algorithm is compared as to its performance with some of the existing approaches, such as the conventional Glowworm Swarm Optimization (GSO), FireFly (FF), Particle Swarm Optimization (PSO), Artificial Bee Colony (ABC), Genetic Algorithm (GA), and Genetically Modified Glowworm Swarm (GMGW), in terms of analysis involving the best, worst, mean, median and standard deviation values, sanitization and restoration effectiveness, convergence analysis, and sensitivity analysis of the generated optimal key. The comparison shows the supremacy of the developed approach

Thermodynamic Modelling for Design of Synthetic Slag for Inclusion Removal

Enhancement in steel cleanliness is essential for high-performance steels for their high-end applications in structural, automotive, defence and strategic sectors. The non-metallic inclusions play a decisive role in clean steelmaking. The inclusions have to be necessarily minimized or modified by controlling their morphology, composition and size distribution to reduce the detrimental effect of inclusion on the mechanical properties of steel. The present article discusses the results of a thermodynamic study carried out on the various synthetic slags for inclusion removal. It involves computational thermodynamics determining the products of deoxidation, their physical properties and their implication on the quality of steel