Data Integration Protocol In Ten-steps (DIPIT) : a new standard for medical researchers

The exponential increase in data, computing power and the availability of readily accessible analytical software has allowed organisations around the world to leverage the benefits of integrating multiple heterogeneous data files for enterprise-level planning and decision making. Benefits from effective data integration to the health and medical research community include more trustworthy research, higher service quality, improved personnel efficiency, reduction of redundant tasks, facilitation of auditing and more timely, relevant and specific information. The costs of poor quality processes elevate the risk of erroneous outcomes, an erosion of confidence in the data and the organisations using these data. To date there are no documented set of standards for best practice integration of heterogeneous data files for research purposes. Therefore, the aim of this paper is to describe a set of clear protocol for data file integration (Data Integration Protocol In Ten-steps; DIPIT) translational to any field of research.

Novel immobilization of a quaternary ammonium moiety on keratin fibers for medical applications.

This paper introduces a new approach for immobilizing a quaternary ammonium moiety on a keratinous substrate for enhanced medical applications. The method involves the generation of thiols by controlled reduction of cystine disulfide bonds in the keratin, followed by reaction with [2-(acryloyloxy)ethyl]trimethylammonium chloride through thiol-ene click chemistry. The modified substrate was characterized with Raman and infrared spectroscopy, and assessed for its antibacterial efficacy and other performance changes. The results have demonstrated that the quaternary ammonium moiety has been effectively attached onto the keratin structure, and the resultant keratin substrate exhibits a multifunctional effect including antibacterial and antistatic properties, improved liquid moisture management property, improved dyeability and a non-leaching characteristic of the treated substrate.

Enhancing predictive accuracy of cardiac autonomic neuropathy using blood biochemistry features and iterative multitier ensembles

Blood biochemistry attributes form an important class of tests, routinely collected several times per year for many patients with diabetes. The objective of this study is to investigate the role of blood biochemistry for improving the predictive accuracy of the diagnosis of cardiac autonomic neuropathy (CAN) progression. Blood biochemistry contributes to CAN, and so it is a causative factor that can provide additional power for the diagnosis of CAN especially in the absence of a complete set of Ewing tests. We introduce automated iterative multitier ensembles (AIME) and investigate their performance in comparison to base classifiers and standard ensemble classifiers for blood biochemistry attributes. AIME incorporate diverse ensembles into several tiers simultaneously and combine them into one automatically generated integrated system so that one ensemble acts as an integral part of another ensemble. We carried out extensive experimental analysis using large datasets from the diabetes screening research initiative (DiScRi) project. The results of our experiments show that several blood biochemistry attributes can be used to supplement the Ewing battery for the detection of CAN in situations where one or more of the Ewing tests cannot be completed because of the individual difficulties faced by each patient in performing the tests. The results show that AIME provide higher accuracy as a multitier CAN classification paradigm. The best predictive accuracy of 99.57% has been obtained by the AIME combining decorate on top tier with bagging on middle tier based on random forest. Practitioners can use these findings to increase the accuracy of CAN diagnosis.

Forms of n-3 (ALA, C18:3n-3 or DHA, C22:6n-3) fatty acids affect carcass yield, blood lipids, muscle n-3 fatty acids and liver gene expression in lambs

The effects of supplementing diets with n-3 alpha-linolenic acid (ALA) and docosahexaenoic acid (DHA) on plasma metabolites, carcass yield, muscle n-3 fatty acids and liver messenger RNA (mRNA) in lambs were investigated. Lambs (n = 120) were stratified to 12 groups based on body weight (35 ± 3.1 kg), and within groups randomly allocated to four dietary treatments: basal diet (BAS), BAS with 10.7 % flaxseed supplement (Flax), BAS with 1.8 % algae supplement (DHA), BAS with Flax and DHA (FlaxDHA). Lambs were fed for 56 days. Blood samples were collected on day 0 and day 56, and plasma analysed for insulin and lipids. Lambs were slaughtered, and carcass traits measured. At 30 min and 24 h, liver and muscle samples, respectively, were collected for determination of mRNA (FADS1, FADS2, CPT1A, ACOX1) and fatty acid composition. Lambs fed Flax had higher plasma triacylglycerol, body weight, body fat and carcass yield compared with the BAS group (P < 0.001). DHA supplementation increased carcass yield and muscle DHA while lowering plasma insulin compared with the BAS diet (P < 0.01). Flax treatment increased (P < 0.001) muscle ALA concentration, while DHA treatment increased (P < 0.001) muscle DHA concentration. Liver mRNA FADS2 was higher and CPT1A lower in the DHA group (P < 0.05). The FlaxDHA diet had additive effects, including higher FADS1 and ACOX1 mRNA than for the Flax or DHA diet. In summary, supplementation with ALA or DHA modulated plasma metabolites, muscle DHA, body fat and liver gene expression differently.