METABOLISM OF INTRAVENOUS METHYLNALTREXONE IN MICE, RATS, DOGS AND HUMANS

were observed in rats. Dogs produced only one metabolite, MNTX-3-glucuronide (M9). In conclusion, MNTX was not extensively metabolized in humans. Conversion to methyl-6-naltrexol isomers (M4 and M5) and MNTX-3-sulfate (M2) were the primary pathways of metabolism in humans. MNTX was metabolized to a higher extent in mice than in rats, dogs, and humans. Glucuronidation was a major metabolic pathway in mice, rats and dogs, but not in humans. Overall, the data suggested species differences in the metabolism of MNTX

Microscopic characteristics of biodiesel – Graphene oxide nanoparticle blends and their Utilisation in a compression ignition engine

Use of nano-additives in biofuels is an important research and development topic for achieving optimum engine performance with reduced emissions. In this study, rice bran oil was converted into biodiesel and graphene oxide (GO) nanoparticles were infused into biodiesel-diesel blends. Two blends containing (i) 5% biodiesel, 95% diesel and 30 ppm GO (B5D95GO30) and (ii) 15% biodiesel, 85% diesel and 30 ppm GO (B15D85GO30) were prepared. The fuel properties like heating value, kinematic viscosity, cetane number, etc. of the nanoadditives–biodiesel-diesel blends (NBDB) were measured. Effects of injection timing (IT) on the performance, combustion and emission characteristics were studied. It was observed that both B15D85GO30 and B5D95GO30 blends at IT23° gave up to 13.5% reduction in specific fuel consumption. Compared to diesel, the brake thermal efficiency was increased by 7.62% for B15D85GO30 at IT23° and IT25°. An increase in IT from 23° to 25° deteriorated the indicated thermal efficiency by 6.68% for B15D85GO30. At maximum load condition, the peak heat release rates of NBDB were found to be lower than the pure diesel at both IT. The CO, CO2 & NOx emissions were reduced by 2–8%. The study concluded that B15D85GO30 at IT23° gave optimum results in terms of performance, combustion and emission characteristics

Prevalence and Risk Factors of Neurologic Manifestations in Hospitalized Children Diagnosed with Acute SARS-CoV-2 or MIS-C

Background: Our objective was to characterize the frequency, early impact, and risk factors for neurological manifestations in hospitalized children with acute severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection or multisystem inflammatory syndrome in children (MIS-C). Methods: Multicenter, cross-sectional study of neurological manifestations in children aged <18 years hospitalized with positive SARS-CoV-2 test or clinical diagnosis of a SARS-CoV-2-related condition between January 2020 and April 2021. Multivariable logistic regression to identify risk factors for neurological manifestations was performed. Results: Of 1493 children, 1278 (86%) were diagnosed with acute SARS-CoV-2 and 215 (14%) with MIS-C. Overall, 44% of the cohort (40% acute SARS-CoV-2 and 66% MIS-C) had at least one neurological manifestation. The most common neurological findings in children with acute SARS-CoV-2 and MIS-C diagnosis were headache (16% and 47%) and acute encephalopathy (15% and 22%), both P < 0.05. Children with neurological manifestations were more likely to require intensive care unit (ICU) care (51% vs 22%), P < 0.001. In multivariable logistic regression, children with neurological manifestations were older (odds ratio [OR] 1.1 and 95% confidence interval [CI] 1.07 to 1.13) and more likely to have MIS-C versus acute SARS-CoV-2 (OR 2.16, 95% CI 1.45 to 3.24), pre-existing neurological and metabolic conditions (OR 3.48, 95% CI 2.37 to 5.15; and OR 1.65, 95% CI 1.04 to 2.66, respectively), and pharyngeal (OR 1.74, 95% CI 1.16 to 2.64) or abdominal pain (OR 1.43, 95% CI 1.03 to 2.00); all P < 0.05. Conclusions: In this multicenter study, 44% of children hospitalized with SARS-CoV-2-related conditions experienced neurological manifestations, which were associated with ICU admission and pre-existing neurological condition. Posthospital assessment for, and support of, functional impairment and neuroprotective strategies are vitally needed

REVIEW ON FEATURE SELECTION TECHNIQUES AND ITS IMPACT FOR EFFECTIVE DATA CLASSIFICATION USING UCI MACHINE LEARNING REPOSITORY DATASET

Feature selection goal is to get rid of redundant and irrelevant features. The problem of feature subset selection is that of finding a subset of the original features of a dataset, such that an induction algorithm run on data containing only selected features makes a classifier to generate with the highest possible accuracy. High dimensional data can contain a high degree of irrelevant and redundant features which may greatly degrade the performance of learning algorithms. The performance of different feature selectors such as CFS, Chi-Square, Information Gain, Gain Ratio, One R and Symmetrical Uncertainty were evaluated on two different popular classification algorithms namely Decision Tree and Naive Bayesian method. A significant improvement in the performance of DT and NB classifier was shown after reducing the number of both irrelevant and redundant features by the use of different feature ranking methods

A Novel Feature Selection Technique for Improved Survivability Diagnosis of Breast Cancer

AbstractIn this paper we propose a novel Shapely Value Embedded Genetic Algorithm, called as SVEGA that improves the breast cancer diagnosis accuracy that selects the gene subset from the high dimensional gene data. Particularly, the embedded Shapely Value includes two memetic operators namely “include” and “remove” features (or genes) to realize the genetic algorithm (GA) solution. The method is ranking the genes according to its capability to differentiate the classes. The method selects the genes that can maximize the capability to discriminate between different classes. Thus, the dimensionality of data features is reduced and the classification accuracy rate is improved. Four classifiers such as Support vector machine (SVM), Naïve Bayes (NB), K-Nearest Neighbor (KNN) and J48 are used on the breast cancer dataset from the Kent ridge biomedical repository to classify between the normal and abnormal tissues and to diagnose as benign and malignant tumours. The obtained classification accuracy demonstrates that the proposed method contributes to the superior diagnosis of breast cancer than the existing methods

Multi Filtration Feature Selection (MFFS) to improve discriminatory ability in clinical data set

Selection of optimal features is an important area of research in medical data mining systems. In this paper we introduce an efficient four-stage procedure – feature extraction, feature subset selection, feature ranking and classification, called as Multi-Filtration Feature Selection (MFFS), for an investigation on the improvement of detection accuracy and optimal feature subset selection. The proposed method adjusts a parameter named “variance coverage” and builds the model with the value at which maximum classification accuracy is obtained. This facilitates the selection of a compact set of superior features, remarkably at a very low cost. An extensive experimental comparison of the proposed method and other methods using four different classifiers (Naïve Bayes (NB), Support Vector Machine (SVM), multi layer perceptron (MLP) and J48 decision tree) and 22 different medical data sets confirm that the proposed MFFS strategy yields promising results on feature selection and classification accuracy for medical data mining field of research