131,003 research outputs found
Levels of uroporphyrinogen decarboxylase (URO-D) in erythocytes of Italian porphyria cutanea tarda patients
No full textPorphyria cutanea tarda (PCT) is a human metabolic disorder due to the acquired or genetic impairment of uroporphyrinogen decarboxylase (URO-D) activity, the fifth enzyme of the heme biosynthetic pathway. A classification of inherited and non-inherited forms is based on the enzyme activity levels in red blood cells (RBC). Clinical manifestations of PCT are often precipitated by triggering factors such as alcohol, drug abuse, estrogens, virus infections, hepatotoxic chemicals and hepatic siderosis. We measured URO-D activity in RBC from a large sample of Italian PCT patients in order to define the enzyme activity distribution and to attempt a correlation among activity, risk factors and clinical outcome. Three classes of patients with low, normal and over-normal URO-D activity were defined according to control values. Low URO-D levels were present in 25.8% of patients, suggesting the familial form of PCT (type II). In this group, the outcome of PCT seems to be less influenced by risk factors. Patients with over-normal URO-D activity in RBC deserve further investigation
Early onset methylmalonic aciduria and homocystinuria cblC type with demyelinating neuropathy.
No full textMethylmalonic aciduria and homocystinuria, cblC type, is the most common inborn error of vitamin B(12) (cobalamin) metabolism. The recent cloning of the disease gene, MMACHC, has permitted genotype-phenotype correlation. In a 1-year-old girl, compound heterozygous c.271dupA and c.616C>T mutations in MMACHC were identified as causing an early onset methylmalonic aciduria and homocystinuria, cblC type, which was complicated by sensorimotor peripheral demyelinating neuropathy
Fish oil and post-operative atrial fibrillation: a meta-analysis of randomized controlled trials
No full textDariush Mozaffarian, Jason H. Y. Wu, Marcia C. de Oliveira Otto, Chirag M. Sandesara, Robert G. Metcalf, Roberto Latini, Peter Libby, Federico Lombardi, Patrick T. O’Gara, Richard L. Page, Maria G. Silletta, Luigi Tavazzi, Roberto Marchiol
Development of predictive models for short-term prediction of disability progression in multiple sclerosis
No full textMultiple Sclerosis (MS) is an autoimmune degenerative disease of the central nervous system, in which chronic inflammation leads to demyelination with transient or permanent axon damage. Symptoms of MS include problems with vision, movement, sensation and balance, which can be intermittent or progressively increasing over time until bringing to permanent disability. Predictive models of MS disability progression can be very useful to support the clinician in choosing the best care for each patient. The aim of this work is to develop predictive models of short-term MS disability progression. Data are part of the Multiple Sclerosis Outcome Assessments Consortium (MSOAC) Placebo database, which includes longitudinal demographic and clinical data of 2465 MS patients who were enrolled in the control arm of different MS clinical trials. Variables collected in the first visit were used to predict a binary outcome of disability progression at 6 months and 18 months from the baseline, using a logistic regression model. Disability progression was defined as a 1.5 increase in the Expanded Disability Status Scale (EDSS) value compared to the baseline time. 20 input variables were considered in each model, including demographics, medical history, functional tests, questionnaires, and MS phenotype. Preprocessed data were split into a training and a test set with an 80%-20% proportion. Logistic regression models were trained on the training set, using over-/undersampling techniques for balancing the classes. The identified models were tested on the test set by assessing the area under the receiver operating characteristic curve (AUC). Prediction performance on the test set was satisfactory, although not optimal, with AUC equal to 0.74 at 6 months and 0.71 at 18 months. These prediction performances are comparable with results obtained by other literature studies on smaller cohorts. Future developments of this work include the use of other machine learning techniques for model training, the application of feature selection and variable ranking techniques, the incorporation of new variables (e.g., imaging variables), and the external validation of the models on new populations
Eleven quick tips for data cleaning and feature engineering
No full textApplying computational statistics or machine learning methods to data is a key component of many scientific studies, in any field, but alone might not be sufficient to generate robust and reliable outcomes and results. Before applying any discovery method, preprocessing steps are necessary to prepare the data to the computational analysis. In this framework, data cleaning and feature engineering are key pillars of any scientific study involving data analysis and that should be adequately designed and performed since the first phases of the project. We call “feature” a variable describing a particular trait of a person or an observation, recorded usually as a column in a dataset. Even if pivotal, these data cleaning and feature engineering steps sometimes are done poorly or inefficiently, especially by beginners and unexperienced researchers. For this reason, we propose here our quick tips for data cleaning and feature engineering on how to carry out these important preprocessing steps correctly avoiding common mistakes and pitfalls. Although we designed these guidelines with bioinformatics and health informatics scenarios in mind, we believe they can more in general be applied to any scientific area. We therefore target these guidelines to any researcher or practitioners wanting to perform data cleaning or feature engineering. We believe our simple recommendations can help researchers and scholars perform better computational analyses that can lead, in turn, to more solid outcomes and more reliable discoveries
Distance rendering and perception of nearby virtual sound sources with a near-field filter model
No full textHeadphone rendering of nearby virtual sound sources represents to date an open issue in 3-D audio, due to a number of technical challenges and temporal requirements involved in the measurement of individual Head-Related Transfer Functions (HRTFs). In order to tackle this problem, we propose a filter model of near-field effects based on the Distance Variation Function (Kan et al., 2009). Thanks to its simple structure and low order, the model can be applied to any far-field virtual auditory display to yield a realistic and computationally efficient near-field compensation of spectral and binaural effects. The model is subjectively evaluated in two psychophysical experiments where the relative distance of pairs of virtually rendered sound sources is judged. Results show that even though sound intensity overshadows subtler near-field effects when it is available as a cue for distance, the model is capable of offering relative distance information of near lateral virtual sources when intensity cues are removed. Furthermore, performances of the model in relative distance rendering are compared to those of alternative near-field rendering methods available in the literature
MeSH term explosion and author rank improve expert recommendations
No full textInformation overload is an often-cited phenomenon that reduces the productivity, efficiency and efficacy of scientists. One challenge for scientists is to find appropriate collaborators in their research. The literature describes various solutions to the problem of expertise location, but most current approaches do not appear to be very suitable for expert recommendations in biomedical research. In this study, we present the development and initial evaluation of a vector space model-based algorithm to calculate researcher similarity using four inputs: 1) MeSH terms of publications; 2) MeSH terms and author rank; 3) exploded MeSH terms; and 4) exploded MeSH terms and author rank. We developed and evaluated the algorithm using a data set of 17,525 authors and their 22,542 papers. On average, our algorithms correctly predicted 2.5 of the top 5/10 coauthors of individual scientists. Exploded MeSH and author rank outperformed all other algorithms in accuracy, followed closely by MeSH and author rank. Our results show that the accuracy of MeSH term-based matching can be enhanced with other metadata such as author rank
Going Beyond Counting First Authors in Author Co-citation Analysis
No full textThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
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