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Eller-Vainicher C.,University of Milan | Zhukouskaya V.V.,University of Milan | Zhukouskaya V.V.,Belarusian State Medical University | Tolkachev Y.V.,Republic Clinical Hospital of Medical Rehabilitation | And 6 more authors.
Diabetes Care | Year: 2011

OBJECTIVE - To investigate factors associated with bone mineral density (BMD) in type 1 diabetes by classic statistic and artificial neural networks. RESEARCH DESIGN AND METHODS - A total of 175 eugonadal type 1 diabetic patients (age 32.8 ± 8.4 years) and 151 age- and BMI-matched control subjects (age 32.6 ± 4.5 years) were studied. In all subjects, BMI and BMD (as Z score) at the lumbar spine (LS-BMD) and femur (F-BMD) were measured. Daily insulin dose (DID), age at diagnosis, presence of complications, creatinine clearance (ClCr), and HbA 1c were determined. RESULTS - LS- and F-BMD levels were lower in patients (20.11 ± 1.2 and 20.32 ± 1.4, respectively) than in control subjects (0.59 ± 1, P < 0.0001, and 0.63 ± 1, P < 0.0001, respectively). LS-BMD was independently associated with BMI and DID, whereas F-BMD was associated with BMI and ClCr. The cutoffs for predicting low BMD were as follows: BMI <23.5 kg/m 2, DID >0.67 units/kg, and ClCr <88.8 mL/min. The presence of all of these risk factors had a positive predictive value, and their absence had a negative predictive value for low BMD of 62.9 and 84.2%, respectively. Data were also analyzed using the TWIST system in combination with supervised artificial neural networks and a semantic connectivity map. The TWIST system selected 11 and 12 variables for F-BMD and LS-BMD prediction, which discriminated between high and low BMD with 67 and 66% accuracy, respectively. The connectivity map showed that low BMD at both sites was indirectly connected with HbA1c through chronic diabetes complications. CONCLUSIONS - In type 1 diabetes, low BMD is associated with low BMI and low ClCr and high DID. Chronic complications negatively influence BMD. © 2011 by the American Diabetes Association. Source


Coppede F.,University of Pisa | Grossi E.,Semeion Research Center | Migheli F.,University of Pisa | Migliore L.,University of Pisa
BMC Medical Genomics | Year: 2010

Background. Studies in mothers of Down syndrome individuals (MDS) point to a role for polymorphisms in folate metabolic genes in increasing chromosome damage and maternal risk for a Down syndrome (DS) pregnancy, suggesting complex gene-gene interactions. This study aimed to analyze a dataset of genetic and cytogenetic data in an Italian group of MDS and mothers of healthy children (control mothers) to assess the predictive capacity of artificial neural networks assembled in TWIST system in distinguish consistently these two different conditions and to identify the variables expressing the maximal amount of relevant information to the condition of being mother of a DS child. The dataset consisted of the following variables: the frequency of chromosome damage in peripheral lymphocytes (BNMN frequency) and the genotype for 7 common polymorphisms in folate metabolic genes (MTHFR 677C>T and 1298A>C, MTRR 66A>G, MTR 2756A>G, RFC1 80G>A and TYMS 28bp repeats and 1494 6bp deletion). Data were analysed using TWIST system in combination with supervised artificial neural networks, and a semantic connectivity map. Results. TWIST system selected 6 variables (BNMN frequency, MTHFR 677TT, RFC1 80AA, TYMS 1494 6bp +/+, TYMS 28bp 3R/3R and MTR 2756AA genotypes) that were subsequently used to discriminate between MDS and control mothers with 90% accuracy. The semantic connectivity map provided important information on the complex biological connections between the studied variables and the two conditions (being MDS or control mother). Conclusions. Overall, the study suggests a link between polymorphisms in folate metabolic genes and DS risk in Italian women. © 2010 Coppedè et al; licensee BioMed Central Ltd. Source


Grossi E.,Centro Diagnostico Italiano | Podda G.M.,University of Milan | Pugliano M.,University of Milan | Gabba S.,Centro Diagnostico Italiano | And 5 more authors.
Pharmacogenomics | Year: 2014

Background: In recent years, pharmacogenetic algorithms were developed for estimating the appropriate dose of vitamin K antagonists. Aim: To evaluate the performance of new generation artificial neural networks (ANNs) to predict the warfarin maintenance dose. Methods: Demographic, clinical and genetic data (CYP2C9 and VKORC1 polymorphisms) from 377 patients treated with warfarin were used. The final prediction model was based on 23 variables selected by TWIST® system within a bipartite division of the data set (training and testing) protocol. Results: The ANN algorithm reached high accuracy, with an average absolute error of 5.7 mg of the warfarin maintenance dose. In the subset of patients requiring ≤21 mg and 21-49 mg (45 and 51% of the cohort, respectively) the absolute error was 3.86 mg and 5.45 with a high percentage of subjects being correctly identified (71 and 73%, respectively). Conclusion: ANN appears to be a promising tool for vitamin K antagonist maintenance dose prediction. © 2014 Future Medicine Ltd. Source


Buscema M.,Semeion Research Center | Buscema M.,University of Colorado at Denver | Sacco P.L.,IULM University | Sacco P.L.,Harvard University
Physica A: Statistical Mechanics and its Applications | Year: 2016

In this paper, we introduce a new methodology for the evaluation of alternative algorithms in capturing the deep statistical structure of datasets of different types and nature, called MST Fitness, and based on the notion of Minimum Spanning Tree (MST). We test this methodology on six different databases, some of which artificial and widely used in similar experimentations, and some related to real world phenomena. Our test set consists of eight different algorithms, including some widely known and used, such as Principal Component Analysis, Linear Correlation, or Euclidean Distance. We moreover consider more sophisticated Artificial Neural Network based algorithms, such as the Self-Organizing Map (SOM) and a relatively new algorithm called Auto-Contractive Map (AutoCM). We find that, for our benchmark of datasets, AutoCM performs consistently better than all other algorithms for all of the datasets, and that its global performance is superior to that of the others of several orders of magnitude. It is to be checked in future research if AutoCM can be considered a truly general-purpose algorithm for the analysis of heterogeneous categories of datasets. © 2016 Elsevier B.V. Source


Gironi M.,Polidiagnostic Center | Gironi M.,San Raffaele Scientific Institute | Borgiani B.,Polidiagnostic Center | Borgiani B.,San Raffaele Scientific Institute | And 8 more authors.
Journal of Alzheimer's Disease | Year: 2014

Alzheimer's disease (AD) is the most common form of dementia, while mild cognitive impairment (MCI) causes a slight but measurable decline in cognitive abilities. A person with MCI has an increased risk of developing AD or another dementia. Thus, it is of medical interest to develop predictive tools to assess this risk. A growing awareness exists that pro-oxidative state and neuro-inflammation are both involved in AD. However, the extent of this relationship is still a matter of debate. Due to the expected non-linear correlations between oxidative and inflammatory markers, traditional statistics is unsuitable to dissect their relationship with the disease. Artificial neural networks (ANNs) are computational models inspired by central nervous system networks, capable of machine learning and pattern recognition. The aim of this work was to disclose the relationship between immunological and oxidative stress markers in AD and MCI by the application of ANNs. Through a machine learning approach, we were able to construct an algorithm to classify MCI and AD with high accuracy. Such an instrument, requiring a small amount of immunological and oxidative-stress parameters, would be useful in the clinical practice. Moreover, applying an innovative non-linear mathematical technique, a global immune deficit was shown to be associated with cognitive impairment. Surprisingly, both adaptive and innate immunity were peripherally defective in AD and MCI patients. From this study, new pathogenetic aspects of these diseases could emerge. © 2015 - IOS Press and the authors. Source

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