Prediction of high on-treatment platelet reactivity in clopidogrel-treated patients with acute coronary syndromes.

BACKGROUND: About 40% of clopidogrel-treated patients display high platelet reactivity (HPR). Alternative treatments of HPR patients, identified by platelet function tests, failed to improve their clinical outcomes in large randomized clinical trials. A more appealing alternative would be to identify HPR patients a priori, based on the presence/absence of demographic, clinical and genetic factors that affect PR. Due to the complexity and multiplicity of these factors, traditional statistical methods (TSMs) fail to identify a priori HPR patients accurately. The objective was to test whether Artificial Neural Networks (ANNs) or other Machine Learning Systems (MLSs), which use algorithms to extract model-like 'structure' information from a given set of data, accurately predict platelet reactivity (PR) in clopidogrel-treated patients. METHODS: A complete set of fifty-nine demographic, clinical, genetic data was available of 603 patients with acute coronary syndromes enrolled in the prospective GEPRESS study, which showed that HPR after 1month of clopidogrel treatment independently predicted adverse cardiovascular events in patients with Syntax Score >14. Data were analysed by MLSs and TSMs. ANNs identified more variables associated PR at 1month, compared to TSMs. RESULTS: ANNs overall accuracy in predicting PR, although superior to other MLSs was 63% (95% CI 59-66). PR phenotype changed in both directions in 35% of patients across the 3 time points tested (before PCI, at hospital discharge and at 1month). CONCLUSIONS: Despite their ability to analyse very complex non-linear phenomena, ANNs or MLS were unable to predict PR accurately, likely because PR is a highly unstable phenotype.

Gate Controlled Photocurrent Generation Mechanisms in High-Gain In2Se3 Phototransistors.

Photocurrent in photodetectors incorporating van der Waals materials is typically produced by a combination of photocurrent generation mechanisms that occur simultaneously during operation. Because of this, response times in these devices often yield to slower, high gain processes, which cannot be turned off. Here we report on photodetectors incorporating the layered material In2Se3, which allow complete modulation of a high gain, photogating mechanism in the ON state in favor of fast photoconduction in the OFF state. While photoconduction is largely gate independent, photocurrent from the photogating effect is strongly modulated through application of a back gate voltage. By varying the back gate, we demonstrate control over the dominant mechanism responsible for photocurrent generation. Furthermore, because of the strong photogating effect, these direct-band gap, multilayer phototransistors produce ultrahigh gains of (9.8 ± 2.5) × 10(4) A/W and inferred detectivities of (3.3 ± 0.8) × 10(13) Jones, putting In2Se3 among the most sensitive 2D materials for photodetection studied to date.

Type 2 diabetic patients with Graves' disease have more frequent and severe Graves' orbitopathy.

BACKGROUND AND AIMS: Due to the worldwide increasing prevalence of diabetes (DM), patients with both diabetes and Graves' disease (GD) have become more frequent. Sporadic reports indicate that Graves' orbitopathy (GO), a GD complication that affects orbital soft tissues, can be severe in DM patients. The relationship between these diseases is not well understood. This study aims at evaluating the association of GD and GO with autoimmune and non-autoimmune diabetes (DM) and to assess diabetic features that influence GD and GO prevalence and severity. METHODS AND RESULTS: This retrospective study evaluated GD, GO and DM association in 1211 consecutive GD patients (447 with GO and 77 with DM). A case-control study was carried out to evaluate DM relationship with GO severity by comparing at 1:2 ratio GO patients with or without DM. A strong association was found between GD and T1DM (p = 0.01) but not T2DM. Instead, the presence of GO was strongly associated with T2DM (p = 0.01). Moreover, GO was more frequently severe in GD patients with T2DM (11/30 or 36.6%) than in those without T2DM (1/60 or 1.7%, p = 0.05). T2DM was the strongest risk factor for severe GO (OR = 34.1 vs. 4.4 p < 0.049 in cigarette smokers). DM duration, obesity and vascular complications, but not metabolic control were significant determinants of GO severity. CONCLUSIONS: GD is associated with T1DM but not with T2DM, probably because of the common autoimmune background. GO, in contrast, is more frequent and severe in T2DM, significantly associated with obesity, diabetes duration and diabetic vasculopathy but not metabolic control.

Gene-gene and gene - clinical factors interaction in acute myocardial infarction: a new detailed risk chart.

AIMS: The complex pathogenesis of acute myocardial infarction (AMI) implicates phenotypic and genetic heterogeneity. In this pilot case-control study single nucleotide polymorphism (SNP) in several inflammatory genes, such as interleukin (IL)-1beta, IL-6, IL-10, alpha-1-antichymotrypsin (ACT), tumor necrosis factor alpha (TNF)-alpha and interferon gamma (IFN)-gamma genes along with SNPs of genes regulating vascular functions (vascular endothelial growth factor; VEGF) and cholesterol synthesis (hydroxy-methyl-glutaryl CoA reductase; HMGCR) were investigated. METHODS: Patients were genotyped with RT-PCR technique and data were analyzed with a new mathematical algorithm named Auto Contractive Map. RESULTS: The Auto Contractive Map (AutoCM), was applied in AMI patients with the aim to detect and evaluate the relationships among genetic factors, clinical variables and classical risk factors. Genes were selected because their strong regulatory effect on inflammation and SNP in these gene were located in the promoter region. In the connectivity map generated by AutoCM a group of variables was directly linked with the AMI status; these were: gender (male), early age at onset (50-65 years), HMGCR gene (CC wild type genotype), IL-1betaCT, IL-6 GG and VEGF CC genotypes. This direct link suggested a possible pathogenetic association with AMI. Other genetic, clinical and phenotypic variables were associated to the disease under a statistically defined hierarchy showed in the new connectivity map generated by AutoCM. CONCLUSION: These analyses suggested that genotypes of few inflammatory genes, a SNP in HMGCR gene, middle age, gender, low HDL and diabetes were very informative variables to predict the risk of AMI.

How to predict a high rate of inappropriateness for upper endoscopy in an endoscopic centre?

BACKGROUND: Inappropriateness of upper endoscopy (EGD) indication causes decreased diagnostic yield. Our aim of was to identify predictors of appropriateness rate for EGD among endoscopic centres. METHODS: A post-hoc analysis of two multicentre cross-sectional studies, including 6270 and 8252 patients consecutively referred to EGD in 44 (group A) and 55 (group B) endoscopic Italian centres in 2003 and 2007, respectively, was performed. A multiple forward stepwise regression was applied to group A, and independently validated in group B. A <70% threshold was adopted to define inadequate appropriateness rate clustered by centre. RESULTS: discrete variability of clustered appropriateness rates among the 44 group A centres was observed (median: 77%; range: 41-97%), and a <70% appropriateness rate was detected in 11 (25%). Independent predictors of centre appropriateness rate were: percentage of patients referred by general practitioners (GP), rate of urgent examinations, prevalence of relevant diseases, and academic status. For group B, sensitivity, specificity and area under receiver operating characteristic curve of the model in detecting centres with a <70% appropriateness rate were 54%, 93% and 0.72, respectively. CONCLUSIONS: A simple predictive rule, based on rate of patients referred by GPs, rate of urgent examinations, prevalence of relevant diseases and academic status, identified a small subset of centres characterised by a high rate of inappropriateness. These centres may be presumed to obtain the largest benefit from targeted educational programs.

The I.F.A.S.T. model allows the prediction of conversion to Alzheimer disease in patients with mild cognitive impairment with high degree of accuracy.

This paper presents the results obtained with the innovative use of special types of artificial neural networks (ANNs) assembled in a novel methodology named IFAST (implicit function as squashing time) capable of compressing the temporal sequence of electroencephalographic (EEG) data into spatial invariants. The aim of this study is to test the potential of this parallel and nonlinear EEG analysis technique in providing an automatic classification of mild cognitive impairment (MCI) subjects who will convert to Alzheimer's disease (AD) with a high degree of accuracy. Eyes-closed resting EEG data (10-20 electrode montage) were recorded in 143 amnesic MCI subjects. Based on 1-year follow up, the subjects were retrospectively classified to MCI converted to AD and MCI stable. The EEG tracks were successively filtered according to four different frequency ranges, in order to evaluate the hypotheses that a specific range, corresponding to specific brain wave type, could provide a better classification (0.12 Hz, 12.2 - 29.8 Hz; 30.2 - 40 Hz, and finally Notch Filter 48 - 50 Hz). The spatial content of the EEG voltage was extracted by IFAST step-wise procedure using ANNs. The data input for the classification operated by ANNs were not the EEG data, but the connections weights of a nonlinear auto-associative ANN trained to reproduce the recorded EEG tracks. These weights represented a good model of the peculiar spatial features of the EEG patterns at scalp surface. The classification based on these parameters was binary and performed by a supervised ANN. The best results distinguishing between MCI stable and MCI/AD reached to 85.98%.(012 Hz band). And confirmed the working hypothesis that a correct automatic classification can be obtained extracting spatial information content of the resting EEG voltage by ANNs and represent the basis for research aimed at integrating spatial and temporal information content of the EEG. These results suggest that this low-cost procedure can reliably distinguish eyes-closed resting EEG data in individual MCI subjects who will have different prognosis at 1-year follow up, and is promising for a large-scale periodic screening of large populations at amnesic MCI subjects at risk of AD.

Auto-Contractive Maps: an artificial adaptive system for data mining. An application to Alzheimer disease.

This article presents a new paradigm of Artificial Neural Networks (ANNs): the Auto-Contractive Maps (Auto-CM). The Auto-CM differ from the traditional ANNs under many viewpoints: the Auto-CM start their learning task without a random initialization of their weights, they meet their convergence criterion when all their output nodes become null, their weights matrix develops a data driven warping of the original Euclidean space, they show suitable topological properties, etc. Further two new algorithms, theoretically linked to Auto-CM are presented: the first one is useful to evaluate the complexity and the topological information of any kind of connected graph: the H Function is the index to measure the global hubness of the graph generated by the Auto-CM weights matrix. The second one is named Maximally Regular Graph (MRG) and it is an development of the traditionally Minimum Spanning Tree (MST). Finally, Auto-CM and MRG, with the support of the H Function, are applied to a real complex dataset about Alzheimer disease: this data come from the very known Nuns Study, where variables measuring the abilities of normal and Alzheimer subject during their lifespan and variables measuring the number of the plaques and of the tangles in their brain after their death. The example of the Alzheimer data base is extremely useful to figure out how this new approach can help to re design bottom-up the overall structure of factors related to a complex disease like this.

International experience on the use of artificial neural networks in gastroenterology.

In this paper, we reconsider the scientific background for the use of artificial intelligence tools in medicine. A review of some recent significant papers shows that artificial neural networks, the more advanced and effective artificial intelligence technique, can improve the classification accuracy and survival prediction of a number of gastrointestinal diseases. We discuss the 'added value' the use of artificial neural networks-based tools can bring in the field of gastroenterology, both at research and clinical application level, when compared with traditional statistical or clinical-pathological methods.

Assessment of the role of genetic polymorphism in venous thrombosis through artificial neural networks.

PURPOSE: To assess the role of genetic polymorphisms in venous thrombosis events (VTE) using Artificial Neural Networks (ANNs), a model for solving non-linear problems frequently associated with complex biological systems, due to interactions between biological, genetic and environmental factors. METHODS: A database was generated from a case-control study of venous thrombosis, using 238 patients and 211 controls. The database of 64 variables included age, gender and a panel of 62 genetic variants. Three different ANNs were compared, with logistic regression for the accuracy of predicting cases and controls. RESULTS: ANNs yielded a better performance than the logistic regression algorithm. Indeed, through ANNs models, the 62 variables related to genetic variants were first reduced to a set of 9, and then of 3 (MTHFR 677 C/T, FV arg506gln, ICAM1 gly214arg). CONCLUSIONS: The findings of this study illustrate the power of ANN in evaluating multifactorial data, and show that the different sensitivities of the models of elaboration are related to the characteristics of the data. This may contribute to a better understanding of the role played by genetic polymorphisms in VTE, and help to define, if possible, a test panel of genetic variants to estimate an individual's probability of developing the disease.

An optimized experimental protocol based on neuro-evolutionary algorithms application to the classification of dyspeptic patients and to the prediction of the effectiveness of their treatment.

OBJECTIVE: This paper aims to present a specific optimized experimental protocol (EP) for classification and/or prediction problems. The neuro-evolutionary algorithms on which it is based and its application with two selected real cases are described in detail. The first application addresses the problem of classifying the functional (FD) or organic (OD) forms of dyspepsia; the second relates to the problem of predicting the 6-month follow-up outcome of dyspeptic patients treated by helicobacter pylori (HP) eradication therapy. METHODS AND MATERIAL: The database built by the multicentre observational study, performed in Italy by the NUD-look Study Group, provided the material studied: a collection of data from 861 patients with previously uninvestigated dyspepsia, being referred for upper gastrointestinal endoscopy to 42 Italian Endoscopic Services. The proposed EP makes use of techniques based on advanced neuro-evolutionary systems (NESs) and is structured in phases and steps. The use of specific input selection (IS) and training and testing (T and T) techniques together with genetic doping (GenD) algorithm is described in detail, as well as the steps taken in the two benchmark and optimization protocol phases. RESULTS: In terms of accuracy results, a value of 79.64% was achieved during optimization, with mean benchmark values of 64.90% for the linear discriminant analysis (LDA) and 68.15% for the multi layer perceptron (MLP), for the classification task. A value of 88.61% was achieved during optimization for the prediction task, with mean benchmark values of 49.32% for the LDA and 70.05% for the MLP. CONCLUSIONS: The proposed EP has led to the construction of inductors that are viable and usable on medical data which is representative but highly not linear. In particular, for the classification problem, these new inductors may be effectively used on the basal examination data to support doctors in deciding whether to avoid endoscopic examinations; whereas, in the prediction problem, they may support doctors' decisions about the advisability of eradication therapy. In both cases the variables selected indicate the possibility of reducing the data collection effort and also of providing information that can be used for general investigations on symptom relevance.

Improved artificial neural networks in prediction of malignancy of lesions in contrast-enhanced MR-mammography.

The aim of this study was to evaluate the capability of improved artificial neural networks (ANN) and additional novel training methods in distinguishing between benign and malignant breast lesions in contrast-enhanced magnetic resonance-mammography (MRM). A total of 604 histologically proven cases of contrast-enhanced lesions of the female breast at MRI were analyzed. Morphological, dynamic and clinical parameters were collected and stored in a database. The data set was divided into several groups using random or experimental methods [Training & Testing (T&T) algorithm] to train and test different ANNs. An additional novel computer program for input variable selection was applied. Sensitivity and specificity were calculated and compared with a statistical method and an expert radiologist. After optimization of the distribution of cases among the training and testing sets by the T & T algorithm and the reduction of input variables by the Input Selection procedure a highly sophisticated ANN achieved a sensitivity of 93.6% and a specificity of 91.9% in predicting malignancy of lesions within an independent prediction sample set. The best statistical method reached a sensitivity of 90.5% and a specificity of 68.9%. An expert radiologist performed better than the statistical method but worse than the ANN (sensitivity 92.1%, specificity 85.6%). Features extracted out of dynamic contrast-enhanced MRM and additional clinical data can be successfully analyzed by advanced ANNs. The quality of the resulting network strongly depends on the training methods, which are improved by the use of novel training tools. The best results of an improved ANN outperform expert radiologists.

Contribution of artificial neural networks to the classification and treatment of patients with uninvestigated dyspepsia.

OBJECTIVES: To verify whether symptoms reported by patients with uninvestigated dyspepsia might be helpful in either classifying functional from organic dyspepsia (1st experiment), or recognising which Helicobacter pylori infected patients may benefit from eradication therapy (2nd experiment). METHODS: We compared the performance of artificial neural networks and linear discriminant analysis in two experiments on a database including socio-demographic features, past medical history, alarming symptoms, and symptoms at presentation of 860 patients with uninvestigated dyspepsia enrolled in a large observational multi-centre Italian study. RESULTS: In the 1st experiment, the best prediction for organic disease was given by the Sine Net model (specificity of 87.6% with 13 patients misclassified) and the best prediction for functional dyspepsia by the FF Bp model (sensitivity of 83.4% with 56 patients misclassified). The highest global accuracy of linear discriminant analysis was 65.1%, with 150 patients misclassified. In the 2nd experiment, the highest predictive performance was provided by the SelfDASn model: all infected patients who became symptom-free after successful eradicating treatment were correctly classified, whereas nine errors were made in forecasting patients who did not benefit from such a therapy. The highest global performance of linear discriminant analysis was 53.2%, with 37 patients misclassified. CONCLUSIONS: In patients with uninvestigated dyspepsia, artificial neural networks might have potential for categorising those affected by either organic or functional dyspepsia, as well as for identifying all Helicobacter pylori infected dyspeptic patients who will benefit from eradication.

Multicenter comparative evaluation of six commercial systems and the national committee for clinical laboratory standards m27-a broth microdilution method for fluconazole susceptibility testing of Candida species.

Fluconazole susceptibility among 800 clinical Candida isolates (60% C. albicans) and two control strains (C. krusei ATCC 6258 and C. parapsilosis ATCC 22019) was tested with the NCCLS M27-A method (gold standard) and six commercial products (Candifast, disk, Etest, Fungitest, Integral System Yeasts, and Sensititre YeastOne). Results were classified as susceptible, susceptible-dose dependent, or resistant using M27-A breakpoints or, for Fungitest, Integral System Yeasts, and Candifast, as susceptible, intermediate, or resistant, according to the manufacturers' instructions. Concordance with NCCLS M27-A results was analyzed with the chi(2) test. Intra- and interlaboratory reproducibility was also evaluated. NCCLS M27-A (90.1%), Etest (93.1%), Sensititre YeastOne (93.1%), disk (96.7%), Fungitest (92.6%), Integral System Yeasts (40.6%), and Candifast (6.0%) classified the indicated percentages of C. albicans isolates as susceptible. Among non-C. albicans strains, the percentages of susceptible isolates were as follows: NCCLS M27-A, 74.0%; Etest, 83.8%; Sensititre YeastOne, 64.1%; disk, 60.6%; Fungitest, 76.6%; Integral System Yeasts, 28.3%; and Candifast, 27.4%. All methods except Candifast and Integral System Yeasts showed good agreement with NCCLS M27-A results for both C albicans and non-C. albicans isolates. Intralaboratory reproducibility was excellent for NCCLS M27-A, Etest, Sensititre YeastOne, disk, and Fungitest (88 to 91%). Similar results emerged from the interlaboratory reproducibility evaluation. Our findings indicate that some commercial methods can be useful for fluconazole susceptibility testing of clinical Candida isolates. Those characterized by a lack of medium standardization and/or objective interpretative criteria should be avoided. Particular caution is necessary when testing is being done for clinical and epidemiological purposes.

Artificial neural network assessment of substitutive pharmacological treatments in hospitalised intravenous drug users.

Artificial neural networks (ANNs) provide better solutions than linear discriminant analysis (LDA) to problems of classification and estimation involving a large number of non-homogeneous (categorical and metric) variables. In this study, we compared the ability of traditional LDA and a feed-forward back-propagation (FF-BP) ANN with self-momentum to predict pharmacological treatments received by intravenous drug users (IDUs) hospitalised for coexisting medical illness. When medical staff considered detoxification appropriate they usually suggested methadone (MET) and (or) benzodiazepines (BDZ). Given four different treatment options (MET, BDZ, MET+BDZ, no treatment) as dependent variables and 38 independent variables, the FF-BP ANN provided the best prediction of the consultant's decision (overall accuracy: 62.7%). It achieved the highest level of predictive accuracy for the BDZ option (90.5%), the lowest for no treatment (29.6), often misclassifying no treatment as BDZ. The LDA yielded a lower mean accuracy (50.3%). When the untreated group was excluded, ANN improved its absolute recognition rate by only 1.2% and the BDZ group remained the best predicted. In contrast, LDA improved its absolute recognition rate from 50.3 to 58.9%, maximum 65.7% for the BDZ group. In conclusion, the FF-BP ANN was more accurate than the statistical model (discriminant analysis) in predicting the pharmacological treatment of IDUs.

Toward an integrative approach in the analysis of dependency problems.

This article takes some preliminary steps towards an integrated analysis of dependency problems e.g., long-term tranquillizer use, alcohol dependence, problematic use of narcotics. It argues for the need to outline important theoretical, epistemological, and methodological prerequisites in the analysis of the complex dynamic developmental processes involved in dependency problems. The dynamic process leading to dependence can be studied by the aid of an artificial science neural network approach in combination with a mixed method strategy including a clarification of a combination of different epistemological positions. It is intended that the empirical output of this complex strategy will provide a starting point for a new theoretical analysis which, in turn, will lead to new and more relevant input variables in the neural network approach that will help us to extend our knowledge of the dynamic processes leading to dependency.

Artificial neural networks for drug vulnerability recognition and dynamic scenarios simulation.

Semeion researchers have developed and used different kinds of Artificial Neural Networks (ANN) in order to process selected, "standard" data coming from drug users and from people who never used drugs before. In the first step a collection of 112 general variables, not traditionally connected to drug user's behavior, were collected from a sample of 545 people (223 heroin addicted and 322 non-users). Different types of ANNs were used to test the capability of the system to classify the drug users and the non-drug users correctly. A special ANN tool, created by Semeion, was also used to prune the number of the independent variables. The ANN selected for this first experiment was a Supervised Feed Forward Network, whose equations were enhanced by Semeion researchers. For the validation of the capability of generalization of the ANN, the Training-Testing protocol was used. This ANN was able, in the Testing phase, to classify approximately 95% of the sample with accuracy. A special sensitivity tool selected only 47 among the 112 independent variables as necessary to train the ANN. In the second step, different types of ANN were tested on the new 47 variables to decide which kind of ANN was better able to classify the sample. This benchmark included the following ANNs: a) Back Propagation with Soft Max; b) Learning Vector Quantization; c) Logicon Projection; d) Radial Basis Function; e) Squash (Semeion Network); f) Fuzzy Art Map; g) Modular Neural Network. In the third step a Constraint Satisfaction Network, specifically created by Semeion, was used to simulate a dynamic fuzzy map of the drug user's world; that is, which fuzzy, or approximate, variables are critical to decide the fuzzy membership of a subject from the fuzzy membership of the drug users to the fuzzy membership of non-users and vice versa.

Application of artificial neural networks to eating disorders.

An experimental application of Artificial Neural Networks to Eating Disorders is presented. The sample, composed of 172 cases (all women) collected at the Centre for the Diagnosis and Treatment of Eating Disorders of the 1st Medical Division of the St. Eugenio Hospital of Rome, was subdivided, on the basis of the diagnosis made by the specialist of the St. Eugenio, into four classes: Anorexia Nervosa (AN), Nervous Bulimia (NB), Binge Eating Disorders (BED) and Psychogenic Eating Disorders that are Not Otherwise Specified (PED-NOS). The data base was composed of 124 different variables: generic information, alimentary behavior, eventual treatment and hospitalization, substance use, menstrual cycles, weight and height, hematochemical and instrumental examinations, psychodiagnostic tests, etc. The goal of this experiment was to verify the accuracy of the Neural Networks in recognising anorexic and bulimic patients. This article describes 6 experiments, using a Feed Forward Neural Network, each one using different variables. Starting from only the generic variables (life styles, family environment, etc.) and hematoclinical and instrumental examinations, a Neural Networks provided 86.94% of the prediction precision. This work is meant to be a first contribution to creating diagnostic procedures for Eating Disorders, that would be simple and easy-to-use by professionals who are neither psychologists nor psychiatrists nor psychotherapists but who are, however, among the first to meet these patients and who are therefore called upon to give such patients the very first pieces of advice on seeking proper treatment.

A neural network investigation of the crucial facets of urban sustainability.

This paper focuses on the concept of a sustainable city and its theoretical implications for the Italian urban system. Urban sustainability is based on positive interactions among three different urban subsystems: social, economic and physical, where social well-being coexists with economic development and environmental quality. This utopian scenario does not appear in the existing cities. The aesthetic quality of natural and man-made environment is often associated with marginality and poverty, labor market variety and urban efficiency coexisting with pollution, criminality and high settlement costs. Moreover, since each city differs institutionally, historically, culturally and economically, few attempts have been implemented to build a comparative synthetic vision of the urban sustainability in different cities. The interactions among these selected systems are complex and unpredictable and present the opportunity for a new methodology of scientific investigation: the connectionistic approach. The dual aim of this study is to: investigate the underlying relationships among the three subsystems with a set of social, economic and physical attributes of the chief towns of a Province in Italy and ; verify if this underlying structure could reproduce the heterogeneity of urban realities, allowing one to distinguish groups of cities with different assets or drawbacks in their sustainability. The Data Base (DB), composed of 43 attributes for 95 cities, was processed by Self-Reflexive Neural Networks (SRNN) (Buscema, 1995). These Networks are a useful instrument of investigation and analogic questioning of the Data Base. Once the SRNN has learned the structure of the weights from the DB, by querying the network with the maximization or minimization of specific groups of attributes, it is possible to read the related properties and to rank the cities' urban profile.