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Liquid chromatography tandem mass spectrometry (LC-MS/MS) is routinely used in many clinical applications, including toxicology, drug monitoring, endocrinology, microbiology, and proteomics, thanks to its versatility and effectiveness in the determination of small molecules. The standard approach for analyzing spectral data is the spectral matching that is based on creating a library of annotated spectra against which individual spectrum can be searched for. Building an own spectral library is time-consuming and dependent on the LC-MS/MS instrumentation used, whereas freely available libraries only cover a limited number of molecules. In many cases and especially with newly synthetized substances, the spectrum could not be found in any spectral databases. In-silico fingerprints are binary vectors that, through a hashing algorithm, encode features of molecules. Due to their easy computation and instrument-independence, in-silico fingerprint databases are bigger and more updated than spectral ones. Prediction of molecular fingerprints starting from the LC-MS/MS spectra would consequently assist the match of any target compound under investigation, which would benefit from the increased dimension of fingerprint databases. In this study, we developed a multi-task neural network able to predict molecular fingerprints starting from the LC-MS/MS spectra. We initially collected and pruned around 70’000 MS spectra from available sources (MassBank of North America). For each compound, fingerprints were calculated (MACCS167 keys and Dragon ECFP of 512 bits) and then multi-task feedforward neural networks were trained to predict the binary bits of molecular fingerprints. Models were validated through specific validation protocols and demonstrated to have suitable performances in terms of predictive accuracy (>85% of bits correctly predicted). Thus, the proposed networks can represent a potential approach for the development of a reliable method to enhance matching of MS spectral data to larger molecular databases. Moreover, with respect to recent studies, the proposed model is simple and works with spectra obtained in different experimental conditions.

In many pattern recognition methods, numerical features for each sample should be represented as a vector to the learning algorithm and generally the data can be arranged in a two dimensional array. This could be a challenging issue if we have an array of features, say a matrix per sample which results in a three dimensional data array. The MOLMAP (MOLecular Map of Atom-level Properties) approach was originally introduced to deal with three dimensional data arrays and calculate molecular descriptors. The MOLMAP approach is based on selforganizing map (SOM) and needs to have a predefine network structure which is not easily decidable. We presented a dynamic MOLMAP approach based on Growing Self-Organizing Map (GSOM) for classification of three-way data set. The proposed approach produces an informative MOLMAP-score which can used to learn a classifier. The potential of the proposed method was evaluated using two analytical datasets, electronic-nose and fluorescence. The final classification models were built using XYfused neural network and evaluated by10-fold cross validation. The results show that the Dynamic MOLMAP outperforms the classical one at the same number of neurons in term of classification accuracy. The proposed approach not only have less tunable parameters but also can be used to exploratory analysis and inspecting feature space.

Breast muscle samples from capons fed with diets containing either soy oil or lard were analysed in order to identify the added fat source and thus recognise a possible fraud. Among extracted total lipids, neutral (triacylglycerol and free fatty acids) and polar (phosphatidyl-ethanolamine, -choline and -serine+inositol) fractions were separated by thin-layer chromatography and their fatty acid percentages were determined. The data, subjected to principal component analysis, demonstrated that capons can be distinguished according to the diet only by the fatty acid content of the phosphatidyl-choline fraction.

Multivariate chemometric techniques, such as Principal Component Analysis and Discriminant Analysis, were previously used to determine the authenticity of the Cypriot traditional spirit Zivania, but these techniques revealed difficulties in making this characterization. In the present paper, a non-linear classification model has been built by means of Counterpropagation Artificial Neural Networks. The aim of this model is the characterization of Zivania and the differentiation of this alcoholic beverage from other, similar, beverages from all over the world, especially Europe. This procedure may be an ideal tool for describing Zivania's uniqueness, since the mapping based on the Neural Networks has shown acceptable predictive capabilities. Moreover, the role of each variable in the classification model has been considered: Counterpropagation Artificial Neural Network results have been analysed by means of Principal Component Analysis, in order to study which variables have a real discriminant role in the classification model. This procedure appeared as a promising tool to study the relationship between variables and classes in a global way and not variable by variable, and to obtain a multivariate overview of variable behaviour in the classification model. © 2006 Elsevier B.V. All rights reserved. 87 1 78 84 Cited By :15

Aim. Hyperhidrosis is a disabling troublesome characterized by excessive perspiration that produces a significant social and professional deficit for patients. It main involves the palmar sides of hands and feet, axillae, face and, with a lesser incidence, trunk and scalp. Thoracoscopic sympathectomy represents the best approach for this disorder. The aim of our prospective study is to assess the outcome, the safety and efficacy after thoracoscopic sympathetic block by clipping in the treatment of primary hyperhidrosis. Methods. Between september 2002 and april 2005, 281 patients (117 female and 164 male, with a mean age of 28 years) underwent Endoscopic Thoracic Sympathectomy (ETS) to treat hyperhidrosis with palmar, axillar, facial and widespread localization. Results. There were no deaths or major intraoperative problems. There was no conversion to open surgery. During intermediate and long-term follow up we obtained excellent results and few complications. In 3 patients (1.06%) we noted a major complication (Horner's Syndrome), but one patient only had the surgical operation reversed; compensatory sweating occurred in 61 individuals (21.7%). Gustatory sweating occurred in no patient. Conclusions. The clipping of the thoracic sympathetic chain is a reversible surgical technique considered safe and effective. Despite of an incidence of complications and/or compensatory sweating and according to our operatory results, we conclude that the sympathetic block by clamping is the elective treatment for hyperhidrosis.

Aim. Facial blushing represents the peculiar symptom in social phobia and is defined as the "hallmark of embarrassment". At present, endoscopie thoracic sympathectomy (ETS) is the technique only able to guarantee longterm results in the treatment of sympathetic disorders. The aim of our study was to evaluate results, complications and the true value of 52 ETSs by two-port approach with the use of endoclips in patients with isolated facial blushing or in association with hyperhidrosls. Methods. A retrospective review of 52 subjects who underwent standard ETS by clamping between September 2002 and April 2005 was carried out. This surgical practice was performed in 26 cases (50%) for isolated facial blushing only and in other 26 cases (50%) for facial blushing associated to hyperhidrosfe with changeable localizations. Under endoscopie guidance we operated an endoscopie sympathetic block (ESB) with interruption of the intergangliar trunk at the level of T2-T3 in 51 patients (98.07%). In 1 patient (1.92%) we performed a sympathetic block with the complete exclusion of T3 thoracic ganglion. Results. There were no intra and postoperative deaths. The effect rate was excellent in all patients with early complications in one patient (1.92%) only. Conclusions. ETS by clamping is safe and effective. Patients referred an high satisfaction in terms of complications and quality of life; specific early and late diseases are neglectables