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The optimal interface between an implant and the host organism is that which minimizes uncontrolled and irregular growth of fibrous tissue around the foreign material. One way to achieve this goal is to coat the outer surface of the implant with a biocompatible material. A thin film of turbostratic carbon (Carbofilm, Sorin Biomedica, Saluggia, Italy) can be applied as a thin coating on tissue expanders and breast implants made of silicone elastomers. The success of biologic trials with such implants in rats and cytologic studies with murine and human fibroblasts encouraged us to employ the Carbofilm technology in clinical situations. Preliminary results indicate that Carbofilm-coated implants satisfy the requirements of an ideal alloplastic material and induce a minimal periprosthetic inflammatory reaction, with the formation of a thin, soft, pliable, fibrous capsule around coated gel-filled silicone implants.

L’articolo presenta e discute i risultati emersi dalla prima sperimentazione di un corso orientato alla promozione e allo sviluppo di competenze nel problem solving aritmetico in alunni del secondo ciclo della scuola primaria. Il corso è stato costruito secondo tecniche di e-learning utilizzando gli strumenti del sistema ARI@ITALES. Tali strumenti permettono di integrare in un corso attività di tipo costruttivo che vengono rese accessibili e utilizzabili attraverso la rete internet. Con questo lavoro si voleva analizzare come un corso del tipo proposto potesse essere utile per l’insegnante nella progettazione e nella gestione dell’attività di classe e valutare l’efficacia del corso e degli strumenti utilizzati nel guidare gli allievi alla costruzione di competenze matematiche.

The paper deals with the development of an online UHPLC-MS/MS method for the identification and determination of 25 Endocrine Disrupting Chemicals (EDCs) belonging to different chemical classes (perfluorochemicals, bisphenols, alkylphenols, phthalates, and parabens). The study regards the possible EDC migration from different medical devices of diverse materials used in the routine hospitals for blood sampling or for the parenteral nutrition therapies. In order to evaluate the release of EDCs, the equipment used for blood sampling (syringes, butterfly, cannula-needle, microcapillary tube) was put in contact with a physiological solution at a prefixed time. As regards the migration tests carried out on the medical devices used in the parenteral nutrition therapy (infusion tubes, venous catheters), the catheters were undergone to internal contact with parenteral solution and external contact with physiological one, whereas the infusion tubes were subjected only to internal migration test with parenteral solution. The results demonstrated the released of some ECDs (such as parabens at the concentration of few pg mL−1, diethylphthalate and dibutylphthlate at ng mL−1, level), when using physiological solution as the leaching agent. In addition, the presence of a PFOS structural isomer was found at 29.0 ng mL-1: this compound was released in part from the infusion tubes and mostly from the venous catheters, particularly from silicone ones. Using a more lipophilic solution, such as parenteral preparations, a greater amount of EDCs was released, among which also the diethylhexylphthalate that was quantified in all the investigated devices (without highlighting particular trends or migration from PVC devices), but its concentration remained always lower than imposed maximum threshold limit on medical device of 0.1% w/w. The releases from the infusion tubes of the same materials (polyethylene) from different manufacturers are very comparable, whereas the catheter of silicone releases greater amount of EDCs than that of polyurethane.

The characterization of 72 Italian honey samples from 8 botanical varieties was carried out by a comprehensive approach exploiting data fusion of IR, NIR and Raman spectroscopies, Proton Transfer Reaction - Time of Flight - Mass Spectrometry (PTR-MS) and electronic nose. High-, mid- and low-level data fusion approaches were tested to verify if the combination of several analytical sources can improve the classification ability of honeys from different botanical origins. Classification was performed on the fused data by Partial Least Squares - Discriminant Analysis; a strict validation protocol was used to estimate the predictive performances of the models. The best results were obtained with high-level data fusion combining Raman and NIR spectroscopy and PTR-MS, with classification performances better than those obtained on single analytical sources (accuracy of 99% and 100% on test and training samples respectively). The combination of just three analytical sources assures a limited time of analysis.

Background: In the last decades particular attention has been devoted to packaging materials to obtain the best conditions to store food and to preserve the organoleptic properties. Methods: In the present study samples of tomato sauce collected in different Italian markets and preserved in different packaging materials (glass, tin and multilayer plastic containers) were chemically characterized considering the content of anions (6 species), carotenoids (lycopene, sum of alpha-and beta-carotene), flavonoids (epicatechin and quercetin), metals (41 species), total polyphenols, vitamins (C and Bgroup), and volatile profile. Different analytical techniques were employed like GC and LC with different detectors (MS and DAD), ICP-MS and ICP-OES. All developed methods were validated and applied to different commercial products collected from local markets. Results: Multivariate statistical tools (Principal Components Analysis – Discriminant Analysis, PCA – DA, Ranking PCA and Partial Least Square – Discriminant Analysis, PLS – DA) were then applied to evaluate the effect played by the different packaging on the chemical profile of the samples. The multivariate strategy adopted allowed to identify markers able to discriminate samples stored in glass, tin or multilayer plastic containers. Conclusion: In both cases, the differences identified between the classes of samples can be ascribed to a double effect: 1) products stored in glass preserve their content in vitamins and other important nutrients or 2) storage in glass could force manufacturers to provide a product of better quality since the product is visible to the final consumer.

We conducted a proteomics study in order to detect the proteomic method which provides the most complete characterization of the proteins of rice milk. In particular, we compared the results obtained from LC-MS/MS after protein precipitation with acetone or TCA, as well as the results obtained from LC-MS/MS after protein prefractionation based on SDS-PAGE (GeLC-MS/MS) or ProteoMiner™ technology (ProteoMiner-LC-MS/MS), and after peptide prefractionation based on IEF (pIEF-LC-MS/MS). A total of 158 protein species have been detect in rice milk. The physical-chemical analysis and classification of the identified proteins were also reported. In particular, we showed that pIEF-LC-MS/MS method led to a significant increase in the proteome coverage, allowing the identification of a total of 96 proteins of milk rice. This study demonstrates the utility of a prefractionation step based on pIEF before the shotgun proteomic analysis and offers an in-depth insight into the rice milk proteome.

An on-line solid phase extraction (SPE) ultra high performance liquid chromatography tandem mass spectrometry method has been developed for the simultaneous identification and determination of thirteen polycyclic aromatic hydrocarbons (PAHs) and twelve aldehydes (derivatized with 2,4-dinitrophenylhydrazine). The chromatographic conditions have been optimized to obtain the maximum of sensitivity and resolution taking into account the different retention interactions and the different ionization conditions of PAHs and derivatized aldehydes. LOD values ranging from 0.028 to 0.768 μg L −1 for PAHs and from 0.002 to 0.125 μg L −1 for aldehydes were obtained. The resolution permitted the separation of four couples of PAH isomers. Sample pre-treatment and SPE were optimized in order to apply the whole methodology to the analysis of different food matrices as salmon, frankfurter, steak, and pork chop, subjected to different cooking modes (smoked, grilled, cooked in oil or in butter). Particular attention was devoted to the evaluation of matrix effect that was significantly reduced through the on-line SPE treatment. For each food matrix the method detection limits, the method quantitation limits, and the recovery R were evaluated. R was shown not to depend on analyte concentration in the explored concentration range (LOQ – 50.000 μg L −1 ): the average R percent ranges from 70.6% to 120.0%.

Standard automatic tuning for mass spectrometry usually exploits a one-variable-at-a-time approach. This method suffers from important drawbacks: the target function selected for optimization improves the signal of a single channel or a pool of channels without considering noise; the interactions between the parameters are not evaluated. The optimization of the experimental settings of an ESI IT mass spectrometer is carried out here by a multivariate procedure exploiting a target function representing the S/N ratio calculated by principal component analysis and a regular simplex optimization algorithm. A preliminary feasibility study was performed since the target function must be sensitive to the changes in the experimental conditions applied during the iterative tuning and be free from drifts. The feasibility study was carried out to evaluate: the presence of memory effects; the size of the variations in the S/N ratio; the number of scans needed to generate a reliable S/N ratio; the concentration of the multi-standard mixture to use during tuning; the experimental duration required to achieve S/N stability when the experimental settings are modified. The feasibility study led to the identification of the best protocol to accomplish the tuning, while simplex optimization allowed the S/N ratio to be improved by about 70% with respect to the default conditions suggested by the manufacturer.

A reversed-phase high-performance liquid chromatography (HPLC) method was developed for the simultaneous determination in food of biogenic amines and their precursor amino acids after a precolumn derivatization with dansyl chloride. The chromatographic conditions, selected to be suitable for mass spectrometry detection, were optimized through experimental design and artificial neural networks. The HPLC-UV method was validated by comparing the separation results with those obtained through a HPLC method, working under the same chromatographic conditions but employing mass spectrometry detection. The HPLC-UV method was then applied to the analysis of different food samples, namely, cheese, clams, salami, and beer. For all of the matrices, recoveries (relative standard deviation always 92% were obtained. The results are discussed as a function of the total biogenic amine content and of the concentration ratio between amines and precursor amino acids

When mass spectrometry is not combined to separation techniques, the evaluation of signal and noise in a complex mass spectrum is not trivial. The tuning of the spectrometer based only on the increase of the signal of a selected number of m/z values does not ensure the achievement of the best experimental conditions: signal could improve and noise could increase as well. The scope of this work is the development of a function separating signal and noise (for evaluating the S/N) from complex mass spectra for potential use as target function for the automatic tuning of the instrument. Two different methods were applied: the first is based on the separation of a pool of m/z values attributable to the signal from the m/z values due to the noise, while the second is based on the application of principal component analysis to separate the signal (present in the significant components) from the noise (present in the residuals). The comparison of the two methods was carried out by the evaluation of the stability of the signal and the target functions obtained, and the evaluation of the variation of the target functions as a function of concentration.

The rate of margins involvement and the associated recurrence risk in basal cell carcinomas (BCCs) varies widely in published works (7%-25% and 26%-67%, respectively). This study investigated the risk factors associated with incomplete excision and their relevance in surgical management when positive margins occur in 3957 BCCs excised in 2358 patients. This study performed a multivariate analysis on the database collected from all patients operated for BCCs in the Plastic Surgery Department between 1 January 1992 and 1 September 2007. All data collected (3957 excisions; 2358 individuals) were divided into complete and incomplete excisions groups and analyzed according to 14 variables. The overall rate of incomplete excisions was 14%. Mean age (68), size of the lesion (< 0.5 cm), BCC subtype (nodular with sclerosant aspects, sclerosant and basosquamous), location (face), infiltration depth (hypodermis and deep tissues), recurrent BCC and re-excised BCC were significantly associated with a higher rate of incomplete excision. The recurrence rate for incompletely excised tumours was 26.8%, while only 5.9% for completely excised tumours. Most of the risk factors associated to incomplete excision can be identified before surgery (by simple anamnesis and clinical examination) and successfully overcome by appropriate surgical margins. The high recurrence rate after incomplete excision and the low patient compliance towards follow-up should lead the surgeon to early re-excise residual cancer.

A new approach for monitoring the state of conservation of cultural heritage objects surfaces is being developed. The technique utilizes multi-spectral imaging, multivariate analysis and statistical process control theory for the automatic detection of a possible deterioration process, its localization and identification, and the wavelengths most sensitive to detecting this before the human eye can detect the damage or potential degradation changes occur. A series of virtual degradation analyses were performed on images of parchment in order to test the proposed algorithm in controlled conditions. The spectral image of a Dead Sea Scroll (DSS) parchment, IAA (Israel Antiquities Authority) inventory plate # 279, 4Q501 Apocryphal Lamentations B, taken during the 2008 Pilot of the DSS Digitization Project, was chosen for the simulation.

A rapid on-line solid phase extraction ultra high performance liquid chromatography tandem mass spectrometry method was developed for the identification and quantitation of nine perfluorinated compounds in matrices of environmental, biological and food interest. Pre-treatment, solid phase extraction, chromatographic and mass detection conditions were optimised, in order to apply the whole methodology to the analysis of different matrices. Particular attention was devoted to the evaluation of matrix effect and the correlated phenomena of ion enhancement or suppression in mass spectrometry detection. LOD and LOQ range from 3 to 15 ng L−1 and from 10 to 50 ng L−1, respectively. Method detection limits (MDLs) were also calculated for each kind of matrix. The recovery, evaluated for each analyte, does not depend on analyte concentration in the explored concentration range: average R ¯ % values are always greater than 82.9%. In the whole, the results obtained for samples of river waters, blood serum, blood plasma, and fish confirm the ubiquitous presence of perfluorinated compounds, as recently denounced by many sources.

The identification of biomarkers is one of the leading research areas in proteomics. When biomarkers have to be searched for in spot volume datasets produced by 2D gel-electrophoresis, problems may arise related to the large number of spots present in each map and the small number of samples available in each class (control/pathological). In such cases multivariate methods are usually exploited together with variable selection procedures, to provide a set of possible biomarkers: they are however usually aimed to the selection of the smallest set of variables (spots) providing the best performances in prediction. This approach seems not to be suitable for the identification of potential biomarkers since in this case all the possible candidate biomarkers have to be identified to provide a general picture of the "pathological state'': in this case exhaustivity has to be preferred to provide a complete understanding of the mechanisms underlying the pathology. We propose here a ranking and classification method, "Ranking-PCA'', based on Principal Component Analysis and variable selection in forward search: the method selects one variable at a time as the one providing the best separation of the two classes investigated in the space given by the relevant PCs. The method was applied to an artificial dataset and a real case-study: Ranking-PCA exhaustively identified the potential biomarkers and provided reliable and robust results