Filippo Amato

Development and validation of a general approach to predict and quantify the synergism of anti-cancer drugs using experimental design and artificial neural networks.

The combination of two or more drugs using multidrug mixtures is a trend in the treatment of cancer. The goal is to search for a synergistic effect and thereby reduce the required dose and inhibit the development of resistance. An advanced model-free approach for data exploration and analysis, based on artificial neural networks (ANN) and experimental design is proposed to predict and quantify the synergism of drugs. The proposed method non-linearly correlates the concentrations of drugs with the cytotoxicity of the mixture, providing the possibility of choosing the optimal drug combination that gives the maximum synergism. The use of ANN allows for the prediction of the cytotoxicity of each combination of drugs in the chosen concentration interval. The method was validated by preparing and experimentally testing the combinations with the predicted highest synergistic effect. In all cases, the data predicted by the network were experimentally confirmed. The method was applied to several binary mixtures of cisplatin and [Cu(1,10-orthophenanthroline)2(H2O)](ClO4)2, Cu(1,10-orthophenanthroline)(H2O)2(ClO4)2 or [Cu(1,10-orthophenanthroline)2(imidazolidine-2-thione)](ClO4)2. The cytotoxicity of the two drugs, alone and in combination, was determined against human acute T-lymphoblastic leukemia cells (CCRF-CEM). For all systems, a synergistic effect was found for selected combinations.

Laser desorption ionisation quadrupole ion trap time‐of‐flight mass spectrometry of titanium‐carbon thin films

RATIONALE Titanium-carbon (Ti-C) ceramic thin films (abbreviated as n-TiC/a-C:H) are very important for industrial applications. However, their chemical structure is still not completely resolved. The aim of this study was to determine the chemical composition of such n-TiC/a-C:H layers prepared by balanced magnetron sputtering under various experimental conditions. METHODS Mass spectrometric analysis of Ti-C thin films was carried out via laser desorption ionisation (LDI) using a quadrupole ion trap and reflectron time-of-flight analyser. The stoichiometry of clusters formed via laser ablation was determined, and the relative abundances of species for which the isotopic patterns overlaps were estimated using a least-squares program. RESULTS Ti-C films were found to be composites of (i) pure and hydrogenated TiC, (ii) titanium oxycarbides, and (iii) titanium oxides of various degrees of hydrogenation (all embedded in an amorphous and/or diamond-like carbon matrix). Hydrogenated titanium oxycarbide was the main component of the surface layer, whereas deeper layers were composed primarily of TiC and titanium oxides (also embedded in the carbon matrix). CONCLUSIONS Mass spectrometry proved useful for elucidating the chemical structure of the hard ceramic-like Ti-C layers produced by magnetron sputtering. The Ti-C layers were found to be complex composites of various chemical entities. Knowledge of the resolved structure could accelerate further development of these kinds of materials.

Multivariate Calibration Approach for Quantitative Determination of Cell-Line Cross Contamination by Intact Cell Mass Spectrometry and Artificial Neural Networks

Cross-contamination of eukaryotic cell lines used in biomedical research represents a highly relevant problem. Analysis of repetitive DNA sequences, such as Short Tandem Repeats (STR), or Simple Sequence Repeats (SSR), is a widely accepted, simple, and commercially available technique to authenticate cell lines. However, it provides only qualitative information that depends on the extent of reference databases for interpretation. In this work, we developed and validated a rapid and routinely applicable method for evaluation of cell culture cross-contamination levels based on mass spectrometric fingerprints of intact mammalian cells coupled with artificial neural networks (ANNs). We used human embryonic stem cells (hESCs) contaminated by either mouse embryonic stem cells (mESCs) or mouse embryonic fibroblasts (MEFs) as a model. We determined the contamination level using a mass spectra database of known calibration mixtures that served as training input for an ANN. The ANN was then capable of correct quantification of the level of contamination of hESCs by mESCs or MEFs. We demonstrate that MS analysis, when linked to proper mathematical instruments, is a tangible tool for unraveling and quantifying heterogeneity in cell cultures. The analysis is applicable in routine scenarios for cell authentication and/or cell phenotyping in general.

Generation of new Agm Ten clusters via laser ablation synthesis using Ag-Te nano-composite as precursor. Quadrupole ion trap time-of-flight mass spectrometry.

RATIONALE Metal tellurides have applications in various fields of science and technology but only a few gold-silver tellurides have been reported. The laser ablation synthesis (LAS) method allows the preparation of nano-materials from solid substrates. Therefore, this method was selected to synthesise some gold-silver tellurides. METHODS Laser desorption ionisation quadrupole ion trap time-of-flight mass spectrometry (LDI QIT TOF MS) was used for the generation of new Au(p)Ag(q)Te(r) clusters. Scanning electron microscopy (SEM) and energy-dispersive X-ray spectroscopy (EDX) were used to characterise the materials. The stoichiometry of the clusters generated was determined via collision-induced dissociation (CID) and modeling of isotopic patterns. RESULTS Chemisorption of gold and silver nano-particles on tellurium powder led to the formation of a new kind of Au-Ag-Te nano-composite. The LDI of this nano-composite yielded nine unary (Ag(q), Te(r)), 40 binary (Au(p)Te(r) and Ag(p)Te(r)) and 78 ternary clusters. The stoichiometry of these novel Au(p)Ag(q)Te(r) clusters is reported here for the first time. CONCLUSIONS The new Au-Ag-Te nano-composite was found to be a more suitable precursor for the generation of clusters than the mixtures of the elements. TOF MS was shown to be a useful technique for following the generation of gold-silver tellurides. Knowledge of the cluster stoichiometry could accelerate the further development of novel high-tech materials such as chalcogenide glasses.

Remotely sensed soil data analysis using artificial neural networks. A case study of El-Fayoum depression, Egypt

Abstract: Earth observation and monitoring of soil quality, long term changes of soil characteristics and deterioration processes such as degradation or desertification are among the most important objectives of remote sensing. The georeferenciation of such information contributes to the development and progress of the Digital Earth project in the framework of the information globalization process. Earth observation and soil quality monitoring via remote sensing are mostly based on the use of satellite spectral data. Advanced techniques are available to predict the soil or land use/cover categories from satellite imagery data. Artificial Neural Networks (ANNs) are among th e most widely used tools for modeling and prediction purposes in various fields of science. The assessment of satellite image quality and suitability for analysing the soil conditions (e.g., soil classification, land use/cover estimation, etc. ) is fundamental. In this paper, methodology for data screening and subsequent application of ANNs in remote sensing is presented. The first stage is achieved

Laser Ablation Synthesis of Gold Selenides by using a Mass Spectrometer as a Synthesizer: Laser Desorption Ionization Time-of-Flight Mass Spectrometry.

Methods for the rapid construction of new chemical motifs have the potential to accelerate the development of nanoscience. The synthesis of new chemical entities by laser ablation has been systematically demonstrated by using mixtures of gold and selenium. The compounds generated are detected by time-of-flight mass spectrometry and, for selected compounds, the structure is investigated by using density functional theory optimization. In total, 67 new gold selenide clusters have been synthesized, demonstrating an unsuspected richness in gold chemistry. Chemical species generated in the gas phase might inspire new routes for the synthesis of novel compounds in the solid state.

Matrix-assisted laser desorption/ionisation quadrupole ion trap time-of-flight mass spectrometry of novel shape-persistent macrocycles.

RATIONALE Shape-persistent macrocycles (SPMs) represent innovative molecular building blocks for the development of highly organised supramolecular architectures with application in nanotechnology, chemistry, catalysis and optoelectronics. Systematic mass spectrometric characterisation of SPMs and their collision-activated decay is not available to date. METHODS Characterisation of alkoxy-decorated SPMs was performed by matrix-assisted laser desorption/ionisation quadrupole ion trap time-of-flight mass spectrometry (MALDI-QIT-TOFMS) and collision-induced dissociation (CID). RESULTS Laser excitation of SPMs leads to the formation of stable cation radicals which show characteristic fragmentation patterns. All the product ions formed were identified. Photoelectrons generated during the MALDI process and full-ring conjugation were found to be fundamental for the formation of molecular cation radicals and their stabilisation, respectively. Formation of supramolecular aggregates of SPMs by π-π stacking was proven. SPMs are suitable motifs for the preparation of novel fullerene-based donor-acceptor systems. CONCLUSIONS Alkoxy-decorated SPMs represent promising electron-donating building blocks that can be exploited in electronics and optoelectronics for the development of robust and highly efficient laser-activated supramolecular switches.