This workF., Costantini presents a portable lab-on-chip system, basedR. M., Tiggelaar on thin film electronic devicesR., Salvio and an all-glass microfluidic network, forM., Nardecchia the real-time monitoring of enzymatic chemiluminescent reactions. The microfluidic networkS., Schlautmann is patterned, through wet etching, in a 1.1 mm-thick glass substrate that is subsequently bonded to a 0.5 mm-thick glass substrate. The electronic devices are amorphousC., Manetti silicon p-i-n photosensors, deposited on the outer side of the thinner glass substrate. The photosensorsH. J. G. E., Gardeniers, the microfluidic network and the electronic boards reading out the photodiodes’ currentD., Caputo are enclosed in a small metallic box ( 10 × 8 × 15 cm^3 ) in order to ensure shielding from electromagnetic interferences. Preliminary tests haveA., Nascetti been performed immobilizing horseradish peroxidase on the inner wall of the microchannel as model enzyme for detecting hydrogen peroxide. Limits of detection and quantificationG., de Cesare equal to 18 and 60 M, respectively, have been found. These values are comparable to the best performances reported in literature for chemiluminescent-based optofluidic sensors.

Background Metabolomics belongs to the family of “-omics” sciences, all of which share the advantage of a non-targeted approach for identifying biomarkers and profiling the patient. Metabolomic procedure has become feasible recently with the advent and accessibility of new high-throughput technologies, including mass spectrometry and 1H Nuclear Magnetic Resonance (1H NMR) and a few studies have been published in rheumatic disorders. Objectives To evaluate whether a 1H NMR-based metabolomic analysis in serum from patients with rheumatoid arthritis (RA) could predict the response to etanercept evaluated at 6 months. Methods Adult patients fulfilling the 1987 ACR revised criteria for the classification of RA and designated to start anti-TNF therapy were prospectively enrolled. The analysis was restricted to female patients with active disease starting etanercept as the first biological treatment and having a minimum of 6 months9 follow-up. Each patient was evaluated by the same rheumatologist at baseline before starting etanercept and after 6 months following the onset of biological treatment. DAS28 was calculated and the clinical response (good, moderate, none) was evaluated according to the EULAR criteria, based on both erythrocyte sedimentation rate (EULAR-ESR) and C-reactive protein (EULAR-CRP). We merged good and moderate categories as response in comparison with no response. Sera collected prior to the onset of etanercept were analyzed via 1H NMR-based metabolomics. Discriminating metabolites were identified, and the relationship between metabolic profiles and clinical outcomes was assessed. Results Twenty-seven patients were included (mean age 57.8 years, ± SD 12.5; mean disease duration 102.5 months, ± SD 78.05). Eighteen patients had a good/moderate response and 9 were non responders according to both EULAR-ESR and EULAR-CRP after 6 months of etanercept. Baseline serum metabolic profiles discriminated between RA patients who did or did not have a response to etanercept. Unsupervised Principal Component Analysis (PCA) results according to EULAR-ESR or EULAR-CRP response criteria highlighted significant differences between the metabolic profiles of responders and non responders (p<0.0001), but failed to discriminate between good and moderate responders. Comorbidity moderately influenced the clustering of metabolic profiles (p=0.041), as confirmed by partial correlation analysis results. Conversely, age, smoking, dietary habits, and therapy were not found to be confounding factors. Supervised model, Orthogonal Projections to Latent Structures-Discriminant Analysis (OPLS-DA), enabled discrimination between good responders, moderate responders and non-responders according to EULAR-ESR criteria with a very good predictivity (index of predictivity Q2=0.68) and an excellent sensitivity (100%), specificity (100%), and accuracy (100%). Conclusions Metabolomics approach is a potentially useful technique for predicting the response of RA patients to etanercept, as demonstrated by differences in serum metabolic profiles at baseline. This observation deserves further investigation in larger cohorts of patients to confirm the capability of predicting clinical response without the need for empirical treatment. Disclosure of Interest : None declared DOI 10.1136/annrheumdis-2014-eular.5080

A lab-on-chip for the diagnosis of celiac disease relying on the monitoring of patient-specific immune response to gliadin fractions has been developed. The detection is based on a chemiluminescent immunoenzymatic reaction that ensures high specificity and sensitivity. The chemiluminescent signal is monitored by hydrogenated amorphous silicon photosensors, fabricated on the same glass substrate hosting the biochemical recognition. The main challenge of the work has been the identification of the materials and the setup of the entire process that permitted the reliable fabrication of the device. Experiments performed with serum samples of rabbit immunized towards an epitope show a good specificity of the proposed technique, proving the feasibility of an integrated device for the patient-specific profiling.

In this work we report on a system for food quality analysis based on the use of a "smart" thin layer chromatography plate, which couples an amorphous silicon photosensor array to a chromatographic plate. The system is able to give quantitative information by measuring the fluorescence of the analyte during the chromatographic run. In particular, the system has been tested to detect and quantify aflatoxins B-1, B-2, G(1) and G(2). The aflatoxin quantity is proportional to the photocurrent flowing in the p-i-n amorphous silicon diode junction, as the effect of the analyte fluorescence excited by a 365 nm UV radiation. Characterization performed diluting 5-20 ng of aflatoxin in 2 mu l of methanol demonstrate the capability of the system to detect in real time the analyte separation and to quantify the toxin.

In this paper, we present a compact lab-on-chip system (LOC) fabricated on a conventional microscope glass slide using thin-film and thick-film technologies at “La Sapienza” University of Rome by the integration of know-how of researchers from Department of Electronic Engineering, Department of Aerospace and Astronautics Eng., Department of Chemistry and Department of Plant Biology. It integrates a heating chamber, an electrowetting-based droplet handling system and hydrogenated amorphous silicon (a-Si:H) photosensor array for biomolecule detection. The heating chamber incorporates a thin metal film heater whose geometry has been optimized for uniform temperature distribution over a 1cm2 area. An a-Si:H p-i-n junction integrated with the heater and biased with a forward current acts as temperature monitoring, achieving a sensitivity -3.3 mV/K with a linear behavior in the investigated range. The droplet-handling unit, relying on the electrowetting method, is designed to move the sample from the heating chamber to the sensor array. The unit includes a set of metal pads beneath a layer of PDMS that provides both the electric insulation of the electrodes and the hydrophobic surface needed by the electrowetting technique. The detection unit has been applied to quantify Ochratoxin A (OTA) based on hydrogenated amorphous silicon (a-Si:H) sensors. 2 μl of acidified toluene containing OTA at different concentrations were spotted on the silica side of a High Performance Thin Layer Cromatography plate and aligned with a a-Si:H p-i-n photodiode deposited on the LOC. Results show a very good linearity between OTA concentration and the sensor photocurrent down to 0.1ng, showing that the presented system has the potential for a low cost system suitable for the early detection of toxins. To extend the application of LOC to the analysis of real matrices the group are developing new chemical strategies, that are also presented in this paper.

In this work, we present a 'smart thin layer chromatography plate': a system which couples a linear array of amorphous silicon p–i–n photodiodes with a TLC plate. The basic idea is to monitor in real-time the separation of the components of the mixture during the chromatographic run. These are achieved by measuring the natural fluorescence/absorption of the compounds induced by UV radiation. The 'smart TLC plate' has been tested on several mixtures of naturally fluorescent molecules proving the suitability of our system to provide qualitative and quantitative information on their composition. In particular, quantification performance has been verified comparing the intensity of the sensor photocurrent measured at different quantities of fluorescein spotted on the TLC plate. An excellent linearity has been achieved.

We present an innovative thin layer chromatography plate, which integrates a linear array of amorphous silicon photodiodes for real-time qualitative and quantitative chromatographic analysis.

In this work, we present a novel thin layer chromatography system based on fluorescence detection by means of a linear array of amorphous silicon photodiodes. The photodiodes are optically coupled to a thin layer chromatography plate to monitor, in real-time, the separation of the components of a mixture during the chromatographic run. We designed a horizontal development chamber with UV transparent window and integrated eluent tank. The resulting system is extremely compact and ensures fast analysis using only small amounts of eluent. With our system, we analyzed different mixtures of fluorescent inks and other molecules such as fluorescein. Real-time data of sensors located in different position are used to detect the composition of the mixture. The various components can be determined from the time of the transit of the different species in front of the sensors. Furthermore, from the measured data it is possible to extract additional information as the transport properties of the stationary phase or the velocity profile along the run for each component of the mixture.

Metabolic profiling is defined as the simultaneous assessment of substrate fluxes within and among the different pathways of metabolite synthesis and energy production under various physiological conditions. The use of stable-isotope tracers and the analysis of the distribution of labeled carbons in various intermediates, by both mass spectrometry and NMR spectroscopy, allow the role of several metabolic processes in cell growth and death to be defined. In the present paper we describe the metabolic profiling of Jurkat cells by isotopomer analysis using (13)C-NMR spectroscopy and [1,2-(13)C(2)]glucose as the stable-isotope tracer. The isotopomer analysis of the lactate, alanine, glutamate, proline, serine, glycine, malate and ribose-5-phosphate moiety of nucleotides has allowed original integrated information regarding the pentose phosphate pathway, TCA cycle, and amino acid metabolism in proliferating human leukemia T cells to be obtained. In particular, the contribution of the glucose-6-phosphate dehydrogenase and transketolase activities to phosphoribosyl-pyrophosphate synthesis was evaluated directly by the determination of isotopomers of the [1'-(13)C], [4',5'-(13)C(2)]ribosyl moiety of nucleotides. Furthermore, the relative contribution of the glycolysis and pentose cycle to lactate production was estimated via analysis of lactate isotopomers. Interestingly, pyruvate carboxylase and pyruvate dehydrogenase flux ratios measured by glutamate isotopomers and the production of isotopomers of several metabolites showed that the metabolic processes described could not take place simultaneously in the same macrocompartments (cells). Results revealed a heterogeneous metabolism in an asynchronous cell population that may be interpreted on the basis of different metabolic phenotypes of subpopulations in relation to different cell cycle phases.

Because of their peculiar physico-chemical properties, alginate beads have often been proposed as an alternative cell immobilization matrix for many biotechnological applications. For entrapped hepatocytes perfused in a bioreactor, alginate beads have been demonstrated to promote viability and three-dimensional cell organization. In order to optimise the hepatocyte cell culture, we investigated the relationship between alginate beads properties, at high and low content of guluronic acid (G), and the relative cell viability and reorganization when perfused in a bioreactor. The primary structure of alginates did not apparently influence the hepatocytes culture in 8 h of perfusion in a bioreactor. However, our results confirm a preference for beads with a high content of G due to their superior mechanical resistance.

In a previous article (Zbilut et al., Biophys J 2003;85:3544–3557), we demonstrated how an aggregation versus folding choice could be approached considering hydrophobicity distribution and charge. In this work, our aim is highlighting the mutual interaction of charge and hydrophobicity distribution in the aggregation process. Use was made of two different peptides, both derived from a transmembrane protein (amyloid precursor protein; APP), namely, Aβ(1‐28) and Aβ(1‐40). Aβ(1‐28) has a much lower aggregation propensity than Aβ(1‐40). The results obtained by means of molecular dynamics simulations show that, when submitted to the most “aggregation‐prone” environment, corresponding to the isoelectric point and consequently to zero net charge, both peptides acquire their maximum flexibility, but Aβ(1‐40) has a definitely higher conformational mobility than Aβ(1‐28). The absence of a hydrophobic “tail,” which is the most mobile part of the molecule in Aβ(1‐40), is the element lacking in Aβ(1‐28) for obtaining a “fully aggregating” phenotype. Our results suggest that conformational flexibility, determined by both hydrophobicity and charge effect, is the main mechanistic determinant of aggregation propensity. Proteins 2005. © 2004 Wiley‐Liss, Inc.

Hydrogel contact lenses swollen in viscoelastic artificial tears solution have been studied, measuring transversal relaxation times of water molecules using LF-NMR techniques. Data were processed by classical multiexponential fitting, by principal component analysis and by SLICING, a multi-way analysis method. The reason for using multivariate data analysis was not to obtain a better fitting, but rather more effective data description. The single-sample relaxation curves were projected in a space spanned by the loading curves, and in this space it was simpler to compare data. In particular, it contributed to the description of the variability of motion characteristics of the water molecule 'families' contained in the studied samples. Applying multivariate techniques, we were able to group lenses with different Equilibrium Water Content (EWC) and with the same water content but different compositions. Accordingly, we were able to point out that, if the lenses are swollen first in physiological solution and then in viscoelastic artificial tears solution, hydration characteristics remained unchanged in all the studied samples, except for 38% EWC lenses.

The different swelling properties of ionic and non-ionic lenses, immersed in sodium chloride or artificial tears, were investigated by Low-Field Nuclear Magnetic Resonance (LF-NMR) through relaxation time and self-diffusion coefficient measurements. A new model was developed to estimate the water self-diffusion coefficient inside the lenses, the results of which revealed a different mechanism of interaction for ionic versus non-ionic materials with artificial tears. The consequence of this finding suggests clinical implications.

The aim of this research was to verify the possibility of identifying and classifying maize seeds obtained from transgenic plants, in different classes according to the modification, on the basis of the concerted variation in metabolite levels detected by NMR spectra. It was possible to recognise the discriminant metabolites of transgenic samples as well as to classify non-a priori defined samples of maize. It is important to underline that the obtained results are useful to point out the metabolic consequences of a specific genic modification on a plant, without using a targeted analysis of the different metabolites, in fact it was possible to classify the seeds also without the complete assignment of the spectra.The analysis was performed by applying multivariate techniques (principal component analysis and partial least squares-discriminant analysis) to NMR data.

Determining the time constants and amplitudes of exponential decays from relaxation data is a common task in LF-NMR. In this communication, we present an application of the SLICING algorithm to evaluate its possibilities for solving this problem. The method, originally introduced to compare different samples, is applied here to analyse a single relaxation curve, using the embedding technique. To test this procedure, we acquired data sets from samples of liquids properly separated, and characterized by different relaxation times. The results show a good estimation of parameters, comparable with those obtained applying Marquardt's algorithm, when the components have sufficiently different relaxation times.

The network metaphor is currently one of the most common general paradigms in biological sciences: this paradigm spans different scales of definition going from gene regulation to protein–protein interaction studies and metabolic regulation networks.Generally, the networks are defined by the nature of the connected elements (nodes) and their relative relations (edges). In this paper we demonstrate how the same biochemical regulation network can assume different shapes in terms of both constituting elements and intervening relations while remaining recognizable as a specific entity. This behaviour can be explained by the general scaling properties of biological networks and points to regulation pathways as emergent features of biochemical systems posited at a different hierarchical level with respect to the intervening metabolites.

The problem of protein folding vs. aggregation was investigated in acylphosphatase and the amyloid protein Abeta(1-40) by means of nonlinear signal analysis of their chain hydrophobicity. Numerical descriptors of recurrence patterns provided the basis for statistical evaluation of folding/aggregation distinctive features. Static and dynamic approaches were used to elucidate conditions coincident with folding vs. aggregation using comparisons with known protein secondary structure classifications, site-directed mutagenesis studies of acylphosphatase, and molecular dynamics simulations of amyloid protein, Abeta(1-40). The results suggest that a feature derived from principal component space characterized by the smoothness of singular, deterministic hydrophobicity patches plays a significant role in the conditions governing protein aggregation.