Continuos Flow Single Cell Separation into Open Microwell Arrays

A novel design based on electric field-free open microwell arrays for the automated continuous-flow sorting of single or small clusters of cells is presented. The main feature of the proposed device is the parallel analysis of cell-cell and cell-particle interactions in each microwell of the array. High throughput sample recovery with a fast and separate transfer from the microsites to standard microtiter plates is also possible thanks to the flexible printed circuit board technology which permits to produce cost effective large area arrays featuring geometries compatible with laboratory equipment. The particle isolation is performed via negative dielectrophoretic forces which convey the particles’ into the microwells. Particles such as cells and beads flow in electrically active microchannels on whose substrate the electrodes are patterned. The introduction of particles within the microwells is automatically performed by generating the required feedback signal by a microscope-based optical counting and detection routine. In order to isolate a controlled number of particles we created two particular configurations of the electric field within the structure. The first one permits their isolation whereas the second one creates a net force which repels the particles from the microwell entrance. To increase the parallelism at which the cell-isolation function is implemented, a new technique based on coplanar electrodes to detect particle presence was implemented. A lock-in amplifying scheme was used to monitor the impedance of the channel perturbed by flowing particles in high-conductivity suspension mediums. The impedance measurement module was also combined with the dielectrophoretic focusing stage situated upstream of the measurement stage, to limit the measured signal amplitude dispersion due to the particles position variation within the microchannel. In conclusion, the designed system complies with the initial specifications making it suitable for cellomics and biotechnology applications.

Open-cell Metallic Foams for the Electrochemical Conversion of Biomass-derived Compounds

The electrochemical conversion is a sustainable way for the production of added-value products, operating in mild conditions, using in-situ generated hydrogen/oxygen by water and avoiding the use of high H2/O2 pressures. The aim of this work is to investigate the electrocatalytic conversion of 5-hydroxymetilfurfural (HMF) and D-glucose, in alkaline media, using metallic open-cell foams based-catalysts. The electrochemical hydrogenation of HMF to 2,5-bis(hydroxymethyl)furan (BHMF) was performed using nanostructured Ag, deposited by galvanic displacement (GD) or electrodeposition (ED), on Cu foam, obtaining AgCu bimetallic nanoparticles (ED) or dendrites (GD) which enhanced electroactive surface area, charge and mass transfer, than bare foams. In diluted 0.02M HMF solutions, Ag/Cu samples selectively produce BHMF; the large surface area enhanced the productivity, compared to their 2D counterparts. Furthermore, at more concentrated solutions (0.05 – 0.10M) a gradually decrease of selectivity is observed. The performances of the electrodes is stable during the catalytic tests but a Cu-enrichment of particles occurred. The performances of Ni foam-based catalysts, obtained by calcination of Ni foam or by electrodeposition of Ni-hydroxide/Ni and Ni particle/Ni, were firstly investigated for the selective electrochemical oxidation of D-glucose toward gluconic acid (GO) and glucaric acid (GA). Then, the calcined catalyst was chosen to study the influence of the reaction conditions on the reaction mechanism. The GO and GA selectivities increase with the charge passed, while the formation of by-products from C-C cleavage/retro-aldol process is maximum at low charge. The fructose obtained from glucose isomerization favours the formation of by-products. The best glucose/NaOH ratio is between 0.5 and 0.1: higher values suppress the OER, while lower values favour the formation of low molecular weight products. The increases of the potential enhance the GO selectivity, nevertheless higher GA selectivity is observed at 0.6 – 0.7V vs SCE, confirmed by catalytic test performed in gluconate (30-35% GA selectivity).

Pre-clinical and clinical development of leukemia stem cell inhibitors in acute leukemias

In Leukemias, recent developments have demonstrated that the Hedgehog pathway plays a key-role in the peculiar ability of self renewal of leukemia stem cells. The aim of this research activity was to investigate, through a first in man, Phase I, open label, clinical trial, the role and the impact, mainly in terms of safety profile, adverse events and pharmacokinetics, of a Sonic Hedgehog inhibitor compound on a population of heavely pretreated patients affected by AML, CML, MF, or MDS, resistant or refractory to standard chemotherapy. Thirty-five patients have been enrolled. The drug was administered orally, in 28 days cycles, without rest periods. The compound showed a good safety profile. The half life was of 17-35 hours, justifying the daily administration. Significant signs of activity, in terms of reduction of bone marrow blast cell amount were seen in most of the patients enrolled. Interestingly, correlative biological studies demonstrated that, comparing the gene expression profyiling signature of separated CD34+ cells before and after one cycle of treatment, the most variably expressed genes were involved in the Hh pathway. Moreover, we observed that many genes involved in MDR (multidrug resistance)were significantly down regulated after treatment. These data might lead to future clinical trials based on combinatory approaches, including, for instance, Hh inhibitors and conventional chemotherapy.

Cell Host-Microbe Interactions: Turning Pathogen Mechanisms Into Cell's Advantages

Host-Pathogen Interaction is a very vast field of biological sciences, indeed every year many un- known pathogens are uncovered leading to an exponential growth of this field. The present work lyes between its boundaries, touching different aspects of host-pathogen interaction: We have evaluate the permissiveness of Mesenchimal Stem cell (FM-MSC from now on) to all known human affecting herpesvirus. Our study demonstrate that FM-MSC are full permissive to HSV1, HSV2, HCMV and VZV. On the other hand HHV6, HHV7, EBV and HHV8 are susceptible, but failed to activate a lytic infection program. FM-MSC are pluripotent stem cell and have been studied intensely in last decade. FM-MSC are employed in some clinical applications. For this reason it is important to known the degree of susceptibility to transmittable pathogens. Our atten- tion has then moved to bacterial pathogens: we have performed a proteome-wide in silico analy- sis of Chlamydiaceae family, searching for putative Nuclear localization Signal (NLS). Chlamy- diaceae are a family of obligate intracellular parasites. It’s reasonably to think that its members could delivered to nucleus effector proteins via NLS sequences: if that were the case the identifi- cation of NLS carrying proteins could open the way to therapeutic approaches. Our results strengthen this hypothesis: we have identified 72 protein bearing NLS, and verified their func- tionality with in vivo assays. Finally we have conceived a molecular scissor, creating a fusion protein between HIV-1 IN protein and FokI catalytic domain (a deoxyexonuclease domain). Our aim is to obtain chimeric enzyme (trojIN) which selectively identify IN naturally occurring target (HIV LTR sites) and cleaves subsequently LTR carrying DNA (for example integrated HIV1 DNA). Our preliminary results are promising since we have identified trojIN mutated version capable to selectively recognize LTR carrying DNA in an in vitro experiments.

Computational Modelling of Cardiac Electrophysiology: from Cell to Bedside

Heart diseases are the leading cause of death worldwide, both for men and women. However, the ionic mechanisms underlying many cardiac arrhythmias and genetic disorders are not completely understood, thus leading to a limited efficacy of the current available therapies and leaving many open questions for cardiac electrophysiologists. On the other hand, experimental data availability is still a great issue in this field: most of the experiments are performed in vitro and/or using animal models (e.g. rabbit, dog and mouse), even when the final aim is to better understand the electrical behaviour of in vivo human heart either in physiological or pathological conditions. Computational modelling constitutes a primary tool in cardiac electrophysiology: in silico simulations, based on the available experimental data, may help to understand the electrical properties of the heart and the ionic mechanisms underlying a specific phenomenon. Once validated, mathematical models can be used for making predictions and testing hypotheses, thus suggesting potential therapeutic targets. This PhD thesis aims to apply computational cardiac modelling of human single cell action potential (AP) to three clinical scenarios, in order to gain new insights into the ionic mechanisms involved in the electrophysiological changes observed in vitro and/or in vivo. The first context is blood electrolyte variations, which may occur in patients due to different pathologies and/or therapies. In particular, we focused on extracellular Ca2+ and its effect on the AP duration (APD). The second context is haemodialysis (HD) therapy: in addition to blood electrolyte variations, patients undergo a lot of other different changes during HD, e.g. heart rate, cell volume, pH, and sympatho-vagal balance. The third context is human hypertrophic cardiomyopathy (HCM), a genetic disorder characterised by an increased arrhythmic risk, and still lacking a specific pharmacological treatment.

Robotic Approach in the Treatment of Locally Advanced (Stage IIIA-pN2) non Small Cell Lung Cancer after Induction Therapy

Introduction: Despite there are already many studies on robotic surgery as minimally invasive approach for non-small cell lung cancer (NSCLC) patients, the use of this technique for stage III disease is still poorly described. These are the preliminary results of our prospective study on safety and effectiveness of robotic approach in patients with locally advanced NSCLC, in terms of postoperative complications and oncological outcome. Methods: Since 2016, we prospectively investigated, using standardized questionnaire and protocol, 21 consecutive patients with NSCLC stage IIIA-pN2 (diagnosed by EBUS-TBNA) who underwent lobectomy and radical lymph node dissection with robotic approach after induction treatment. Then, we performed a matched case-control study with 54 patients treated with open surgery during the same period of time, with similar age, clinical and pathological tumor stage. Results: The individual matched population was composed of 14 robot-assisted thoracic surgery and 14 patients who underwent open surgery. The median time range of resection was inferior in the open group compared to robotic lobectomy (148 vs 229 minutes; P=0.002). Lymph nodes resection and positivity were not statistically significantly different (p=0.66 and p=0.73 respectively). No difference was observed also for PFS (P=0.99) or OS (P=0.94). Conclusions: Our preliminary results demonstrated that the early outcomes and oncological results of N2-patients after robotic lobectomy were similar to open surgery. Considering the advantages of minimally invasive surgery, robotic assisted lobectomy should be a safe approach also to patients with local advanced disease.

Developing a radiomic based machine learning model to identify patients with resected non-small-cell lung cancer at high risk of relapse

Background There is a wide variation of recurrence risk of Non-small-cell lung cancer (NSCLC) within the same Tumor Node Metastasis (TNM) stage, suggesting that other parameters are involved in determining this probability. Radiomics allows extraction of quantitative information from images that can be used for clinical purposes. The primary objective of this study is to develop a radiomic prognostic model that predicts a 3 year disease free-survival (DFS) of resected Early Stage (ES) NSCLC patients. Material and Methods 56 pre-surgery non contrast Computed Tomography (CT) scans were retrieved from the PACS of our institution and anonymized. Then they were automatically segmented with an open access deep learning pipeline and reviewed by an experienced radiologist to obtain 3D masks of the NSCLC. Images and masks underwent to resampling normalization and discretization. From the masks hundreds Radiomic Features (RF) were extracted using Py-Radiomics. Hence, RF were reduced to select the most representative features. The remaining RF were used in combination with Clinical parameters to build a DFS prediction model using Leave-one-out cross-validation (LOOCV) with Random Forest. Results and Conclusion A poor agreement between the radiologist and the automatic segmentation algorithm (DICE score of 0.37) was found. Therefore, another experienced radiologist manually segmented the lesions and only stable and reproducible RF were kept. 50 RF demonstrated a high correlation with the DFS but only one was confirmed when clinicopathological covariates were added: Busyness a Neighbouring Gray Tone Difference Matrix (HR 9.610). 16 clinical variables (which comprised TNM) were used to build the LOOCV model demonstrating a higher Area Under the Curve (AUC) when RF were included in the analysis (0.67 vs 0.60) but the difference was not statistically significant (p=0,5147).