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The primary goal of precision medicine is to minimize side effects and optimize efficacy of treatments. Recent advances in medical imaging technology allow the use of more advanced image analysis methods beyond simple measurements of tumor size or radiotracer uptake metrics. The extraction of quantitative features from medical images to characterize tumor pathology or heterogeneity is an interesting process to investigate, in order to provide information that may be useful to guide the therapies and predict survival. This paper discusses the rationale supporting the concept of radiomics and the feasibility of its application to Non-Small Cell Lung Cancer in the field of radiation oncology research. We studied 91 stage III patients treated with concurrent chemoradiation and adaptive approach in case of tumor reduction during treatment. We considered 12 statistics features and 230 textural features extracted from the CT images. In our study, we used an ensemble learning method to classify patients' data into either the adaptive or non-adaptive group during chemoradiation on the basis of the starting CT simulation. Our data supports the hypothesis that a specific signature can be identified (AUC 0.82). In our experience, a radiomic signature mixing semantic and image-based features has shown promising results for personalized adaptive radiotherapy in non-small cell lung cancer.

Voluntary deep inspiration breath hold technique (vDIBH) is considered as the key to achieving the widest cardiac sparing in whole breast irradiation. Several techniques have been implemented to achieve a reproducible, fast and friendly treatment. The aim of the present study is to implement vDIBH using the ExacTrac (BrainLAB AG, Germany) monitoring system.Women with left-sided breast cancer, younger than 50 years or with cardiac disease, underwent whole breast RT with vDIBH using the ExacTrac (BrainLAB AG, Germany) monitoring system. Simulations were performed with patients positioned supine on a breast board with both arms raised above the head. Five optical markers were placed on the skin around the border of the left breast gland and their position was referenced with ink marking. Each patient received a training session to find the individual deep inspiration level. Finally, a vDIBH CT was taken. All patients were also studied in free breathing (FB) in order to compare the dose distribution for PTV, heart and left anterior descending coronary artery (LAD). Pre-treatment verification was carried out through the ExacTrac (BrainLAB AG, Germany) system and verified with electronic portal imaging (EPI). Moreover, daily real time EPIs in during modality (captured during the beam delivery) were taken in order to check the reproducibility.34 patients have been evaluated and 30 were eligible for vDIBH. Most patients showed small setup errors during the treatment course of below 5 mm in 94.9% of the recorded fields. Mean Displacement was less in cranio-caudal direction. Mean intra-fraction displacement was below 3 mm in all directions. vDIBH plans provided better cardiac dosimetry.vDIBH technique using ExacTrac (BrainLAB AG, Germany) monitoring system was applied with good reproducibility.

Giant lipid vesicles (GVs) are emerging models for investigating the properties and reactivity of cell-like microcompartments, providing useful information about plausible protocellular structures in primitive times, as well as for the modern synthetic biology goal of constructing the first artificial cell from its reconstituted and partly modified components. Here we explore a novel methodology of GV purification by microfiltration under reduced pressure, operated by a simple apparatus. The method has been characterized in terms of flow rate, amount of lipid loss, quality of recovered GVs, and size distribution. A case study is reported to show the practicability of GV microfiltration. A clickable fluorescent probe was encapsulated inside GVs; more than 99.9% of the non-entrapped probe was easily and rapidly removed by multiple microfiltrations. This novel methodology is briefly discussed as a future tool for selection experiments on GV populations.

Neuroblastoma (NB) is a severe pediatric tumor originating from neural crest derivatives and accounting for 15% of childhood cancer mortality. The heterogeneous and complex genetic etiology has been confirmed with the identification of mutations in two genes, encoding for the receptor tyrosine kinase Anaplastic Lymphoma Kinase (ALK) and the transcription factor Paired-like Homeobox 2B (PHOX2B), in a limited proportion of NB patients. Interestingly, these two genes are overexpressed in the great majority of primary NB samples and cell lines. These observations led us to test the hypothesis of a regulatory or functional relationship between ALK and PHOX2B underlying NB pathogenesis. Following this possibility, we first confirmed a striking correlation between the transcription levels of ALK, PHOX2B and its direct target PHOX2A in a panel of NB cell lines. Then, we manipulated their expression in NB cell lines by siRNA-mediated knock-down and forced over-expression of each gene under analysis. Surprisingly, PHOX2B- and PHOX2A-directed siRNAs efficiently downregulated each other as well as ALK gene and, consistently, the enhanced expression of PHOX2B in NB cells yielded an increment of ALK protein. We finally demonstrated that PHOX2B drives ALK gene transcription by directly binding its promoter, which therefore represents a novel PHOX2B target. These findings provide a compelling explanation of the concurrent involvement of these two genes in NB pathogenesis and are going to foster a better understanding of molecular interactions at the base of the disease. Moreover, this work opens new perspectives for NBs refractory to conventional therapies that may benefit from the design of novel therapeutic RNAi-based approaches for multiple gene targets.

Functionally Graded Scaffolds (FGSs) are porous biomaterials where porosity changes in space with a specific gradient. In spite of their wide use in bone tissue engineering, possible models that relate the scaffold gradient to the mechanical and biological requirements for the regeneration of the bony tissue are currently missing. In this study we attempt to bridge the gap by developing a mechanobiology-based optimization algorithm aimed to determine the optimal graded porosity distribution in FGSs. The algorithm combines the parametric finite element model of a FGS, a computational mechano-regulation model and a numerical optimization routine. For assigned boundary and loading conditions, the algorithm builds iteratively different scaffold geometry configurations with different porosity distributions until the best microstructure geometry is reached, i.e. the geometry that allows the amount of bone formation to be maximized. We tested different porosity distribution laws, loading conditions and scaffold Young's modulus values. For each combination of these variables, the explicit equation of the porosity distribution law-i.e the law that describes the pore dimensions in function of the spatial coordinates-was determined that allows the highest amounts of bone to be generated. The results show that the loading conditions affect significantly the optimal porosity distribution. For a pure compression loading, it was found that the pore dimensions are almost constant throughout the entire scaffold and using a FGS allows the formation of amounts of bone slightly larger than those obtainable with a homogeneous porosity scaffold. For a pure shear loading, instead, FGSs allow to significantly increase the bone formation compared to a homogeneous porosity scaffolds. Although experimental data is still necessary to properly relate the mechanical/biological environment to the scaffold microstructure, this model represents an important step towards optimizing geometry of functionally graded scaffolds based on mechanobiological criteria.