Scaffold e bioreattori per la costruzione di vasi sanguigni ingegnerizzati

Nell'accezione più generale, l'oggetto di approfondimento di questa tesina è l'approccio dell'ingegneria tessutale allo sviluppo di impianti vascolari. Nello specifico, ci si sofferma su alcuni aspetti della realizzazione di suddetti impianti, che consta di differenti passaggi. In primis si assiste alla preparazione dello scaffold, successivamente avviene la coltura e la semina delle cellule sul supporto ed infine si procede all'impianto nel paziente. Analisi immunoistochimiche permettono poi di valutare il risultato ottenuto. La tesina si aprirà con una rapida presentazione delle tipologie di impianti vascolari, dei richiami al sistema circolatorio e delle relative problematiche. Si tratterà poi dell’approccio dell'ingegneria tessutale ai vasi sanguigni (TEBVs: tissue-engineered blood vessels), passando per gli step citati in precedenza. In seguito si focalizzerà l'attenzione sui due argomenti cardine: i materiali con cui si realizza lo scaffold ed i bioreattori. Per quanto concerne il primo punto, si esamineranno le proprietà e le tecniche di lavorazione dei materiali e si riporteranno per ognuno uno studio ad esso inerente. La discussione verterà poi sui bioreattori, dei quali si analizzeranno la struttura ed il funzionamento, riportando anche in questo caso alcuni esempi delle varie tipologie di bioreattoreope

A chemical threshold controls nanocrystallization and degassing behaviour in basalt magmas

An increasing number of studies are being presented demonstrating that volcanic glasses can be heterogeneous at the nanoscale. These nano-heterogeneities can develop both during viscosity measurements in the laboratory and during magma eruptions. Our multifaceted study identifies here total transition metal oxide content as a crucial compositional factor governing the tendency of basalt melts and glasses towards nanolitization: at both anhydrous and hydrous conditions, an undercooled trachybasalt melt from Mt. Etna readily develops nanocrystals whose formation also hampers viscosity measurements, while a similar but FeO- and TiO2-poorer basalt melt from Stromboli proves far more stable at similar conditions. We therefore outline a procedure to reliably derive pure liquid viscosity without the effect of nanocrystals, additionally discussing how subtle compositional differences may contribute to the different eruptive styles of Mt. Etna and Stromboli

Scaffold e bioreattori per la costruzione di vasi sanguigni ingegnerizzati

Nell'accezione più generale, l'oggetto di approfondimento di questa tesina è l'approccio dell'ingegneria tessutale allo sviluppo di impianti vascolari. Nello specifico, ci si sofferma su alcuni aspetti della realizzazione di suddetti impianti, che consta di differenti passaggi. In primis si assiste alla preparazione dello scaffold, successivamente avviene la coltura e la semina delle cellule sul supporto ed infine si procede all'impianto nel paziente. Analisi immunoistochimiche permettono poi di valutare il risultato ottenuto. La tesina si aprirà con una rapida presentazione delle tipologie di impianti vascolari, dei richiami al sistema circolatorio e delle relative problematiche. Si tratterà poi dell’approccio dell'ingegneria tessutale ai vasi sanguigni (TEBVs: tissue-engineered blood vessels), passando per gli step citati in precedenza. In seguito si focalizzerà l'attenzione sui due argomenti cardine: i materiali con cui si realizza lo scaffold ed i bioreattori. Per quanto concerne il primo punto, si esamineranno le proprietà e le tecniche di lavorazione dei materiali e si riporteranno per ognuno uno studio ad esso inerente. La discussione verterà poi sui bioreattori, dei quali si analizzeranno la struttura ed il funzionamento, riportando anche in questo caso alcuni esempi delle varie tipologie di bioreattor

Metodi in metagenomica per l'analisi del microbioma: applicazione a pazienti affetti da broncopneumopatia cronica ostruttiva e da cancro al colon

In questo lavoro di tesi si è implementata una pipeline per analizzare il microbiota al fine di evidenziare una relazione tra lo stesso e lo stato di salute dell’ospite. Le analisi perciò sono state effettuate sul microbiota di soggetti con stati di salute differente, in particolare si sono prese in considerazione due patologie: la broncopneumopatia cronica ostruttiva (BPCO) ed il cancro al colon (CRC)

Predictive networks for multi meta-omics data integration

The role of microbiome in disease onset and in equilibrium is being exposed by a wealth of high-throughput omics methods. All key research directions, e.g., the study of gut microbiome dysbiosis in IBD/IBS, indicate the need for bioinformatics methods that can model the complexity of the microbial communities ecology and unravel its disease-associated perturbations. A most promising direction is the “meta-omics” approach, that allows a profiling based on various biological molecules at the metagenomic scale (e.g., metaproteomics, metametabolomics) as well as different “microbial” omes (eukaryotes and viruses) within a system biology approach. This thesis introduces a bioinformatic framework for microbiota datasets that combines predictive profiling, differential network analysis and meta-omics integration. In detail, the framework identifies biomarkers discriminating amongst clinical phenotypes, through machine learning techniques (Random Forest or SVM) based on a complete Data Analysis Protocol derived by two initiatives funded by FDA: the MicroArray Quality Control-II and Sequencing Quality Control projects. The biomarkers are interpreted in terms of biological networks: the framework provides a setup for networks inference, quantification of networks differences based on the glocal Hamming and Ipsen-Mikhailov (HIM) distance and detection of network communities. The differential analysis of networks allows the study of microbiota structural organization as well as the evolving trajectories of microbial communities associated to the dynamics of the target phenotypes. Moreover, the framework combines a novel similarity network fusion method and machine learning to identify biomarkers from the integration of multiple meta-omics data. The framework implementation requires only standard open source computational biology tools, as a combination of R/Bioconductor and Python functions. In particular, full scripts for meta-omics integration are available in a GitHub repository to ease reuse (https://github.com/AleZandona/INF). The pipeline has been validated on original data from three different clinical datasets. First, the predictive profiling and the network differential analysis have been applied on a pediatric Inflammatory Bowel Disease (IBD) cohort (in faecal vs biopsy environments) and controls, in collaboration with a multidisciplinary team at the Ospedale Pediatrico Bambino Gesú (Rome, I). Then, the meta-omics integration has been tested on a paired bacterial and fungal gut microbiota human IBD datasets from the Gastroenterology Department of the Saint Antoine Hospital (Paris, F), thanks to the collaboration with “Commensals and Probiotics-Host Interactions” team at INRA (Jouy-en-Josas, F). Finally, the framework has been validated on a bacterial-fungal gut microbiota dataset from children affected by Rett syndrome. The different nature of datasets used for validation naturally supports the extension of the framework on different omics datasets. Besides, clinical practice can take advantage of our framework, given the reproducibility and robustness of results, ensured by the adopted Data Analysis Protocol, as well as the biological relevance of the findings, confirmed by the clinical collaborators. Specifically, the omics-based dysbiosis profiles and the inferred biological networks can support the current diagnostic tools to reveal disease-associated perturbations at a much prodromal earlier stage of disease and may be used for disease prevention, diagnosis and prognosis

A Dynamic Bayesian Network model for the simulation of Amyotrophic Lateral Sclerosis progression

Abstract Background Amyotrophic lateral sclerosis (ALS) is an adult-onset neurodegenerative disease progressively affecting upper and lower motor neurons in the brain and spinal cord. Mean life expectancy is three to five years, with paralysis of muscles, respiratory failure and loss of vital functions being the common causes of death. Clinical manifestations of ALS are heterogeneous due to the mix of anatomic regions involvement and the variability in disease course; consequently, diagnosis and prognosis at the level of individual patient is really challenging. Prediction of ALS progression and stratification of patients into meaningful subgroups have been long-standing interests to clinical practice, research and drug development. Methods We developed a Dynamic Bayesian Network (DBN) model on more than 4500 ALS patients included in the Pooled Resource Open-Access ALS Clinical Trials Database (PRO-ACT), in order to detect probabilistic relationships among clinical variables and identify risk factors related to survival and loss of vital functions. Furthermore, the DBN was used to simulate the temporal evolution of an ALS cohort predicting survival and the time to impairment of vital functions (communication, swallowing, gait and respiration). A first attempt to stratify patients by risk factors and simulate the progression of ALS subgroups was also implemented. Results The DBN model provided the prediction of ALS most probable trajectories over time in terms of important clinical outcomes, including survival and loss of autonomy in functional domains. Furthermore, it allowed the identification of biomarkers related to patients’ clinical status as well as vital functions, and unrevealed their probabilistic relationships. For instance, DBN found that bicarbonate and calcium levels influence survival time; moreover, the model evidenced dependencies over time among phosphorus level, movement impairment and creatinine. Finally, our model provided a tool to stratify patients into subgroups of different prognosis studying the effect of specific variables, or combinations of them, on either survival time or time to loss of autonomy in specific functional domains. Conclusions The analysis of the risk factors and the simulation allowed by our DBN model might enable better support for ALS prognosis as well as a deeper insight into disease manifestations, in a context of a personalized medicine approach

Multi-omics integration for neuroblastoma clinical endpoint prediction

Abstract Background High-throughput methodologies such as microarrays and next-generation sequencing are routinely used in cancer research, generating complex data at different omics layers. The effective integration of omics data could provide a broader insight into the mechanisms of cancer biology, helping researchers and clinicians to develop personalized therapies. Results In the context of CAMDA 2017 Neuroblastoma Data Integration challenge, we explore the use of Integrative Network Fusion (INF), a bioinformatics framework combining a similarity network fusion with machine learning for the integration of multiple omics data. We apply the INF framework for the prediction of neuroblastoma patient outcome, integrating RNA-Seq, microarray and array comparative genomic hybridization data. We additionally explore the use of autoencoders as a method to integrate microarray expression and copy number data. Conclusions The INF method is effective for the integration of multiple data sources providing compact feature signatures for patient classification with performances comparable to other methods. Latent space representation of the integrated data provided by the autoencoder approach gives promising results, both by improving classification on survival endpoints and by providing means to discover two groups of patients characterized by distinct overall survival (OS) curves. Reviewers This article was reviewed by Djork-Arné Clevert and Tieliu Shi

Microbial Communities & Individual Health Trajectories

<p>Presented at the 2nd Workshop "MICROBIOTA: salute, terme e alimentazione" -- Comano Terme </p> <p>http://www.orikata.it/ftp_upload/programma-3-ottobre-2015.pdf</p

Choice of Training-Validation partitions impacts predictive performance

<p>Presented at the 4th Italian Workshop on Machine Learning and Data Mining, September 22nd, 2015, Ferrara (Italy)</p> <p>http://aixia2015.unife.it/events/mldm/</p