Study on the inactivation of enteric viruses by oxidazing compound and high-hydrostatic pressures. Development of recent methods for the viral detection in contaminated food samples.

I virus che causano gastroenteriti, come il Norovirus, costituiscono un problema a livello sanitario mondiale. Durante il periodo di dottorato mi sono interessato dell'inattivazione di questi virus tramite perossido di idrogeno e alte pressioni idrostatiche; inoltre ho svolto prove per valutare l'efficacia di diversi metodi di rilevamento di tali virus in campioni alimentari. I risultati ottenuti per quanto riguarda l'inattivazione sia con perossido di idrogeno che con le alte pressioni si sono rivelati particolarmente interessanti ed è stato possibile ottenere inattivazioni totali anche a basse concentrazioni di disinfettante e a basse pressioni. La valutazione dei metodi di rilevamento ha dimostrato la maggiore efficienza di un protocollo, che utilizza la proteinasi K, per l'eluizione del virus dalla matrice alimentare.Viruses causing gastroenteritis, like Norovirus, are a great problem for health all over the world. During the PhD period I studied the inactivation of these viruses using hydrogen peroxide and high-hydrostatic pressures; moreover I evaluated the efficacy of different methods for the detection of enteric viruses in food samples. The results obtained for the inactivation with hydrogen peroxide and high-hydrostatic pressure are very interesting and it was possible to obtain total inactivations even at low disinfectant concentration and at low pressures. The evaluation of detection methods has shown the higher efficacy of a protocol, which involves proteinase K, to elute the viral particles from the food matrix

Study on the Application of a New Method for the Viral Detection of Caliciviruses in Foodstuffs

Abstract Noroviruses (NoVs

PCR, Real-Time PCR analysis on Norwalk virus direct test on artificial-contaminated foodstuff

Introduction: The most commonly used methods to determine and identify Norwalk virus are based on molecular biology. Methods: A viral extraction protocol from food samples was studied in this work using artificial contamination test. It consists of a new protocol with a phase of viral elution from the food matrix performed using an eluting solution (glycine and beef extract at 3% pH 9) and a concentration phase with polyethylene glycol 8000. To detect Noroviruses, two techniques of molecular biology, polymerase chain reaction and real-time polymerase chain reaction, were compared. At the same time, tests of direct viral identification were conducted on soft fruits and salad obtained from the market. Results: From the results obtained it was possible to evaluate how the phase of viral recovery represents an important critical point of the protocol. Conclusion: It was possible to identify a greater sensitivity of the real-time polymerase chain reaction compared with the traditional polymerase chain reaction

Risk factors for the development of micro-vascular complications of type 2 diabetes in a single-centre cohort of patients

AIMS: In type 2 diabetes, we aimed at clarifying the role of glycated haemoglobin variability and other risk factors in the development of the main micro-vascular complications: peripheral neuropathy, nephropathy and retinopathy. METHODS: In a single-centre cohort of 900 patients, glycated haemoglobin variability was evaluated as intra-individual standard deviation, adjusted standard deviation and coefficient of variation of serially measured glycated haemoglobin in the 2-year period before a randomly selected index visit. We devised four models considering different aspects of glycated haemoglobin evolution. Multivariate stepwise logistic regression analysis was performed including the following covariates at the index visit: age, disease duration, body mass index, total cholesterol, high-density lipoprotein cholesterol, triglycerides, sex, smoking habit, hypertension, dyslipidemia, treatment with anti-diabetic drugs, occurrence of macro-vascular events and the presence of another micro-vascular complication. RESULTS: Males with high mean glycated haemoglobin, long duration of diabetes, presence of macro-vascular events and retinopathy emerged at higher risk for peripheral neuropathy. Development of nephropathy was independently associated with higher glycated haemoglobin variability, older age, male sex, current smoking status, presence of retinopathy, of peripheral neuropathy and of hypertension. Higher mean glycated haemoglobin, younger age, longer duration of diabetes, reduced estimated glomerular filtration rate and the presence of peripheral neuropathy were significantly associated with increased incidence of retinopathy. CONCLUSION: Glycated haemoglobin variability was associated with increased incidence of nephropathy, while mean glycated haemoglobin emerged as independent risk factor for the development of retinopathy and peripheral neuropathy. The presence of macro-vascular events was positively correlated with peripheral neuropathy. Finally, the occurrence of another micro-vascular complication was found to be a stronger risk factor for developing another micro-vascular complication than the mean or variability of glycated haemoglobin

Machine Learning Methods to Predict Diabetes Complications

One of the areas where Artificial Intelligence is having more impact is machine learning, which develops algorithms able to learn patterns and decision rules from data. Machine learning algorithms have been embedded into data mining pipelines, which can combine them with classical statistical strategies, to extract knowledge from data. Within the EU-funded MOSAIC project, a data mining pipeline has been used to derive a set of predictive models of type 2 diabetes mellitus (T2DM) complications based on electronic health record data of nearly one thousand patients. Such pipeline comprises clinical center profiling, predictive model targeting, predictive model construction and model validation. After having dealt with missing data by means of random forest (RF) and having applied suitable strategies to handle class imbalance, we have used Logistic Regression with stepwise feature selection to predict the onset of retinopathy, neuropathy, or nephropathy, at different time scenarios, at 3, 5, and 7 years from the first visit at the Hospital Center for Diabetes (not from the diagnosis). Considered variables are gender, age, time from diagnosis, body mass index (BMI), glycated hemoglobin (HbA1c), hypertension, and smoking habit. Final models, tailored in accordance with the complications, provided an accuracy up to 0.838. Different variables were selected for each complication and time scenario, leading to specialized models easy to translate to the clinical practice

Beyond Cohort Selection: An Analytics-Enabled i2b2

The i2b2 software is a widely adopted solution for secondary use of clinical data for clinical research, specifically designed for cohort identification. i2b2 is still lacking functionalities for data analysis. The aim of this work is to empower the i2b2 framework enabling clinical researchers to perform statistical analyses for accelerating the process of hypothesis testing. To this aim we have developed a flexible extension of i2b2 able to exploit different statistical engines. We have implemented some first applications for basic statistics and survival analyses, exploiting this extension and accessible through suitable user interfaces designed with a special consideration for usability