Precision medicine and machine learning towards the prediction of the outcome of potential celiac disease

Potential Celiac Patients (PCD) bear the Celiac Disease (CD) genetic predisposition, a significant production of antihuman transglutaminase antibodies, but no morphological changes in the small bowel mucosa. A minority of patients (17%) showed clinical symptoms and need a gluten free diet at time of diagnosis, while the majority progress over several years (up to a decade) without any clinical problem neither a progression of the small intestine mucosal damage even when they continued to assume gluten in their diet. Recently we developed a traditional multivariate approach to predict the natural history, on the base of the information at enrolment (time 0) by a discriminant analysis model. Still, the traditional multivariate model requires stringent assumptions that may not be answered in the clinical setting. Starting from a follow-up dataset available for PCD, we propose the application of Machine Learning (ML) methodologies to extend the analysis on available clinical data and to detect most influent features predicting the outcome. These features, collected at time of diagnosis, should be capable to classify patients who will develop duodenal atrophy from those who will remain potential. Four ML methods were adopted to select features predictive of the outcome; the feature selection procedure was indeed capable to reduce the number of overall features from 85 to 19. ML methodologies (Random Forests, Extremely Randomized Trees, and Boosted Trees, Logistic Regression) were adopted, obtaining high values of accuracy: all report an accuracy above 75%. The specificity score was always more than 75% also, with two of the considered methods over 98%, while the best performance of sensitivity was 60%. The best model, optimized Boosted Trees, was able to classify PCD starting from the selected 19 features with an accuracy of 0.80, sensitivity of 0.58 and specificity of 0.84. Finally, with this work, we are able to categorize PCD patients that can more likely develop overt CD using ML. ML techniques appear to be an innovative approach to predict the outcome of PCD, since they provide a step forward in the direction of precision medicine aimed to customize healthcare, medical therapies, decisions, and practices tailoring the clinical management of PCD children

Il paesaggio tra mirabilia e miracoli : concezioni e rappresentazioni della natura nella poesia latina tardoantica

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Assistive Bass Guitar

Muscular Dystrophy is a group of disorders in which there is a progressive loss of muscle mass and strength. The most common form, Duchenne Muscular Dystrophy (DMD) tends to affect young males and has no cure. DMD causes a decrease in motor control over time, so those affected with talents in the arts may lose their ability to express themselves in this way. This project creates an assistive aid for those affected with DMD which allows them to play the bass guitar. Using a combination of motors, solenoid plungers, and a micro-controller, the user is able to interface with a tablet and create music

Progression of Celiac Disease in Children With Antibodies Against Tissue Transglutaminase and Normal Duodenal Architecture

Potential celiac disease is characterized by positive results from serologic tests for tissue transglutaminase antibodies (anti-TG2) but normal duodenal architecture (Marsh stages 0-1). There is controversy over the best way to manage these patients. We investigated risk factors associated with the development of villous atrophy in children with potential celiac disease

Clinical variability of neurofibromatosis 1: A modifying role of cooccurring PTPN11 variants and atypical brain MRI findings

Neurofibromatosis 1 (NF1) is a disorder characterized by variable expressivity caused by loss-of-function variants in NF1, encoding neurofibromin, a protein negatively controlling RAS signaling. We evaluated whether concurrent variation in proteins functionally linked to neurofibromin contribute to the variable expressivity of NF1. Parallel sequencing of a RASopathy gene panel in 138 individuals with molecularly confirmed clinical diagnosis of NF1 identified missense variants in PTPN11, encoding SHP2, a positive regulator of RAS signaling, in four subjects from three unrelated families. Three subjects were heterozygous for a gain-of-function variant and showed a severe expression of NF1 (developmental delay, multiple cerebral neoplasms and peculiar cortical MRI findings), and features resembling Noonan syndrome (a RASopathy caused by activating variants in PTPN11). Conversely, the fourth subject, who showed an attenuated presentation, carried a previously unreported PTPN11 variant that had a hypomorphic behavior in vitro. Our findings document that functionally relevant PTPN11 variants occur in a small but significant proportion of subjects with NF1 modulating disease presentation, suggesting a model in which the clinical expression of pathogenic NF1 variants is modified by concomitant dysregulation of protein(s) functionally linked to neurofibromin. We also suggest targeting of SHP2 function as an approach to treat evolutive complications of NF1

Clinical variability of neurofibromatosis 1: A modifying role of cooccurring PTPN11 variants and atypical brain MRI findings

Neurofibromatosis 1 (NF1) is a disorder characterized by variable expressivity caused by loss-of-function variants in NF1, encoding neurofibromin, a protein negatively controlling RAS signaling. We evaluated whether concurrent variation in proteins functionally linked to neurofibromin contribute to the variable expressivity of NF1. Parallel sequencing of a RASopathy gene panel in 138 individuals with molecularly confirmed clinical diagnosis of NF1 identified missense variants in PTPN11, encoding SHP2, a positive regulator of RAS signaling, in four subjects from three unrelated families. Three subjects were heterozygous for a gain-of-function variant and showed a severe expression of NF1 (developmental delay, multiple cerebral neoplasms and peculiar cortical MRI findings), and features resembling Noonan syndrome (a RASopathy caused by activating variants in PTPN11). Conversely, the fourth subject, who showed an attenuated presentation, carried a previously unreported PTPN11 variant that had a hypomorphic behavior in vitro. Our findings document that functionally relevant PTPN11 variants occur in a small but significant proportion of subjects with NF1 modulating disease presentation, suggesting a model in which the clinical expression of pathogenic NF1 variants is modified by concomitant dysregulation of protein(s) functionally linked to neurofibromin. We also suggest targeting of SHP2 function as an approach to treat evolutive complications of NF1

Procedure for Organic Matter Removal from Peat Samples for XRD Mineral Analysis

Ombrotrophic peatlands are recognized archives of past atmospheric mineral dust deposition. Net dust deposition rates, grain size, mineral hosts and source areas are typically inferred from down-core elemental data. Although elemental analysis can be time efficient and data rich, there are some inherent limitations. X-ray diffraction (XRD) analysis allowsdirect identification of mineral phases in environmental samples but few studies have applied this method to peat samples and a well-developed protocol for extracting the inorganic fraction of highly organic samples (>95%) is lacking. We tested and compared different levels of pre-treatment: no pre-treatment, thermal combustion (300, 350, 400, 450, 500 and 550 degrees C) and chemical oxidation (H2O2 and Na2S2O8) using a homogenised highly organic (>98%) composite peat sample. Subsequently, minerals were identified by XRD. The results show that combustion is preferred to chemical oxidation because it most efficiently removes organic matter (OM), an important pre-requisite for identifying mineral phases by XRD analysis. Thermally induced phase transitions can be anticipated when temperature is the only factor to take into consideration. Based on the data required in this studythe recommended combustion temperature is 500 degrees C which efficiently removes OM while preserving a majority of common dust minerals