Screening and Genetic Diagnosis of Hemoglobinopathies in Southern and Northern Europe: Two Examples

Prevention of Hemoglobinopathies has developed around the world based upon the experience done in pioneering endemic countries and is now facing a new phase in non-endemic areas with a recent immigration history. We describe two situations, taking Latium (central Italy) and The Netherlands as two models for endemic and non-endemic countries both confronted with a large multi-ethnic immigrant society. We present prevention results and discuss aspects such as local knowledge and organization. We illustrate the importance of issues like information, carrier diagnostics, screening, counseling and prenatal diagnosis in particular situation of contrasting interest an different ethical opinions. We conclude by underlining the importance of implementing primary prevention at the European level, based upon better information, diagnostics and counseling

Digitization of Gandharan Artefacts: A Digitization Concept

The DiGA project digitizes and catalogues of a corpus of 1,791 Buddhist sculptures from the ancient region of Gandhara (present-day eastern Afghanistan/north-western Pakistan). These originate from 13 Buddhist sites in the modern districts of Lower Dir and Swat in the Province of Khyber-Pakhtunkhwa and are currently kept in the Dir Museum in Chakdara and in the Mission House of the Missione Archeologica Italiana in Pakistan in Saidu Sharif. These collections provide a solid corpus for reassessing the history of Buddhism and Buddhist art in the region because their archaeological provenance is known. DiGA will result in a database that will be accessible on heidICON, the multi-media platform of Heidelberg University Library. This publication presents the digitization concept which underpins the DiGA project. It highlights the scientific relevance of the collections, describes the data management plan implemented for the photographic documentation of the objects and the indexing of the related metadata. In this respect, it addresses Open Data strategies employed to ensure the interoperability of the database and its long-term accessibility

L’immagine antropomorfa del Buddha nell’arte della valle dello Swāt: analisi iconografica e classificazione

Oggetto di questa tesi di dottorato è l’immagine antropomorfa del Buddha nell’arte della valle dello Swāt (Pakistan). La comparsa dell’immagine antropomorfa del Buddha nella produzione artistica buddhista è stata (e per certi versi continua a esserlo) oggetto di studio ampiamente dibattuto nella letteratura scientifica. Nonostante ciò, a oggi – a parte sporadici casi – non sembra siano stati condotti studi statistici circa le raffigurazioni del Buddha nell’arte del Gandhāra, soprattutto con una visione d’insieme dei dati in nostro possesso. Con ogni probabilità, l’assenza di studi sistematici e onnicomprensivi è dovuta alla scarsità di informazioni di carattere storico-archeologico sui reperti e sull’utilizzo di strumenti obsoleti per un processo (la catalogazione e classificazione iconografica) che richiede sempre più spesso modalità differenti. Si è reso indispensabile quindi un approccio diverso alla rappresentazione antropomorfa del Buddha, tramite l’analisi sistematica di ogni reperto sulla base del contesto archeologico e degli elementi iconografici, e l’inserimento delle informazioni all’interno di una banca dati che ha permesso un’analisi incrociata degli stessi e uno studio più efficace, anche da un punto di vista statistico. Il materiale preso in esame è costituito esclusivamente dai reperti in scisto e “stucco”, provenienti dalla valle dello Swāt, in cui è raffigurato il Buddha; il valore di tale materiale – ai fini di una pubblicazione scientifica – è assicurato dagli scavi regolari effettuati dalla Missione Archeologica Italiana (MAI) in Pakistan e dalla conservazione presso il Museo Nazionale d’Arte Orientale “Giuseppe Tucci” di Roma (MNAO) e il Museo d’Arte Orientale di Torino (MAO). Le nuove tecnologie possono aiutare nella creazione di un database di immagini che permetta una classificazione iconografica meticolosa in grado di prendere in considerazione tutte le informazioni fornite dal reperto.The topic of this doctoral thesis is the anthropomorphic image of the Buddha in the art of the Swāt Valley (Pakistan). The appearance of the anthropomorphic image of the Buddha in the Buddhist artistic production has been (and in some ways continues to be) a widely debated topic in scientific literature. Despite this, to this day - apart from sporadic cases - it does not seem that statistical studies have been conducted on the representations of the Buddha in Gandharan art, especially with an overview of the data in our possession. In all probability, the absence of systematic and all-encompassing approach is due to the scarcity of historical-archaeological information on the finds and the use of obsolete tools for a process (cataloguing and iconographic classification) that increasingly requires different modalities. A different approach to the study of the anthropomorphic representation of the Buddha was therefore indispensable, through the systematic analysis of each artefact on the basis of the archaeological context and iconographic elements, and the insertion of the information into a database that allowed a cross analysis and a more effective study, also from a statistical point of view. The material examined consists exclusively of schist and “stucco” artefacts from the Swāt Valley, in which the Buddha is depicted; the value of this material - for the purposes of scientific publication - is ensured by regular excavations carried out by the Italian Archaeological Mission (MAI) in Pakistan and by conservation at the National Museum of Oriental Art "Giuseppe Tucci" in Rome (MNAO) and the Museum of Oriental Art in Turin (MAO). New technologies can help build up a database of images for meticulous iconographic classification that takes into account all the information provided by the artefact

A Model For e-Government Digital Document

The presence of a great amount of information is typical of bureaucratic processes, like the ones pertaining to public and private administrations. Such information is often recorded on paper or in different digital formats and its management is very expensive, both in terms of space used for storing documents and in terms of time spent in searching for the documents of interest. Furthermore, the manual management of these documents is absolutely not error-free. To efficiently access the information contained in very large document repositories, such as public administration archives, techniques for syntactic and semantic document management are required, so to ensure a large and intense process of document dematerialization, and eliminate, or at least reduce, the quantity of paper documents. In this work we present a novel RDF model of digital documents for improving the dematerialization effectiveness, that constitutes the starting point of an information system able to manage documental streams in the most efficient way. Such model takes into account the important need that is required in several E-Government applications which, depending on authorities or final users or time, provides different representations of the same multimedia contents

An analytic model to calculate Voxel S-Values for 177^{177}Lu

Objective: $^{177}$Lu is one of the most employed isotopes in targeted radionuclide therapies and theranostics, and 3D internal dosimetry for such procedures has great importance. Voxel S-Values (VSVs) approach is widely used for this purpose, but VSVs are available for a limited number of voxel dimensions. The aim of this work is to develop an analytic model for the calculation of $^{177}$Lu-VSVs in any cubic voxelized geometry of practical interest. Approach: Monte Carlo (MC) simulations were implemented with the toolkit GAMOS to evaluate VSVs in voxelized geometries of soft tissue from a source of $^{177}$Lu homogeneously distributed in the central voxel. Nine geometric setups, containing 15x15x15 cubic voxels of sides l ranging from 2 mm to 6 mm, in steps of 0.5 mm, were considered. For each l, the VSVs computed as a function of the "normalized radius", Rn = R/l (with R = distance from the center of the source voxel), were fitted with a parametric function. The dependencies of the parameters as a function of l were then fitted with appropriate functions, in order to implement the model for deducing $^{177}$Lu-VSVs for any l within the aforementioned range. Main results: The MC-derived VSVs were satisfactorily compared with literature data for validation, and the VSVs computed with the analytic model agree with the MC ones within 2\% for Rn $\leq$ 2 and within 6\% for Rn > 2. Significance: The proposed model enables the easy and fast calculation, with a simple spreadsheet, of $^{177}$Lu-VSVs in any cubic voxelized geometry of practical interest, avoiding the necessity of implementing ad-hoc MC simulations to estimate VSVs for specific voxel dimensions not available in literature data.Comment: 16 pages, 7 figures, first round review in "Biomedical Physics & Engineering Express

Altilix\uae Supplement Containing Chlorogenic Acid and Luteolin Improved Hepatic and Cardiometabolic Parameters in Subjects with Metabolic Syndrome: A 6 Month Randomized, Double-Blind, Placebo-Controlled Study

The objective was to evaluate the eects of 6 months of supplementation with Altilix\uae, containing chlorogenic acid and its derivatives, and luteolin and its derivatives, on cardiovascular risk and hepatic markers in subjects with metabolic syndrome (MetS). A randomized, double-blind, placebo-controlled study was performed in 100 subjects with MetS with a follow-up period of 6 months; 50 subjects were randomized to Altilix\uae (26 men and 24 women, mean age 63 8 years) and the other 50 to placebo (28 men and 22 women, mean age 63 11 years). Anthropometric, cardiometabolic, and hepatic parameters were assessed at baseline and at the end of follow-up. Carotid intima-media thickness and endothelial function were assessed by doppler ultrasound and by flow-mediated dilation of the brachial artery, respectively. The presence and degree of non-alcoholic fatty liver disease (NAFLD) was assessed by the fatty liver index (FLI), and subjects were divided into three subgroups: (1) without NAFLD; (2) with borderline NAFLD; and (3) with NAFLD. After 6 months of Altilix\uae supplementation, we found a significant improvement vs. placebo in most of the evaluated parameters, including body weight (2.40% (95% CI 3.79, 1.01); p < 0.001), waist circumference (2.76% (95% CI 4.55, 0.96); p = 0.003), HbA1c (0.95% (95% CI 1.22, 0.67); p < 0.001), plasma lipids, FLI (21.83% (95% CI 27.39, 16.27); p < 0.001), hepatic transaminases, flow-mediated dilation (10.56% (95% CI 5.00, 16.12); p < 0.001), and carotid intima-media thickness (39.48% (95% CI 47.98, 30.97); p < 0.001). Further, the improvement in cardiometabolic variables was independent of the degree of hepatic steatosis. Altilix\uae supplementation improved hepatic and cardio-metabolic parameters in MetS subjects. Altilix\uae supplementation was a beneficial approach in the management of hepatic and cardiometabolic alterations in MetS subjects

Cardiovascular abnormalities and impaired exercise performance in adolescents with congenital adrenal hyperplasia

Context: Patients with classic Congenital Adrenal Hyperplasia (CAH) are treated with lifelong glucocorticoids (GCS). Cardiovascular (CV) and metabolic effects of such therapy in adolescents have never been quantified. Objective: To investigate left ventricular (LV) morphology, function and exercise performance in adolescents with CAH. Design and Setting: cross-sectional and controlled study conducted at a tertiary referral centre. Patients: Twenty patients with classic CAH (10 females) aged 13.6±2.5 years and 20 healthy controls comparable for sex and pubertal status were enrolled in the study and compared to a group of 18 patients without CAH receiving a similar dose of GCS for Juvenile Idiopathic Arthritis (JIA). Main Outcomes Measures: Echocardiographic assessment and symptom-limited exercise testing were performed. Anthropometric, hormonal and biochemical parameters were also measured. Results: Compared to healthy controls, patients with CAH exhibited an increased BMI (p<0.001), waist-to-height ratio (p<0.001), percentage of body fat (p<0.001) as well as higher insulin concentrations and HOMA index even after adjustment for BMI (p=0.03 and p=0.05, respectively). Moreover, CAH patients exhibited an impaired exercise capacity as shown by reduced peak workload (99±27 vs 126±27 W, p<0.01) and higher systolic blood pressure response at peak (156±18 vs 132±11 mmHg, p<0.01; Δ=45±24 vs 22±10 mmHg, p=0.05) with respect to healthy controls. CAH males displayed mild LV diastolic dysfunction as documented by significant prolongation of both isovolumic relaxation time (IRT) (118±18 vs 98±11ms, p<0.05) and mitral deceleration time (MDT) (138±25 vs 111±15 ms, p<0.01). No significant differences in CV function were found between CAH and JIA patients. Conclusion: Adolescents with CAH exhibit impaired exercise performance and enhanced systolic blood pressure response during exercise. In our population, such abnormalities appear related to GCS therapy rather than CAH per se. CAH males, but no females, present mild LV diastolic dysfunction that correlates with testosterone concentrations suggesting a sex hormone related difference

HOLMeS: eHealth in the Big Data and Deep Learning Era

Now, data collection and analysis are becoming more and more important in a variety of application domains, as long as novel technologies advance. At the same time, we are experiencing a growing need for human–machine interaction with expert systems, pushing research toward new knowledge representation models and interaction paradigms. In particular, in the last few years, eHealth—which usually indicates all the healthcare practices supported by electronic elaboration and remote communications—calls for the availability of a smart environment and big computational resources able to offer more and more advanced analytics and new human–computer interaction paradigms. The aim of this paper is to introduce the HOLMeS (health online medical suggestions) system: A particular big data platform aiming at supporting several eHealth applications. As its main novelty/functionality, HOLMeS exploits a machine learning algorithm, deployed on a cluster-computing environment, in order to provide medical suggestions via both chat-bot and web-app modules, especially for prevention aims. The chat-bot, opportunely trained by leveraging a deep learning approach, helps to overcome the limitations of a cold interaction between users and software, exhibiting a more human-like behavior. The obtained results demonstrate the effectiveness of the machine learning algorithms, showing an area under ROC (receiver operating characteristic) curve (AUC) of 74.65% when some first-level features are used to assess the occurrence of different chronic diseases within specific prevention pathways. When disease-specific features are added, HOLMeS shows an AUC of 86.78%, achieving a greater effectiveness in supporting clinical decisions