Clinical Features, Cardiovascular Risk Profile, and Therapeutic Trajectories of Patients with Type 2 Diabetes Candidate for Oral Semaglutide Therapy in the Italian Specialist Care

Introduction: This study aimed to address therapeutic inertia in the management of type 2 diabetes (T2D) by investigating the potential of early treatment with oral semaglutide. Methods: A cross-sectional survey was conducted between October 2021 and April 2022 among specialists treating individuals with T2D. A scientific committee designed a data collection form covering demographics, cardiovascular risk, glucose control metrics, ongoing therapies, and physician judgments on treatment appropriateness. Participants completed anonymous patient questionnaires reflecting routine clinical encounters. The preferred therapeutic regimen for each patient was also identified. Results: The analysis was conducted on 4449 patients initiating oral semaglutide. The population had a relatively short disease duration (42%  60% of patients, and more often than sitagliptin or empagliflozin. Conclusion: The study supports the potential of early implementation of oral semaglutide as a strategy to overcome therapeutic inertia and enhance T2D management

CoTracks: A New Lossy Compression Schema for Tracking Logs Data Based on Multiparametric Segmentation

A massive diffusion of positioning devices and services, transmitting and producing spatio-temporal data, raised space complexity problems and pulled the research focus toward efficient and specific algorithms to compress these huge amount of stored or flowing data. Co Tracks algorithm has been projected for a lossy compression of GPS data, exploiting analogies between all their spatio-temporal features. The original contribution of this algorithm is the consideration of the altitude of the track, an elaboration of 3D data and a dynamic vision of the moving point, because the speed, tightly linked to the time, is supposed to be one of the significant parameters in the uniformity search. Minimum Bounding Box has been the tool employed to group data points and to generate the key points of the approximated trajectory. The compression ratio, resulting also after a further Huffman coding, appears attractively high, suggesting new interesting developments of this new techniqu

Genomic comparison using Data Mining techniques based on a possibilistic fuzzy sets model

Current copiousness of genomic information stored in biological databases makes ultimately feasible the proposal for an application of knowledge management aimed to discover general rules in subcellular phenomena. The goal of this work is primarily to discover relationships between genes by microarray analysis. The tools exploited come from clustering techniques and are mainly based on KDD (Knowledge Discovery in Databases) concepts. Starting from a data set, each element can be represented by a characteristic matrix, which sums up all data attributes. In this case data mining is oriented to perform a Pattern Recognition of related sequences, hidden in databases. Following a bottom up approach, the next refinement is to compare retrieved data to gather similar features, by dedicated clustering algorithms, driven by fuzzy logic, allowing us to perceive by intuition a common denominator for various genomic families and to anticipate likely future developments