New potentially biologically active sulfonamide derivatives of pentacyclic lupane-type triterpenoids, the sulfonamide group of which was bonded to C-17 of the triterpene skeleton through an amidoethane spacer, were synthesized via conjugation of 2-aminoethanesulfonamides to betulinic and betulonic acids in the presence of Mukaiyama reagent (2-bromo-1-methylpyridinium iodide).The main protease (3CLpro) of SARS-CoV and SARS-CoV-2 is a promising target for discovery of novel antiviral agents. In this paper, new possible inhibitors of 3CLpro with high predicted binding affinity were detected through multistep computer-aided molecular design and bioisosteric replacements. For discovery of prospective 3CLpro binders several virtual ligand libraries were created and combined docking was performed. Moreover, the molecular dynamics simulation was applied for evaluation of protein-ligand complexes stability. Besides, important molecular properties and ADMET pharmacokinetic profiles of possible 3CLpro inhibitors were assessed by in silico prediction.Named Data Networking (NDN) is a data-driven networking model that proposes to fetch data using names instead of source addresses. This new architecture is considered attractive for the Internet of Things (IoT) due to its salient features, such as naming, caching, and stateful forwarding, which allow it to support the major requirements of IoT environments natively. Nevertheless, some NDN mechanisms, such as forwarding, need to be optimized to accommodate the constraints of IoT devices and networks. This paper presents LAFS, a Learning-based Adaptive Forwarding Strategy for NDN-based IoT networks. LAFS enhances network performances while alleviating the use of its resources. The proposed strategy is based on a learning process that provides the necessary knowledge allowing network nodes to collaborate smartly and offer a lightweight and adaptive forwarding scheme, best suited for IoT environments. LAFS is implemented in ndnSIM and compared with state-of-the-art NDN forwarding schemes. As the obtained results demonstrate, LAFS outperforms the benchmarked solutions in terms of content retrieval time, request satisfactory rate, and energy consumption.A primary challenge in understanding disease biology from genome-wide association studies (GWAS) arises from the inability to directly implicate causal genes from association data. Integration of multiple-omics data sources potentially provides important functional links between associated variants and candidate genes. Machine-learning is well-positioned to take advantage of a variety of such data and provide a solution for the prioritization of disease genes. Yet, classical positive-negative classifiers impose strong limitations on the gene prioritization procedure, such as a lack of reliable non-causal genes for training. Here, we developed a novel gene prioritization tool-Gene Prioritizer (GPrior). It is an ensemble of five positive-unlabeled bagging classifiers (Logistic Regression, Support Vector Machine, Random Forest, Decision Tree, Adaptive Boosting), that treats all genes of unknown relevance as an unlabeled set. GPrior selects an optimal composition of algorithms to tune the model for each specific phenotype. Altogether, GPrior fills an important niche of methods for GWAS data post-processing, significantly improving the ability to pinpoint disease genes compared to existing solutions.Patients with rare diseases are a major challenge for healthcare systems. These patients face three major obstacles late diagnosis and misdiagnosis, lack of proper response to therapies, and absence of valid monitoring tools. We reviewed the relevant literature on first-generation artificial intelligence (AI) algorithms which were designed to improve the management of chronic diseases. The shortage of big data resources and the inability to provide patients with clinical value limit the use of these AI platforms by patients and physicians. In the present study, we reviewed the relevant literature on the obstacles encountered in the management of patients with rare diseases. C.I 58005 Examples of currently available AI platforms are presented. The use of second-generation AI-based systems that are patient-tailored is presented. The system provides a means for early diagnosis and a method for improving the response to therapies based on clinically meaningful outcome parameters. The system may offer a patient-tailored monitoring tool that is based on parameters that are relevant to patients and caregivers and provides a clinically meaningful tool for follow-up. The system can provide an inclusive solution for patients with rare diseases and ensures adherence based on clinical responses. It has the potential advantage of not being dependent on large datasets and is a dynamic system that adapts to ongoing changes in patients’ disease and response to therapy.Silver-Russell syndrome (SRS) is a rare imprinting disorder associated with prenatal and postnatal growth retardation. Loss of methylation (LOM) on chromosome 11p15 is observed in 40 to 60% of patients and maternal uniparental disomy (mUPD) for chromosome 7 (upd(7)mat) in ~5 to 10%. Patients with LOM or mUPD 14q32 can present clinically as SRS. Delta like non-canonical Notch ligand 1 (DLK1) is one of the imprinted genes expressed from chromosome 14q32. Dlk1-null mice display fetal growth restriction (FGR) but no genetic defects of DLK1 have been described in human patients born small for gestational age (SGA). We screened a cohort of SGA patients with a SRS phenotype for DLK1 variants using a next-generation sequencing (NGS) approach to search for new molecular defects responsible for SRS. Patients born SGA with a clinical suspicion of SRS and normal methylation by molecular testing at the 11p15 or 14q32 loci and upd(7)mat were screened for DLK1 variants using targeted NGS. Among 132 patients, only two rare variants of DLK1 were identified (NM_003836.6c.103 G > C (p.(Gly35Arg) and NM_003836.6 c.194 A > G p.(His65Arg)). Both variants were inherited from the mother of the patients, which does not favor a role in pathogenicity, as the mono-allelic expression of DLK1 is from the paternal-inherited allele. We did not identify any pathogenic variants in DLK1 in a large cohort of SGA patients with a SRS phenotype. DLK1 variants are not a common cause of SGA.