The Emerging Role of Extracellular Vesicles and Autophagy Machinery in NASH—Future Horizons in NASH Management

Non-alcoholic fatty liver disease (NAFLD) is considered the most frequent chronic hepatic disease in the general population, while it is the first cause of liver transplantation in the US. NAFLD patients will subsequently develop non-alcoholic steatohepatitis (NASH), which is characterized by aberrant hepatocellular inflammation with or without the presence of fibrosis. The lack of specific biomarkers and therapeutic strategies makes non-alcoholic steatohepatitis (NASH) management a difficult task for clinicians. Extracellular vesicles (EVs) constitute a heterogenic population of vesicles produced by inward or outward plasma-membrane budding. There is an emerging connection between autophagy EVs production, via an unconventional non-degradative procedure. Alterations in the amount of the secreted EVs and the cargo they carry are also involved in the disease progression and development of NASH. Autophagy constitutes a multistep lysosomal degradative pathway that reassures cell homeostasis and survival under stressful conditions, such as oxygen and energy deprivation. It prevents cellular damage by eliminating defected proteins or nοn-functional intracellular organelles. At the same time, it reassures the optimal conditions for the cells via a different mechanism that includes the removal of cargo via the secretion of EVs. Similarly, autophagy machinery is also associated with the pathogenetic mechanism of NAFLD, while it has a significant implication for the progression of the disease and the development of NASH. In this review, we will shed light on the interplay between autophagy and EVs in NASH, the emerging connection of EVs production with the autophagy pathway, and their possible manipulation for developing future therapeutic strategies for NASH.     

Urine Metabolomic Profile of Breast- versus Formula-Fed Neonates Using a Synbiotic-Enriched Formula

The aim of this study was to compare the urine metabolic fingerprint of healthy neonates exclusively breastfed with that of neonates fed with a synbiotic-enriched formula (Rontamil^® Complete 1) at four time points (the 3rd and 15th days of life and the 2nd and 3rd months). The determination of urine metabolic fingerprint was performed using NMR metabolomics. Multivariate data analyses were performed with SIMCA-P 15.0 software and R language. Non-distinct profiles for both groups (breastfeeding and synbiotic formula) for the two first time points (3rd and 15th days of life) were detected, whereas after the 2nd month of life, a discrimination trend was observed between the two groups, which was further confirmed at the 3rd month of life. A clear discrimination of the synbiotic formula samples was evident when comparing the metabolites taken in the first days of life (3rd day) with those taken in the 2nd and 3rd months of life. In both cases, OPLS-DA models explained more than 75% of the metabolic variance. Non-distinct metabolomic profiles were obtained between breastfed and synbiotic-formula-fed neonates up to the 15th day of life. Discrimination trends were observed only after the 2nd month of the study, which could be attributed to breastfeeding variations and the consequent dynamic profile of urine metabolites compared to the stable ingredients of the synbiotic formula.     

Outcome Prediction in Critically-Ill Patients with Venous Thromboembolism and/or Cancer Using Machine Learning Algorithms: External Validation and Comparison with Scoring Systems

Intensive care unit (ICU) patients with venous thromboembolism (VTE) and/or cancer suffer from high mortality rates. Mortality prediction in the ICU has been a major medical challenge for which several scoring systems exist but lack in specificity. This study focuses on two target groups, namely patients with thrombosis or cancer. The main goal is to develop and validate interpretable machine learning (ML) models to predict early and late mortality, while exploiting all available data stored in the medical record. To this end, retrospective data from two freely accessible databases, MIMIC-III and eICU, were used. Well-established ML algorithms were implemented utilizing automated and purposely built ML frameworks for addressing class imbalance. Prediction of early mortality showed excellent performance in both disease categories, in terms of the area under the receiver operating characteristic curve (AUC–ROC): VTE-MIMIC-III 0.93, eICU 0.87, cancer-MIMIC-III 0.94. On the other hand, late mortality prediction showed lower performance, i.e., AUC–ROC: VTE 0.82, cancer 0.74–0.88. The predictive model of early mortality developed from 1651 VTE patients (MIMIC-III) ended up with a signature of 35 features and was externally validated in 2659 patients from the eICU dataset. Our model outperformed traditional scoring systems in predicting early as well as late mortality. Novel biomarkers, such as red cell distribution width, were identified.     

Introduction of the Asymmetric Cryptography in GSM,GPRS, UMTS,and Its Public Key Infrastructure Integration

The logic ruling the user and network authentication as well as the data ciphering in the GSM architecture is characterized, regarding the transferring of the parameters employed in these processes, by transactions between three nodes of the system, that is the MS, actually the SIM, the visited MSC/VLR, and the AuC, which is attached to the HLR in most cases. The GPRS and the UMTS architecture carry the heritage of the GSM's philosophy regarding the user/network authentication and the data ciphering. So, the corresponding three nodes (MS, VLR, and HLR) of these systems are involved as well in the authentication and data ciphering procedures. Moreover, the methods of the conventional cryptography have been adopted by all three systems. This paper describes in brief the subscriber authentication and data ciphering, as they are recommended by the Specifications for all three aforementioned systems. Based on what the specifications define, we pinpoint the vulnerable points of the systems, exposed to third party attacks, and propose asymmetric cryptography procedures for their coverage, consisting of the introduction of public–private key pairs for the transactions between the VLR-HLR, as well as the MS-VLR. On the other hand, the nature of the services constituting a Public Key Infrastructure (PKI) renders the telecommunication operators the main candidates for the development of PKIs fully or partially fledged. The private–public key pair, stored by the PLMN operator in the SIM, for the GSM/GPRS case, or in the USIM for the UMTS case and created to deal initially with internal system functions, can easily be extended, adopted and employed in secure e/m-transactions, if bound to a digital certificate, in the case the PLMN operator supports PKI services as well.     

Multifunctional epoxy composites modified with a graphene nanoplatelet/carbon nanotube hybrid

Epoxies are a class of thermoset polymers which find use in high performance applications. However, epoxies are inherently brittle and are poor conductors of electricity, which limits their ability to be employed in functional applications. Carbon nanomaterials have attracted considerable attention as filler materials, due to their combination of outstanding properties. In the present work, an epoxy polymer was modified with a hybrid nanofiller, consisting of graphene nanoplatelets (GNPs) and carbon nanotubes (CNTs) at a mass ratio of 9:1, using three-roll milling. Addition of 1 wt% resulted in an increase of eight orders of magnitude in the electrical conductivity and a 182% increase in the fracture energy, G_IC, of the epoxy. CNTs contributed greatly in the reduction of the percolation threshold, which was 10 times lower than that of conventional GNP/epoxy composites, while the increase in toughness was entirely attributed to the GNPs, predominantly through the mechanism of crack deflection. The toughening contribution of the hybrid nanofiller was theoretically calculated using analytical modeling, which showed excellent agreement between the predicted and experimental values of G_IC.     

Chromium content of selected Greek foods

The total chromium content of a wide variety of Greek foods was determined by graphite furnace atomic absorption spectroscopy (GFAAS). Meat, fish and seafood, cereals and pulses were rich sources of chromium (>0.100 microg/g). Fruits, milk, oils and fats and sugar were poor sources. Differences in chromium content were found between different food classes from Greece and those from some other countries. Based on available food consumption data and chromium levels in this study, it was estimated that the chromium intake of Greeks is 143 microg/day, with vegetables, cereals and meat being the main contributors.