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Although a very rich list of classes of space-time covariance functions exists, specific tools for selecting the appropriate class for a given data set are needed. Thus, the main topic of this paper is to present the new R package, covatest, which can be used for testing some characteristics of a covariance function, such as symmetry, separability and type of non-separability, as well as for testing the adequacy of some classes of space-time covariance models. These last aspects can be relevant for choosing a suitable class of covariance models. The proposed results have been applied to an environmental case study.

There were two objectives for this study. First, it focused on eight lipids having cholesterol as hydrophobic tails, carbamate linkers, and different polar headgroups. Second, it aimed to study the physicochemical properties of those eight lipids, including DNA binding, size, zeta potential, and the transfection efficiency. Cholesterol-1,2-diaminoethane and tetramethylguanidinium conjugated lipid showed the highest transfection efficacy into human embryonic kidney cells cells. The optimal formulation of this lipid was found to be 1:1 (weight/weight) for cationic lipid/DOPE, and 1:20 for DNA/liposome. GFP expression experiments further revealed that the liposome exhibited higher transfection efficiency under a 10−40% serum condition than LipofectamineTM 2000.

BackgroundAfter 18 months of responding to the COVID-19 pandemic, there is still no agreement on the optimal combination of mitigation strategies. The efficacy and collateral damage of pandemic policies are dependent on constantly evolving viral epidemiology as well as the volatile distribution of socioeconomic and cultural factors. This study proposes a data-driven approach to quantify the efficacy of the type, duration, and stringency of COVID-19 mitigation policies in terms of transmission control and economic loss, personalised to individual countries.MethodsWe present What If…?, a deep learning pandemic-policy-decision-support algorithm simulating pandemic scenarios to guide and evaluate policy impact in real time. It leverages a uniquely diverse live global data-stream of socioeconomic, demographic, climatic, and epidemic trends on over a year of data (04/2020-06/2021) from 116 countries. The economic damage of the policies is also evaluated on the 29 higher income countries for which data is available. The efficacy and economic damage estimates are derived from two neural networks that infer respectively the daily R-value (RE) and unemployment rate (UER). Reinforcement learning then pits these models against each other to find the optimal policies minimising both RE and UER.FindingsThe models made high accuracy predictions of RE and UER (average mean squared errors of 0.043 [CI95: 0.042-0.044] and 4.473% [CI95: 2.619-6.326] respectively), which allow the computation of country-specific policy efficacy in terms of cost and benefit. In the 29 countries where economic information was available, the reinforcement learning agent suggested a policy mix that is predicted to outperform those implemented in reality by over 10-fold for RE reduction (0.250 versus 0.025) and at 28-fold less cost in terms of UER (1.595% versus 0.057%).ConclusionThese results show that deep learning has the potential to guide evidence-based understanding and implementation of public health policies.

Thoracic aortic rupture may present with subtle clinical and CT-angiography findings. Recognition of the imaging features of early rupture is key for timely diagnosis and treatment. This report presents a new sign of incipient proximal thoracic aortic rupture on CT-angiography.

Interventional Oncology treatments grant low-risk mini-invasive alternatives to surgery for cancer patients. Percutaneous ablative therapies represent a cornerstone for treatment of liver cancer patients. Among these, a newly emerging one is represented by electrochemotherapy. Improvements in analgesia and sedation can nowadays offer optimal support for ablative procedures, serving as a valid alternative to general anesthesia. The intention of this retrospective monocentric study is to report our preliminary experience on feasibility and safety of electrochemotherapy for treatment of complex liver tumors unfit for thermal ablation, using analgosedation instead of general anesthesia. Five patients were enrolled in the study, undergoing electrochemotherapy under analgosedation. Mean procedural time and hospitalization time were recorded. Immediate post-procedural cone-beam CT showed complete coverage of the lesion without complications. One-month CT examination showed an overall response rate of 100% (four complete responses, one partial response). Electrochemotherapy under analgosedation seems to be a safe, feasible, and effective option for liver cancer patients not amenable to other ablative techniques.

Irisin is a recently discovered cytokine, better known as an exercise-induced myokine, produced primarily in skeletal muscle tissue as a response to exercise. Although the skeleton was initially identified as the main target of Irisin, its action is also proving effective in many other tissues. Physical activity determines a series of beneficial effects on health, including the possibility of counteracting the damage that is caused by arthritis to the cartilage of people suffering from osteoarthritis. Nevertheless, up to now, the studies that have taken into consideration the possible involvement of Irisin on the well-being of cartilage tissue are particularly limited. In this study, we postulated that the protective effect of physical activity on cartilage tissue may depend on the paracrine action of Irisin secreted during exercise; therefore, we analyzed the effects of Irisin, in vitro, on chondrogenic differentiation. To achieve this goal, three-dimensional cultures of commercially available human articular chondrocytes (HACs) were treated with the molecule under study. Our results revealed new crosstalk mechanisms between muscle and cartilage tissue. Furthermore, the confirmation of Irisin ability to induce chondrogenic differentiation could favor the development of exercise-mimetic drugs, with application relevance for patients who cannot perform physical activity.

This study presents the results of severe accelerated tests carried out on four encapsulated amorphous silicon (a-Si) mini-modules. All the a-Si mini-modules were exposed to a 85 °C and 85% relative humidity damp heat (DH) prolonged treatment for 5000 h representing five times the duration specified by the IEC 61215 standard for qualification tests. For two of the four mini-modules, the DH test was preceded by a severe UV preconditioning, by applying 30 times the dose of 15 kWh/m2 at a temperature of 50 °C as prescribed by the IEC 61215 standard, in order to enhance the degradation during the following DH test and to reduce the overall testing time. I–V curves were plotted with a time step of 100 h under standard test conditions (STC) using a class A solar simulator and a source meter in order to monitor the degradation throughout both the tests. A visual inspection with photographic capturing was also performed at each stage to detect the apparent defects. Corrosion observed after 2000 h owing to the ingress of humidity is explained here by two possible infiltration paths in the layers of the mini-modules. Delamination occurred after 5000 h for the PV mini-modules which underwent the extended DH test. After 5000 h of damp heat testing, the degradation of the maximal power (Pmax) was found to be slightly accelerated for the a-Si mini-modules that were previously exposed to a severe UV preconditioning, with a value reaching 80% of its initial value, whereas, for the others only subjected to the prolonged DH test, the maximal power remained above 80% of its initial value. In all cases, the mini-modules seemed highly reliable with no failure after 5000 h of accelerated testing, and, based on an equivalent time of 20 years for 1000 h of accelerated test, they would exhibit a limited degradation rate of 0.2%/year in outdoor field conditions.

Interventional radiology, and particularly interventional oncology, represents one of the medical subspecialties in which technological advancements and innovations play an utterly fundamental role. Artificial intelligence, consisting of big data analysis and feature extrapolation through computational algorithms for disease diagnosis and treatment response evaluation, is nowadays playing an increasingly important role in various healthcare fields and applications, from diagnosis to treatment response prediction. One of the fields which greatly benefits from artificial intelligence is interventional oncology. In addition, digital health, consisting of practical technological applications, can assist healthcare practitioners in their daily activities. This review aims to cover the most useful, established, and interesting artificial intelligence and digital health innovations and updates, to help physicians become more and more involved in their use in clinical practice, particularly in the field of interventional oncology.

Opioids are the most effective drugs used for the management of moderate to severe pain; however, their chronic use is often associated with numerous adverse effects. Some results indicate the involvement of oxidative stress as well as of proteasome function in the development of some opioid-related side effects including analgesic tolerance, opioid-induced hyperalgesia (OIH) and dependence. Based on the evidence, this study investigated the impact of morphine, buprenorphine or tapentadol on intracellular reactive oxygen species levels (ROS), superoxide dismutase activity/gene expression, as well as β2 and β5 subunit proteasome activity/biosynthesis in SH-SY5Y cells. Results showed that tested opioids differently altered ROS production and SOD activity/biosynthesis. Indeed, the increase in ROS production and the reduction in SOD function elicited by morphine were not shared by the other opioids. Moreover, tested drugs produced distinct changes in β2(trypsin-like) and β5(chymotrypsin-like) proteasome activity and biosynthesis. In fact, while prolonged morphine exposure significantly increased the proteolytic activity of both subunits and β5 mRNA levels, buprenorphine and tapentadol either reduced or did not alter these parameters. These results, showing different actions of the selected opioid drugs on the investigated parameters, suggest that a low µ receptor intrinsic efficacy could be related to a smaller oxidative stress and proteasome activation and could be useful to shed more light on the role of the investigated cellular processes in the occurrence of these opioid drug side effects.

The capabilities of additive manufacturing (AM) techniques have been extensively examined in the literature. However, scientific gaps persist on the feasibility of realizing a coated component manufactured by using various materials processed by combining different AM processes. From this perspective, this study focuses on the manufacturing of a directed energy deposition (DED) coating by using 18Ni (300) maraging steel powder on AISI 316L components realized by laser-powder bed fusion (L-PBF), in order to assess the production of components with high geometrical complexity combined with high mechanical surface properties in selected areas. The quality of the manufactured coatings was assessed in-process through the implementation of an optical monitoring system and real-time image processing. In addition, an in-depth metallurgical analysis (microstructural and chemical) of the interface between the DED coating and the L-PBF component was carried out. Finally, hardness tests were performed on both the as-deposited and heat-treated coatings to confirm the high mechanical performance of the final component surface. The results revealed the potential of producing cost-effective and geometrically complex parts, such as molds or tools with internal cooling channels, that implement mechanically high-performance surfaces.