Endoscopic application of n-butyl-2-cyanoacrylate on esophagojejunal anastomotic leak: a case report

<p>Abstract</p> <p>Introduction</p> <p>This case report describes an esophagojejunal anastomotic leak following total gastrectomy for gastric cancer. The leak was treated successfully with endoscopic application of <it>n</it>-butyl-2-cyanoacrylate. This is the first case report on the endoscopic application of cyanoacrylate alone for the treatment of an anastomotic leak.</p> <p>Case presentation</p> <p>This report describes a case of a 68-year-old Caucasian man who underwent surgery for gastric cancer. He underwent total gastrectomy and esophagojejunal anastomosis with Roux-en-Y anastomosis plus transverse colectomy. An anastomotic leak was treated conservatively at first for a total of three weeks. However, the leak persisted; therefore, the decision was made to apply topical endoscopic <it>n</it>-butyl-2-cyanoacrylate.</p> <p>Conclusion</p> <p>The endoscopic application of <it>n</it>-butyl-2-cyanoacrylate alone can be used successfully to treat esophagojejunal anastomotic leakage.</p

Impact of a Periodic Power Source on a RES Microgrid

The aim of this article is to highlight the impact of a periodic power source, such as a tidal turbine, on the operation and sizing of an autonomous hybrid microgrid with photovoltaic panels and storage. The technique of hill climbing (repeated local search) is used to find the optimum combination of Renewable Energy Sources (RES) and storage units with respect to the required capital cost for various load curves and weather conditions. To model the operation of the microgrid devices, analytical and phenomenological models, have been used, which take into account the specifications of actual commercial devices. Six different case studies are presented, with and without a tidal generator, which are based on six different sets of electrical consumption data corresponding to the Euripus campus of the National &amp; Kapodistrian University of Athens (NKUA) in Psachna, Evia, Greece, and respective meteorological and tidal current data from the region. The results show that tidal energy may be used in a RES microgrid, where applicable, to satisfy the base load requirements, leading to a reduction in installed capacities of intermittent RES and storage, accompanied with cost reduction, especially in cases where a high load factor is observed or may be achieved, through demand response mechanisms. Such a hybrid microgrid configuration may be appropriate for regions where low velocity tidal and marine currents exist along with substantial solar and/or wind energy potential, such as the Mediterranean coast line and islands

Tackling Faults in the Industry 4.0 Era—A Survey of Machine-Learning Solutions and Key Aspects

The recent advancements in the fields of artificial intelligence (AI) and machine learning (ML) have affected several research fields, leading to improvements that could not have been possible with conventional optimization techniques. Among the sectors where AI/ML enables a plethora of opportunities, industrial manufacturing can expect significant gains from the increased process automation. At the same time, the introduction of the Industrial Internet of Things (IIoT), providing improved wireless connectivity for real-time manufacturing data collection and processing, has resulted in the culmination of the fourth industrial revolution, also known as Industry 4.0. In this survey, we focus on the vital processes of fault detection, prediction and prevention in Industry 4.0 and present recent developments in ML-based solutions. We start by examining various proposed cloud/fog/edge architectures, highlighting their importance for acquiring manufacturing data in order to train the ML algorithms. In addition, as faults might also occur from sources beyond machine degradation, the potential of ML in safeguarding cyber-security is thoroughly discussed. Moreover, a major concern in the Industry 4.0 ecosystem is the role of human operators and workers. Towards this end, a detailed overview of ML-based human&ndash;machine interaction techniques is provided, allowing humans to be in-the-loop of the manufacturing processes in a symbiotic manner with minimal errors. Finally, open issues in these relevant fields are given, stimulating further research

Tackling Faults in the Industry 4.0 Era—A Survey of Machine-Learning Solutions and Key Aspects

The recent advancements in the fields of artificial intelligence (AI) and machine learning (ML) have affected several research fields, leading to improvements that could not have been possible with conventional optimization techniques. Among the sectors where AI/ML enables a plethora of opportunities, industrial manufacturing can expect significant gains from the increased process automation. At the same time, the introduction of the Industrial Internet of Things (IIoT), providing improved wireless connectivity for real-time manufacturing data collection and processing, has resulted in the culmination of the fourth industrial revolution, also known as Industry 4.0. In this survey, we focus on the vital processes of fault detection, prediction and prevention in Industry 4.0 and present recent developments in ML-based solutions. We start by examining various proposed cloud/fog/edge architectures, highlighting their importance for acquiring manufacturing data in order to train the ML algorithms. In addition, as faults might also occur from sources beyond machine degradation, the potential of ML in safeguarding cyber-security is thoroughly discussed. Moreover, a major concern in the Industry 4.0 ecosystem is the role of human operators and workers. Towards this end, a detailed overview of ML-based human&ndash;machine interaction techniques is provided, allowing humans to be in-the-loop of the manufacturing processes in a symbiotic manner with minimal errors. Finally, open issues in these relevant fields are given, stimulating further research

The Combined Use of Platelet-Rich Plasma and Adipose-Derived Mesenchymal Stem Cells Promotes Healing. A Review of Experimental Models and Future Perspectives

Wound healing and tissue regeneration are a field of clinical medicine presenting high research interest, since various local and systematic factors can inhibit these processes and lead to an inferior result. New methods of healing enhancement constantly arise, which, however, require experimental validation before their establishment in everyday practice. Platelet-rich plasma (PRP) is a well-known autologous factor that promotes tissue healing in various surgical defects. PRP derives from the centrifugation of peripheral blood and has a high concentration of growth factors that promote healing. Recently, the use of adipose-derived mesenchymal stem cells (ADMSCs) has been thoroughly investigated as a form of wound healing enhancement. ADMSCs are autologous stem cells deriving from fat tissue, with a capability of differentiation in specific cells, depending on the micro-environment that they are exposed to. The aim of the present comprehensive review is to record the experimental studies that have been published and investigate the synergistic use of PRP and ADMSC in animal models. The technical aspects of experimentations, as well as the major results of each study, are discussed. In addition, the limited clinical studies including humans are also reported. Future perspectives are discussed, along with the limitations of current studies on the long-term follow up needed on efficacy and safety