Are the Organoid Models an Invaluable Contribution to ZIKA Virus Research?

Abstract: In order to prevent new pathogen outbreaks and avoid possible new global health threats, it is important to study the mechanisms of microbial pathogenesis, screen new antiviral agents and test new vaccines using the best methods. In the last decade, organoids have provided a groundbreaking opportunity for modeling pathogen infections in human brains, including Zika virus (ZIKV) infection. ZIKV is a member of the Flavivirus genus, and it is recognized as an emerging infectious agent and a serious threat to global health. Organoids are 3D complex cellular models that offer an inscale organ that is physiologically alike to the original one, useful for exploring the mechanisms behind pathogens infection; additionally, organoids integrate data generated in vitro with traditional tools and often support those obtained in vivo with animal model. In this mini-review the value of organoids for ZIKV research is examined and sustained by the most recent literature. Within a 3D viewpoint, tissue engineered models are proposed as future biological systems to help in deciphering pathogenic processes and evaluate preventive and therapeutic strategies against ZIKV. The next steps in this field constitute a challenge that may protect people and future generations from severe brain defect

Challenges and Barriers of Wireless Charging Technologies for Electric Vehicles

Electric vehicles could be a significant aid in lowering greenhouse gas emissions. Even though extensive study has been done on the features and traits of electric vehicles and the nature of their charging infrastructure, network modeling for electric vehicle manufacturing has been limited and unchanging. The necessity of wireless electric vehicle charging, based on magnetic resonance coupling, drove the primary aims for this review work. Herein, we examined the basic theoretical framework for wireless power transmission systems for EV charging and performed a software-in-the-loop analysis, in addition to carrying out a performance analysis of an EV charging system based on magnetic resonance. This study also covered power pad designs and created workable remedies for the following issues: (i) how power pad positioning affected the function of wireless charging systems and (ii) how to develop strategies to keep power efficiency at its highest level. Moreover, safety features of wireless charging systems, owing to interruption from foreign objects and/or living objects, were analyzed, and solutions were proposed to ensure such systems would operate as safely and optimally as possible

Self-Organized Ordering of Nanostructures Produced by Ion-Beam Sputtering

We study the self-organized ordering of nanostructures produced by ion-beam sputtering (IBS) of targets amorphizing under irradiation. By introducing a model akin to models of pattern formation in aeolian sand dunes, we extend consistently the current continuum theory of erosion by IBS. We obtain new non-linear effects responsible for the in-plane ordering of the structures, whose strength correlates with the degree of ordering found in experiments. Our results highlight the importance of redeposition and surface viscous flow to this nanopattern formation process.Comment: 4 pages, 2 figure

Oral health conditions in an Albanian adolescent population: An epidemiological study

The aim of this study was to determine the oral health conditions of an adolescent population of Tirana

Nucleation and growth of platelets in hydrogen-ion-implanted silicon

H ion implantation into crystalline Si is known to result in the precipitation of planar defects in the form of platelets. Hydrogen-platelet formation is critical to the process that allows controlled cleavage of Si along the plane of the platelets and subsequent transfer and integration of thinly sliced Si with other substrates. Here we show that H-platelet formation is controlled by the depth of the radiation-induced damage and then develop a model that considers the influence of stress to correctly predict platelet orientation and the depth at which platelet nucleation density is a maximum.This work was supported by the U.S. Department of Energy, Office of Basic Energy Sciences

The formation and analysis of thin film high temperature superconductors

Thin films of high temperature superconductors have been fabricated using a variety of physical vapor deposition techniques. Recent results of HTS thin films produced by coevaporation, sputtering and laser deposition will be briefly reviewed. In addition some examples of the utility of high energy ion backscattering for the analysis of film stoichiometry will be given. 34 refs., 6 figs

Excimer laser surface modification: Process and properties

Surface modification can improve materials for structural, tribological, and corrosion applications. Excimer laser light has been shown to provide a rapid means of modifying surfaces through heat treating, surface zone refining, and mixing. Laser pulses at modest power levels can easily melt the surfaces of many materials. Mixing within the molten layer or with the gas ambient may occur, if thermodynamically allowed, followed by rapid solidification. The high temperatures allow the system to overcome kinetic barriers found in some ion mixing experiments. Alternatively, surface zone refinement may result from repeated melting-solidification cycles. Ultraviolet laser light couples energy efficiently to the surface of metallic and ceramic materials. The nature of the modification that follows depends on the properties of the surface and substrate materials. Alloying from both gas and predeposited layer sources has been observed in metals, semiconductors, and ceramics as has surface enrichment of Cr by zone refinement of stainless steel. Rapid solidification after melting often results in the formation of nonequilibrium phases, including amorphous materials. Improved surface properties, including tribology and corrosion resistance, are observed in these materials

The upcoming role of Artificial Intelligence (AI) for retinal and glaucomatous diseases

: In recent years, the role of artificial intelligence (AI) and deep learning (DL) models is attracting increasing global interest in the field of ophthalmology. DL models are considered the current state-of-art among the AI technologies. In fact, DL systems have the capability to recognize, quantify and describe pathological clinical features. Their role is currently being investigated for the early diagnosis and management of several retinal diseases and glaucoma. The application of DL models to fundus photographs, visual fields and optical coherence tomography (OCT) imaging has provided promising results in the early detection of diabetic retinopathy (DR), wet age-related macular degeneration (w-AMD), retinopathy of prematurity (ROP) and glaucoma. In this review we analyze the current evidence of AI applied to these ocular diseases, as well as discuss the possible future developments and potential clinical implications, without neglecting the present limitations and challenges in order to adopt AI and DL models as powerful tools in the everyday routine clinical practice

In situ observation of defect growth beyond the irradiated region in yttria-stabilized zirconia induced by 400 keV xenon ion-beam at -90 and 30{degrees}C

Single crystals of yttria-stabilized zirconia were irradiated with 400 keV Xe ion-beam at room temperature and minus 90 degrees centigrade. Defect growth was monitored in situ with Rutherford Backscattering and ion channeling techniques using a 2 MeV He ion beam