The study of antifungal activities of magnesium oxide and copper oxide nanoparticles against different species of Aspergillus

Background & Objective: Food can be contaminated with various fungi. The use of nanotechnology, especially metal oxides can reduce this contamination. The aim of this study was to investigate the antifungal effect of magnesium oxide and copper oxide nanoparticles against Aspergillus species that are important in food hygiene. Materials & Methods: Magnesium oxide and copper oxide nanoparticles were synthesized chemically, then their morphology and size were investigated by transmission electron microscopy, scanning electron microscopy, X-ray diffraction and zetasizer. MIC and MFC of these nanoparticles against Aspergillus species were examined individually and in combination with each other by micro dilution method in saboraud dextrose broth and saboraud dextrose agar media and FIC was calculated. Results: The size of nanoparticles was between 10 to 60 nm. They had different forms and high purity. The mean MIC and MFC values of magnesium oxide nanoparticles for the species of A. flavus, A. fumigatus, A. niger and A. parasiticus were 10.1 and 10.31 mg/ml, respectively. These values for copper oxide nanoparticles were 10.25 and 10.08, respectively. Most inhibitory and fungicidal effect of these nanoparticles was on A. niger and A. fumigatus respectively. Since FIC index was greater than 1, there was no interaction. The mean MIC value of the two nanoparticles combination was 9.49 mg/ml. Conclusions: This study showed that each of magnesium oxide or copper oxide nanoparticles as anti-fungal substances could have inhibitory and fungicidal properties individually, but their combination do not have any interacting effect

Mobile Apps for COVID-19 Detection and Diagnosis for Future Pandemic Control: Multidimensional Systematic Review

BackgroundIn the modern world, mobile apps are essential for human advancement, and pandemic control is no exception. The use of mobile apps and technology for the detection and diagnosis of COVID-19 has been the subject of numerous investigations, although no thorough analysis of COVID-19 pandemic prevention has been conducted using mobile apps, creating a gap. ObjectiveWith the intention of helping software companies and clinical researchers, this study provides comprehensive information regarding the different fields in which mobile apps were used to diagnose COVID-19 during the pandemic. MethodsIn this systematic review, 535 studies were found after searching 5 major research databases (ScienceDirect, Scopus, PubMed, Web of Science, and IEEE). Of these, only 42 (7.9%) studies concerned with diagnosing and detecting COVID-19 were chosen after applying inclusion and exclusion criteria using the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) protocol. ResultsMobile apps were categorized into 6 areas based on the content of these 42 studies: contact tracing, data gathering, data visualization, artificial intelligence (AI)–based diagnosis, rule- and guideline-based diagnosis, and data transformation. Patients with COVID-19 were identified via mobile apps using a variety of clinical, geographic, demographic, radiological, serological, and laboratory data. Most studies concentrated on using AI methods to identify people who might have COVID-19. Additionally, symptoms, cough sounds, and radiological images were used more frequently compared to other data types. Deep learning techniques, such as convolutional neural networks, performed comparatively better in the processing of health care data than other types of AI techniques, which improved the diagnosis of COVID-19. ConclusionsMobile apps could soon play a significant role as a powerful tool for data collection, epidemic health data analysis, and the early identification of suspected cases. These technologies can work with the internet of things, cloud storage, 5th-generation technology, and cloud computing. Processing pipelines can be moved to mobile device processing cores using new deep learning methods, such as lightweight neural networks. In the event of future pandemics, mobile apps will play a critical role in rapid diagnosis using various image data and clinical symptoms. Consequently, the rapid diagnosis of these diseases can improve the management of their effects and obtain excellent results in treating patients