Enhanced antibacterial activity of streptomycin against some human pathogens using green synthesized silver nanoparticles

AbstractThe development of eco-friendly technologies in nanoparticle synthesis is of utmost importance in order to expand their biological horizons. In the present study, bioreduction of AgNO3 into AgNPs using various leaf extracts of Ficus virens is explained. The resulting AgNPs were characterized by UV–vis spectroscopy, dynamic light scattering (DLS), X-ray diffraction (XRD), Fourier transform infrared spectroscopy (FTIR) and transmission electron microscopy (TEM). Synthesis of AgNPs was confirmed by color change from transparent to brown with maximum absorption at 420 nm due to surface plasmon resonance of AgNPs. X-ray diffraction studies showed that the biosynthesized AgNPs were crystalline in nature, and TEM analysis showed spherical shape of the nanoparticles with size ranging from 4.98 to 29 nm. FTIR study indicates that mainly –C = O, -OH and N-H groups in leaf extracts are involved in the reduction of Ag+ ions to Ag atoms, and proteins are responsible for stabilizing the silver nanoparticles. The synthesized AgNPs showed significant antibacterial activity against Gram positive and gram negative human bacterial pathogens. The results showed that AgNPs also synergistically enhance (2.02–57.98%) the antibacterial activity of streptomycin, a common antibiotic. With this approach, AgNPs can be used as a new generation of antimicrobial agents for successful development of drug delivery

Cyber Attacks in Cyber-Physical Microgrid Systems: A Comprehensive Review

The importance of and need for cyber security have increased in the last decade. The critical infrastructure of the country, modeled with cyber-physical systems (CPS), is becoming vulnerable because of a lack of efficient safety measures. Attackers are becoming more innovative, and attacks are becoming undetectable, thereby causing huge risks to these systems. In this scenario, intelligent and evolving detection methods should be introduced to replace basic and outworn methods. The ability of artificial intelligence (AI) to analyze data and predict outcomes has created an opportunity for researchers to explore the power of AI in cyber security. This article discusses new-age intelligence and smart techniques such as pattern recognition models, deep neural networks, generative adversarial networks, and reinforcement learning for cyber security in CPS. The differences between the traditional security methods used in information technology and the security methods used in CPS are analyzed, and the need for a transition into intelligent methods is discussed in detail. A deep neural network-based controller that detects and mitigates cyber attacks is designed for microgrid systems. As a case study, a stealthy local covert attack that overcomes the existing microgrid protection is modeled. The ability of the DNN controller to detect and mitigate the SLCA is observed. The experiment is performed in a simulation and also in real-time to analyze the effectiveness of AI in cyber security

An artificial intelligence system applied to recurrent cytogenetic aberrations and genetic progression scores predicts MYC rearrangements in large B‐cell lymphoma

Abstract Diffuse large B‐cell lymphoma (DLBCL), the most common type of non‐Hodgkin lymphoma, is characterized by MYC rearrangements (MYC R) in up to 15% of cases, and these have unfavorable prognosis. Due to cryptic rearrangements and variations in MYC breakpoints, MYC R may be undetectable by conventional methods in up to 10%–15% of cases. In this study, a retrospective proof of concept study, we sought to identify recurrent cytogenetic aberrations (RCAs), generate genetic progression scores (GP) from RCAs and apply these to an artificial intelligence (AI) algorithm to predict MYC status in the karyotypes of published cases. The developed AI algorithm is validated for its performance on our institutional cases. In addition, cytogenetic evolution pattern and clinical impact of RCAs was performed. Chromosome losses were associated with MYC‐, while partial gain of chromosome 1 was significant in MYC R tumors. MYC R was the sole driver alteration in MYC‐rearranged tumors, and evolution patterns revealed RCAs associated with gene expression signatures. A higher GPS value was associated with MYC R tumors. A subsequent AI algorithm (composed of RCAs + GPS) obtained a sensitivity of 91.4 and specificity of 93.8 at predicting MYC R. Analysis of an additional 59 institutional cases with the AI algorithm showed a sensitivity and specificity of 100% and 87% each with positive predictive value of 92%, and a negative predictive value of 100%. Cases with a MYC R showed a shorter survival