9 research outputs found
siRNA a promising tool for diabetes complications
No full textRNA interference (RNAi) is a naturally occurring process of gene regulation that has been harnessed to silence specific genes in various cell types, including those involved in diabetes complications. Small interfering RNA (siRNA) is an RNA molecule that activates RNAi and targets specific genes for degradation. Recent research has demonstrated that siRNA holds promise as a tool for treating diabetes complications, including diabetic neuropathy, retinopathy, and nephropathy. In preclinical studies, siRNA has been shown to effectively target genes involved in these complications, resulting in improved clinical outcomes. One potential advantage of siRNA therapy is its ability to selectively target specific genes without disrupting endogenous mRNA pathways, which reduces the risk of off-target effects. Additionally, siRNA has the potential to provide long-lasting effects with a single dose, which could result in reduced treatment frequency and improved patient compliance. While promising preclinical results have been, several challenges still need to be addressed before siRNA can be used in clinical practice. These include delivery issues, as siRNA molecules rapidly degrade in the bloodstream and cannot cross cell membranes without assistance. Despite these challenges, the potential of siRNA as a tool for treating diabetes complications is exciting, and further research is needed to determine its safety and efficacy in clinical trials. With continued investigation and refinement, siRNA has the potential to become an important therapeutic tool for the treatment of diabetes complications, improving patient outcomes and quality of life
Corrigendum to “Synergistic effects of Momordica charantia, Nigella sativa, and Anethum graveolens on metabolic syndrome targets: In vitro enzyme inhibition and in silico analyses” [Heliyon Volume 10, Issue 2, January 2024, Article e24907]
No full textSynergistic effects of Momordica charantia, Nigella sativa, and Anethum graveolens on metabolic syndrome targets: In vitro enzyme inhibition and in silico analyses
No full textMomordica charantia, Nigella sativa, and Anethum graveolens are established medicinal plants possessing noted anti-diabetic and anti-obesity properties. However, the molecular mechanisms underscoring their inhibitory effects on pancreatic lipase, α-glucosidase, and HMG-CoA reductase remain unexplored. This study aimed to elucidate the efficacy of various NS, MC, and AG blends in modulating the enzymatic activity of pancreatic lipase, HMG-CoA reductase, and a-glucosidase, utilizing an integrative approach combining in vitro assessments and molecular modeling techniques. A factorial design matrix generated eight distinct concentration combinations of NS, MC, and AG, subsequently subjected to in vitro enzyme inhibition assays. Molecular docking analyses using AutoDock Vina, molecular dynamics simulations, MMPBSA calculations, and principal component analysis, were executed with Gromacs to discern the interaction dynamics between the compounds and target enzymes. A formulation comprising NS:MC:AG at a 215:50:35 μg/mL ratio yielded significant inhibition of pancreatic lipase (IC50: 74.26 ± 4.27 μg/mL). Moreover, a concentration combination of 215:80:35 μg/mL effectively inhibited both α-glucosidase (IC50: 66.09 ± 3.98 μg/mL) and HMGCR (IC50: 129.03 μg/mL). Notably, MC-derived compounds exhibited superior binding affinity towards all three enzymes, compared to their reference molecules, with diosgenin, Momordicoside I, and diosgenin displaying binding affinities of −11.0, −8.8, and −7.9 kcal/mol with active site residues of pancreatic lipase, α-glucosidase, and HMGCR, respectively. Further, 100 ns molecular dynamics simulations revealed the formation and stabilization of non-bonded interactions between the compounds and the enzymes’ active site residues. Through a synergistic application of in vitro and molecular modeling methodologies, this study substantiated the potent inhibitory activity of the NS:MC:AG blend (at a ratio of 215:80:35 μg/mL) and specific MC compounds against pancreatic lipase, α-glucosidase, and HMGCR. These findings provide invaluable insights into the molecular underpinnings of these medicinal plants' anti-diabetic and anti-obesity effects and may guide future therapeutic development
Synthesis of cadmium sulfide-reduced graphene oxide nanocomposites by pulsed laser ablation in liquid for the enhanced photocatalytic reactions in the visible light
No full textPulsed laser synthesis in liquid of efficient visible-light-active ZnO/rGO nanocomposites for improved photo-catalytic activity
No full textGiant Second-Order Nonlinearity and Anisotropy of Large-Sized Few-Layer SnS with Ferroelectric Stacking
No full textThe giant second-order nonlinearity of SnS with ferroelectric stacking is reported. Based on theoretical calculations, the susceptibility of second harmonic generation (SHG) from SnS with ferroelectric stacking is up to 1354 pm V−1, which is three orders of magnitude higher than the values of traditional nonlinear crystals such as BBO and KTP. The SHG from ferroelectric SnS few layers is experimentally measured and its intensity is found to be 131 times larger than that of a MoS2 monolayer under the same experimental conditions, with a photon energy of 1.55 eV. The SHG susceptibility is determined to be on the order of 100 pm V−1. Numerous SnS flakes are systematically investigated using polarization-resolved SHG. Micrometer-sized flakes with a single domain are found, and their SHG anisotropic patterns fit well with the theoretical calculations derived from first-principles methods. The variation in SHG anisotropic patterns, attributed to SHG interference from multiple domains, is investigated both theoretically and experimentally. Additionally, the impact of stacking disorder on the SHG anisotropic pattern is explored. It is demonstrated that polarization-resolved SHG microscopy is a valuable tool for identifying domains in SnS flakes and examining stacking disorder.補正完畢DE
Anisotropy of Second-Harmonic Generation in SnSe Flakes with Ferroelectric Stacking
No full textThe second-harmonic generation (SHG) susceptibilities of few-layer SnSe with ferroelectric stacking are investigated using both experimental and theoretical approaches. Theoretical calculations predict a maximum bulk SHG susceptibility of 2444 pm V−1 at 1.2 eV, which is three orders of magnitude larger than that of typical nonlinear crystals. Experimentally, a maximum value of 1424 pm V−1 at 1.19 eV in close agreement with the theoretical prediction is measured. The anisotropic SHG patterns observed experimentally align with theoretical predictions based on the material's point group symmetry. The photon-energy dependence of SHG patterns is also measured within the range of 1.19 to 1.55 eV to explore the relative strengths of various SHG susceptibilities. Notably, the measured
is significantly larger than the theoretical value of bulk AC-SnSe, likely due to the strain effects and the mixing of ferroelectric and antiferroelectric stacking configurations in the practical SnSe few-layer samples.補正完畢DE
A secure edge computing model using machine learning and IDS to detect and isolate intruders.
No full textThe article presents a secure edge computing model that utilizes machine learning for intrusion detection and isolation. It addresses the security challenges arising from the rapid expansion of IoT and edge computing. The proposed Intrusion Detection System (IDS) combines Linear Discriminant Analysis (LDA) and Logistic Regression (LR) to swiftly and accurately identify intrusions without alerting neighboring devices. The model outperforms existing solutions with an accuracy of 96.56%, precision of 95.78%, and quick training time (0.04 s). It is effective against various types of attacks, enhancing the security of edge networks for IoT applications. •The methodology employs a hybrid model that combines LDA and LR for intrusion detection.•Machine learning techniques are used to analyze and identify intrusive activities during data acquisition by edge nodes.•The methodology includes a mechanism to isolate suspected devices and data without notifying neighboring edge nodes to prevent intruders from gaining control over the edge network. [Abstract copyright: © 2024 The Author(s).
