Search Results (3)

The green synthesis of nanoparticles (NPs) is attracting enormous attention as a new area of study that encompasses the development and discovery of new agents for their utilization in different fields, such as pharmaceuticals and food. Nowadays, the use of plants, particularly medicinal plants, for the creation of NPs has emerged as a safe, ecofriendly, rapid, and simple approach. Therefore, the present study aimed to use the Saudi mint plant as a medicinal plant for the synthesis of silver nanoparticles (AgNPs) and to evaluate the antimicrobial and antioxidant activities of AgNPs compared to mint extract (ME). A phenolic and flavonoid analysis that was conducted by using HPLC indicated the presence of numerous compounds in the ME. Through an HPLC analysis, chlorogenic acid at a concentration of 7144.66 µg/mL was the main detected component in the ME, while catechin, gallic acid, naringenin, ellagic acid, rutin, daidzein, cinnamic acid, and hesperetin were identified in varying concentrations. AgNPs were synthesized by using ME and were confirmed via UV–visible spectroscopy at 412 nm of the maximum absorption. The mean diameter of the synthesized AgNPs was measured by TEM to be 17.77 nm. Spectra obtained by using energy-dispersive X-ray spectroscopy indicated that silver was the main element formation in the created AgNPs. The presence of various functional groups, analyzed by using Fourier transform infrared spectroscopy (FTIR), indicated that the mint extract was responsible for reducing Ag⁺ to Ag⁰. The spherical structure of the synthesized AgNPs was confirmed by X-ray diffraction (XRD). Furthermore, the ME showed reduced antimicrobial activity (a zone of inhibition of 30, 24, 27, 29, and 22 mm) compared with the synthesized AgNPs (a zone of inhibition of 33, 25, 30, 32, 32, and 27 mm) against B. subtilis, E. faecalis, E. coli, P. vulgaris, and C. albicans, respectively. The minimum inhibitory concentration of the AgNPs was lower than that of the ME for all of the tested micro-organisms, except for P. vulgaris. The MBC/MIC index suggested that the AgNPs revealed a higher bactericidal effect compared to the ME. The synthesized AgNPs exhibited antioxidant activity with a reduced IC₅₀ (IC₅₀ of 8.73 µg/mL) compared to that of the ME (IC₅₀ of 13.42 µg/mL). These findings demonstrate that ME could be applied as a mediator for AgNPs synthesis and natural antimicrobial and antioxidant agents. Full article

The food chain, through vegetable consumption, is considered to be an important route of heavy metal exposure. Therefore, in this study, heavy metal concentrations in leafy vegetables grown in the Jazan region of Saudi Arabia were assessed using an ICP-MS. Lettuce, radish, mint, parsley and jarjir (Arugula) were selected for study and subjected to digestion using HCl. The results indicated that the Fe level was highest in all vegetables, while jarjir was the most contaminated vegetable. However, no tested metal exceeded the maximum permissible limits set by the FAO/WHO and European Committee. The possible health hazards associated with the exposure to metal contaminants via vegetable consumption were evaluated by estimating target hazard quotient (THQ) values, and the results revealed that the vegetables grown in close proximity of Jazan city were the most contaminated and those in Darb the least. However, the daily intakes of all the tested metals were well below the corresponding oral reference doses (RfDs), and the THQ values were less than unity, suggesting that the vegetables grown in the studied region were safe and the heavy metal exposure via vegetable consumption was unlikely to cause adverse effects to the local inhabitants of the region. Full article

The housing market is a crucial economic indicator to which the government must pay special attention because of its impact on the lives of freshly minted city inhabitants. As a guide for government regulation, individual property purchases, third-party evaluation, and understanding how housing prices are distributed geographically may be of great practical use. Therefore, much research has been conducted on how to arrive at a more accurate and efficient way of calculating housing prices in the current market. The goal of this study was to use the artificial neural network (ANN) technique to correctly identify real estate prices. The novelty of the proposed research is to build a prediction model based on ANN for predicting future house prices in Saudi Arabia. The dataset was collected from Aqar in four main Saudi Arabian cities: Riyadh, Jeddah, Dammam, and Al-Khobar. The results showed that the experimental and predicted values were very close. The results of the proposed system were compared with different existing prediction systems, and the developed model achieved high performance. This forecasting system can also help increase investment in the real estate sector. The ANN model could appropriately estimate the housing prices currently available on the market, according to the findings of the assessments of the model. Thus, this study provides a suitable decision support or adaptive suggestion approach for estimating the ideal sales prices of residential properties. This solution is urgently required by both investors and the general population as a whole. Full article