This study presents an innovative approach utilizing artificial intelligence (AI) for the prediction and classification of water quality parameters based on physico-chemical measurements. The primary objective was to enhance the accuracy, speed, and accessibility of water quality monitoring. Data collected from various water samples in Algeria were analyzed to determine key parameters such as conductivity, turbidity, pH, and total dissolved solids (TDS). These measurements were integrated into deep neural networks (DNNs) to predict indices such as the sodium adsorption ratio (SAR), magnesium hazard (MH), sodium percentage (SP), Kelley’s ratio (KR), potential salinity (PS), exchangeable sodium percentage (ESP), as well as Water Quality Index (WQI) and Irrigation Water Quality Index (IWQI). The DNNs model, optimized through the selection of various activation functions and hidden layers, demonstrated high precision, with a correlation coefficient (R) of 0.9994 and a low root mean square error (RMSE) of 0.0020. This AI-driven methodology significantly reduces the reliance on traditional laboratory analyses, offering real-time water quality assessments that are adaptable to local conditions and environmentally sustainable. This approach provides a practical solution for water resource managers, particularly in resource-limited regions, to efficiently monitor water quality and make informed decisions for public health and agricultural applications.

Background: Hepatocellular carcinoma (HCC) is a prevalent and lethal form of liver cancer with limited treatment options. Silymarin, a flavonoid complex derived from milk thistle, has shown promise in liver disease treatment due to its antioxidant, anti-inflammatory, and anticancer properties. This study aims to explore the therapeutic potential of silymarin in HCC through a comprehensive in silico approach. Methods: This study employed a network pharmacology approach to identify key molecular targets of silymarin in HCC. The Genecards and Metascape databases were used for target identification and functional annotation. Molecular docking analysis was conducted on the primary silymarin components against VEGFA and SRC proteins, which are critical in HCC progression. MD simulations followed to assess the stability and interactions of the docked complexes. Results: Network pharmacology analysis identified several key molecular targets and pathways implicated in HCC. The molecular docking results revealed strong binding affinities of silymarin components to VEGFA and SRC, with Silybin A and Isosilybin B showing the highest affinities. MD simulations confirmed the stability of these interactions, indicating potential inhibitory effects on HCC progression. Conclusions: This study provides a comprehensive in silico evaluation of silymarin’s therapeutic potential in HCC. The findings suggest that silymarin, particularly its components Silybin A and Isosilybin B, may effectively target VEGFA and SRC proteins, offering a promising avenue for HCC treatment. Further experimental validation is warranted to confirm these findings and facilitate the development of silymarin-based therapeutics for HCC.

The green synthesis of silver nanoparticles (AgNPs) using plant extracts is an eco-friendly method with potential for biomedical and environmental applications. This study aims to synthesize silver nanoparticles (SO-AgNPs) using Salvia officinalis L. extract and evaluate their antioxidant and antibacterial properties, positioning them as candidates for applications in sustainable biomedicine and wastewater treatment. S. officinalis L. extract was used to synthesize AgNPs under optimized conditions, with a 10% extract/AgNO₃ ratio and a reaction time of 180 min. The SO-AgNPs were characterized using ATR-FTIR, XRD, SEM, DLS, and Zeta potential analysis. The antioxidant activity of the extract and SO-AgNPs was evaluated using ABTS^+• and DPPH^• radical scavenging assays. Antibacterial activity was tested against 11 bacterial strains and bacteria isolated from industrial effluent, with minimal inhibitory concentrations (MIC) determined for both the extract and SO-AgNPs. The SO-AgNPs demonstrated potent antioxidant activity, with IC₅₀ values of 0.233 mg/mL and 0.305 mg/mL in the ABTS^+• assay, and 0.173 mg/mL and 0.185 mg/mL in the DPPH^• assay for the extract and SO-AgNPs, respectively. Antibacterial testing showed MIC values of 0.25 mg/mL for SO-AgNPs and between 3.12 and 6.25 mg/mL for S. officinalis L. extract against E. coli, P. aeruginosa, A. baumannii, MRSA, B. cereus, and S. epidermidis. For bacteria isolated from industrial effluent, the MIC values were 0.125 mg/mL for SO-AgNPs and 0.5 mg/mL for the extract. This study highlights the dual antioxidant and antibacterial capabilities of S. officinalis L. extract and SO-AgNPs, demonstrating their potential for use in both biomedical and environmental applications, including wastewater treatment.