Nanocarriers loaded with anticancer agents strategically intervene to block cell signaling pathways in glioblastoma.

Hepatocellular carcinoma (HCC) is a major global health issue, ranking as the sixth most common cancer and a leading cause of cancer-related deaths worldwide. Risk factors for HCC include chronic hepatitis B and C, obesity, alcohol abuse, diabetes, and metabolic disorders. Current treatments, such as surgery, transplantation, and chemotherapy, are often ineffective in advanced stages due to tumor resistance and the inability to target key oncogenic pathways. Recent advances in small interfering RNA (siRNA) therapy offer a promising solution to silence these pathways and hinder tumor progression. Nanoparticles, especially lipid-based nanoparticles (LNPs) like liposomes, solid lipid nanoparticles, exosomes etc. have emerged as an effective platform for siRNA delivery. LNPs provide critical advantages, including protection of siRNA from enzymatic degradation, improved cellular uptake, and precise tumor targeting through functionalization strategies. Compared to polymeric and metallic nanocarriers, LNPs demonstrate superior biocompatibility, biodegradability, and safety profiles. Furthermore, their ability to exploit natural mechanisms, such as apolipoprotein E (ApoE)-mediated uptake via low-density lipoprotein receptors on hepatocytes, enhances liver-specific delivery. This review explores advancements in siRNA therapeutics for HCC, highlighting nanoparticle-based delivery, cell signaling targets, and synthesis strategies. It also examines AI’s role in optimizing siRNA design, formulation, and personalized treatment. These innovations enhance pathway-specific therapies, advancing clinical translation and improving HCC outcomes.

The quality and effectiveness of composite-based components are influenced by their mechanical characteristics. Hence, a structured approach is essential to identify the key factors that contribute to achieving superior mechanical characteristics. This study investigates the utilization of an artificial neural network (ANN) to predict the mechanical characteristics, explicitly density, and hardness, of carboxyl copper oxide nanoparticle (CCONP)/DL-lactide and glycolide copolymer (PDLG) nanocomposites fabricated through microwave-assisted sintering. The research explores the effects of numerous microwave sintering conditions on the microstructure and mechanical properties of the nanocomposites. Physical characterization techniques including FTIR and TEM analysis are employed to confirm physical interactions and nanocomposite size. A back-propagation neural network with a 2–10-2 architecture was developed to predict density and hardness. The network was trained using the Levenberg-Marquardt algorithm and the Scaled Conjugate Gradient method. The predicted values are compared with experimental results, showing good agreement with correlation coefficients (R) of 0.883 for density and 0.9737 for hardness. This demonstrates the effectiveness of the ANN approach in evaluating the mechanical properties of CCONP/PDLG nanocomposites, providing a reliable tool for decision-making and potentially reducing experimental characterization costs.