Vision Changes After Head and Neck Cancer Treatment.

Background: Literature about ocular toxicity after head and neck cancer (HNC) treatment is scarce. This study aimed to describe visual function and ocular symptoms among HNC patients.

Methods: The study used a cross-sectional design.

Nucleus pulposus cell-mimicking nanoparticles for cell-specific HIF1A editing to modulate SASP-mediated disc inflammation via autophagy activation.

Traditional methods of plasmid delivery, including viral vectors, lipofection, and electroporation, are widely used for gene editing but have limitations, such as cellular toxicity, limited transfection efficiency in primary cells, and nonspecific side effects. Here, we report the development of nucleus pulposus cell (NPC)-mimicking nanoparticles (HIF1A@NNP) with an NPC membrane as the shell and pcDNA3.1-rHIF1A encapsulated in the core via extrusion.

Multiomics analysis revealed the effects of polystyrene nanoplastics at different environmentally relevant concentrations on intestinal homeostasis.

Nanoplastics pollution is a global issue, with the digestive tract being one of the first affected organs, requiring further research on its impact on intestinal health. This study involved orally exposing mice to polystyrene nanoplastics (PS-NPs) at doses of 0.1, 0.

Microencapsulation of cornmint oil with octenyl succinate anhydride debranched starch by electrostatic spray drying: Characterization and release kinetics of the aroma components.

In this study, a novel method for the preparation of corn mint oil microcapsules was developed using electrostatic spray drying (ESD) with three octenyl succinate anhydride debranched starch (OSADS) wall materials. It was complexed with gum arabic (GA) and maltodextrin (MD). This study addresses the research gap that traditional methods have low efficiency in encapsulating volatile compounds (such as essential oils).

DTI-BGCGCN: A novel bipartite graph-based cluster graph convolutional network for drug-target interaction prediction in modern and traditional chinese medicine.

Accurate prediction of Drug-Target Interactions (DTIs) is crucial for drug discovery and development. While current research focuses predominantly on modern medicine, we propose DTI-BGCGCN, a novel predictive model that integrates a bipartite drug-target attribute graph with a Cluster Graph Con- volutional Network (ClusterGCN) for both modern and traditional Chinese medicine. Our approach employs a bipartite attribute graph to efficiently en- capsulate drug-target relationships and common features, while ClusterGCN classifies different graph topological structures and expedites the training process.