Doubling down on siRNAs in the brain

Using nanotechnology to deliver biomolecules from nose to brain - peptides, proteins, monoclonal antibodies and RNA

There is a growing number of biomolecules, including peptides, proteins, monoclonal antibodies and RNA, that could be potentially used for the treatment of central nervous system (CNS) diseases. However, the realization of their potential is being hampered by the extraordinary difficulties these complex biomolecules have to reach the brain in therapeutically meaningful amounts. Nose-to-brain (N-to-B) delivery is now being investigated as a potential option for the direct transport of biomolecules from the nasal cavity to different brain areas. Here, we discuss how different technological approaches enhance this N-to-B transport, with emphasis on those that have shown a potential for clinical translation. We also analyse how the physicochemical properties of nanocarriers and their modification with cell-penetrating peptides (CPPs) and targeting ligands affect their efficacy as N-to-B carriers for biomolecules.

Hyperspectral Image Classification Using Deep Genome Graph-Based Approach

Recently developed hybrid models that stack 3D with 2D CNN in their structure have enjoyed high popularity due to their appealing performance in hyperspectral image classification tasks. On the other hand, biological genome graphs have demonstrated their effectiveness in enhancing the scalability and accuracy of genomic analysis. We propose an innovative deep genome graph-based network (GGBN) for hyperspectral image classification to tap the potential of hybrid models and genome graphs. The GGBN model utilizes 3D-CNN at the bottom layers and 2D-CNNs at the top layers to process spectral–spatial features vital to enhancing the scalability and accuracy of hyperspectral image classification. To verify the effectiveness of the GGBN model, we conducted classification experiments on Indian Pines (IP), University of Pavia (UP), and Salinas Scene (SA) datasets. Using only 5% of the labeled data for training over the SA, IP, and UP datasets, the classification accuracy of GGBN is 99.97%, 96.85%, and 99.74%, respectively, which is better than the compared state-of-the-art methods.

Advances in the Design of (Nano)Formulations for Delivery of Antisense Oligonucleotides and Small Interfering RNA: Focus on the Central Nervous System

RNA-based therapeutics have emerged as one of the most powerful therapeutic options used for the modulation of gene/protein expression and gene editing with the potential to treat neurodegenerative diseases. However, the delivery of nucleic acids to the central nervous system (CNS), in particular by the systemic route, remains a major hurdle. This review will focus on the strategies for systemic delivery of therapeutic nucleic acids designed to overcome these barriers. Pathways and mechanisms of transport across the blood–brain barrier which could be exploited for delivery are described, focusing in particular on smaller nucleic acids including antisense oligonucleotides (ASOs) and small interfering RNA (siRNA). Approaches used to enhance delivery including chemical modifications, nanocarrier systems, and target selection (cell-specific delivery) are critically analyzed. Learnings achieved from a comparison of the successes and failures reported for CNS delivery of ASOs versus siRNA will help identify opportunities for a wider range of nucleic acids and accelerate the clinical translation of these innovative therapies.