Tuning the corona-core ratio of polyplex micelles for selective oligonucleotide delivery to hepatocytes or hepatic immune cells

Targeted delivery of oligonucleotides or small molecular drugs to hepatocytes, the liver's parenchymal cells, is challenging without targeting moiety due to the highly efficient mononuclear phagocyte system (MPS) of the liver. The MPS comprises Kupffer cells and specialized sinusoidal endothelial cells, efficiently clearing nanocarriers regardless of their size and surface properties. Physiologically, this non-parenchymal shield protects hepatocytes; however, these local barriers must be overcome for drug delivery. Nanocarrier structural properties strongly influence tissue penetration, in vivo pharmacokinetics, and biodistribution profile. Here we demonstrate the in vivo biodistribution of polyplex micelles formed by polyion complexation of short interfering (si)RNA with modified poly(ethylene glycol)-block-poly(allyl glycidyl ether) (PEG-b-PAGE) diblock copolymer that carries amino moieties in the side chain. The ratio between PEG corona and siRNA complexed PAGE core of polyplex micelles was chemically varied by altering the degree of polymerization of PAGE. Applying Raman-spectroscopy and dynamic in silico modeling on the polyplex micelles, we determined the corona-core ratio (CCR) and visualized the possible micellar structure with varying CCR. The results for this model system reveal that polyplex micelles with higher CCR, i.e., better PEG coverage, exclusively accumulate and thus allow passive cell-type-specific targeting towards hepatocytes, overcoming the macrophage-rich reticuloendothelial barrier of the liver

Vehicle Safety Planning Control Method Based on Variable Gauss Safety Field

The existing intelligent vehicle trajectory-planning methods have limitations in terms of efficiency and safety. To overcome these limitations, this paper proposes an automatic driving trajectory-planning method based on a variable Gaussian safety field. Firstly, the time series bird’s-eye view is used as the input state quantity of the network, which improves the effectiveness of the trajectory planning policy network in extracting the features of the surrounding traffic environment. Then, the policy gradient algorithm is used to generate the planned trajectory of the autonomous vehicle, which improves the planning efficiency. The variable Gaussian safety field is used as the reward function of the trajectory planning part and the evaluation index of the control part, which improves the safety of the reinforcement learning vehicle tracking algorithm. The proposed algorithm is verified using the simulator. The obtained results show that the proposed algorithm has excellent trajectory planning ability in the highway scene and can achieve high safety and high precision tracking control

MGA-seq: robust identification of extrachromosomal DNA and genetic variants using multiple genetic abnormality sequencing

Abstract Genomic abnormalities are strongly associated with cancer and infertility. In this study, we develop a simple and efficient method — multiple genetic abnormality sequencing (MGA-Seq) — to simultaneously detect structural variation, copy number variation, single-nucleotide polymorphism, homogeneously staining regions, and extrachromosomal DNA (ecDNA) from a single tube. MGA-Seq directly sequences proximity-ligated genomic fragments, yielding a dataset with concurrent genome three-dimensional and whole-genome sequencing information, enabling approximate localization of genomic structural variations and facilitating breakpoint identification. Additionally, by utilizing MGA-Seq, we map focal amplification and oncogene coamplification, thus facilitating the exploration of ecDNA’s transcriptional regulatory function