Publications by authors named "A A Gusev"

N-Acetylgalactosamine (GalNAc) is an efficient and multifunctional delivery tool in the development and synthesis of chemically modified oligonucleotide therapeutics (conjugates). Such therapeutics demonstrate improved potency in vivo due to the selective and efficient delivery to hepatocytes in the liver via receptor-mediated endocytosis, which is what drives the high interest in this molecule. The ways to synthesize such structures are relatively new and have not been optimized in terms of the yields and stages both in lab and large-scale synthesis.

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Pediatric solid tumors are a leading cause of childhood disease mortality. In this work, we examined germline structural variants (SVs) as risk factors for pediatric extracranial solid tumors using germline genome sequencing of 1765 affected children, their 943 unaffected parents, and 6665 adult controls. We discovered a sex-biased association between very large (>1 megabase) germline chromosomal abnormalities and increased risk of solid tumors in male children.

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Article Synopsis
  • RNA sequencing can uncover various types of transcriptional regulation beyond just gene expression, but current studies often struggle with the complexity of analyzing multiple RNA characteristics.
  • Pantry is a new framework that efficiently generates diverse RNA phenotypes from sequencing data and integrates these phenotypes with genetic data using QTL mapping and other analyses.
  • By applying Pantry to existing datasets, researchers found a significant increase in gene associations, highlighting the importance of analyzing multiple RNA modalities for discovering unique gene-trait relationships and understanding the mechanisms behind genetic regulation.
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Early identification of high-risk individuals through the analysis of their unique disease trajectories has a strong potential to support efficient prevention and clinical management across a range of chronic conditions. In this paper we present a novel approach for dynamic modeling of the evolution of chronic disease risks over time, incorporating individual genetic predispositions. Our approach uses a hierarchical Bayesian topic model including Gaussian Processes to capture age effects.

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A molecular Kuhn-scale model is presented for the stress relaxation dynamics of entangled polymer networks. The governing equation of the model is given by the general form of the linearized Langevin equation. Based on the fluctuation-dissipation theorem, the stress relaxation modulus is derived using the normal mode representation.

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