Ionizable lipid nanoparticles (LNPs) are seeing widespread use in mRNA delivery, notably in SARS-CoV-2 mRNA vaccines. However, the expansion of mRNA therapies beyond COVID-19 is impeded by the absence of LNPs tailored for diverse cell types. In this study, we present the AI-Guided Ionizable Lipid Engineering (AGILE) platform, a synergistic combination of deep learning and combinatorial chemistry.
View Article and Find Full Text PDFPredation exerts a significant selection pressure on prey, shaping a multitude of traits that serve as antipredator defences. In turn, natural selection could favour combinations of antipredator defences with synergistic effects that enhance prey survival. An especially interesting antipredator defence is death feigning (DF), present in a wide variety of taxa and usually characterized by the prey lying motionless often along with defaecation, musking and autohaemorrhaging (AH).
View Article and Find Full Text PDFThe advancement of message RNA (mRNA) -based immunotherapies for cancer is highly dependent on the effective delivery of RNA (Ribonucleic) payloads using ionizable lipid nanoparticles (LNPs). However, the clinical application of these therapies is hindered by variable mRNA expression among different cancer types and the risk of systemic toxicity. The transient expression profile of mRNA further complicates this issue, necessitating frequent dosing and thus increasing the potential for adverse effects.
View Article and Find Full Text PDFIonizable lipid nanoparticles (LNPs) pivotal to the success of COVID-19 mRNA (messenger RNA) vaccines hold substantial promise for expanding the landscape of mRNA-based therapies. Nevertheless, the risk of mRNA delivery to off-target tissues highlights the necessity for LNPs with enhanced tissue selectivity. The intricate nature of biological systems and inadequate knowledge of lipid structure-activity relationships emphasize the significance of high-throughput methods to produce chemically diverse lipid libraries for mRNA delivery screening.
View Article and Find Full Text PDFThe aim of our study is to define the most frequent etiology, clinical presentation, and predictive factors of outcome in children with acute ataxia (AA) and to determine "the red flags" in the diagnostic approach to children with AA. The retrospective study included the patients with AA treated in the institute from 2015 to 2021. The inclusion criteria were children aged 1-18 years, evolution time of ataxia within 72 h, and diagnosis made by a physician.
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