Rapid development of antisense therapies can enable on-demand responses to new viral pathogens and make personalized medicine for genetic diseases practical. Antisense phosphorodiamidate morpholino oligomers (PMOs) are promising candidates to fill such a role, but their challenging synthesis limits their widespread application. To rapidly prototype potential PMO drug candidates, we report a fully automated flow-based oligonucleotide synthesizer. Our optimized synthesis platform reduces coupling times by up to 22-fold compared to previously reported methods. We demonstrate the power of our automated technology with the synthesis of milligram quantities of three candidate therapeutic PMO sequences for an unserved class of Duchenne muscular dystrophy (DMD). To further test our platform, we synthesize a PMO that targets the genomic mRNA of SARS-CoV-2 and demonstrate its antiviral effects. This platform could find broad application not only in designing new SARS-CoV-2 and DMD antisense therapeutics, but also for rapid development of PMO candidates to treat new and emerging diseases.
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http://dx.doi.org/10.1038/s41467-021-24598-4 | DOI Listing |
Lab Chip
January 2025
School of Advanced Technology, Xi'an Jiaotong-Liverpool University, Suzhou, 215000, China.
Alzheimer's disease (AD) is the leading cause of dementia worldwide, and the development of early screening methods can address its significant health and social consequences. In this paper, we present a rotary-valve assisted paper-based immunoassay device (RAPID) for early screening of AD, featuring a highly integrated on-chip rotary micro-valve that enables fully automated and efficient detection of the AD biomarker (amyloid beta 42, Aβ42) in artificial plasma. The microfluidic paper-based analytical device (μPAD) of the RAPID pre-stores the required assay reagents on a μPAD and automatically controls the liquid flow through a single valve.
View Article and Find Full Text PDFBio Protoc
January 2025
Center for Translational Neuromedicine, University of Copenhagen, Copenhagen, Denmark.
Magnetic resonance imaging (MRI) is an invaluable method of choice for anatomical and functional in vivo imaging of the brain. Still, accurate delineation of the brain structures remains a crucial task of MR image evaluation. This study presents a novel analytical algorithm developed in MATLAB for the automatic segmentation of cerebrospinal fluid (CSF) spaces in preclinical non-contrast MR images of the mouse brain.
View Article and Find Full Text PDFFront Med (Lausanne)
December 2024
Software Engineering Department, LUT University, Lahti, Finland.
Introduction: Neurodegenerative diseases, including Parkinson's, Alzheimer's, and epilepsy, pose significant diagnostic and treatment challenges due to their complexity and the gradual degeneration of central nervous system structures. This study introduces a deep learning framework designed to automate neuro-diagnostics, addressing the limitations of current manual interpretation methods, which are often time-consuming and prone to variability.
Methods: We propose a specialized deep convolutional neural network (DCNN) framework aimed at detecting and classifying neurological anomalies in MRI data.
J Endovasc Ther
January 2025
Vascular Unit, Department of Surgery, Mater Dei Hospital, Msida, Malta.
Purpose: The use of surgeon-modified fenestrated endograft to treat a bleeding complication in the common iliac artery.
Technique: An Endurant limb graft was modified on back table in theater after planning the fenestration using a semi-automated centerline. The Endurant stent was planned to land flush at the aortic bifurcation.
ISA Trans
January 2025
State Key Laboratory of Rolling and Automation, Northeastern University, Shenyang, Liaoning 110819, China; Engineering Research Center of Frontier Technologies for Low-carbon Steelmaking, Ministry of Education, Shenyang 110819, China. Electronic address:
Multiple processes connected closely during the endless strip production (ESP) rolling, it is difficult to obtain the global optimal solution by multi-objective modelling of a single process, and the parameters to be optimized coupled with each other. To obtain the optimal solution, a multi-objective optimization model combining the power consumption, product quality, and loading balance was proposed for the design of an ESP rolling schedule. The thickness and heating temperature were simultaneously taken as the decision variables for coupling the temperature and loading in the rolling process, and the non-dominated sorting genetic algorithm-II (NSGA-II) based on differential evolution (NSGA-II-DE) was applied to obtain the Pareto solutions.
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