ABSTRACT
Hemophilia B (HB) is an X-linked disorder caused by defects of F9 encoded coagulation factor IX, which is an ideal model for gene therapy. Most existing HB gene therapies are based on viral mediated gene supplementation, which could increase immunoreaction. In this study, CRISPR/Cas9 system was used for gene correction in an F9 mutant HB mouse model in both adult mice (in vivo) and in germline cells (ex vivo). In vivo, naked Cas9-sgRNA plasmid and donor DNA were delivered to HB mice livers to recover the mutation via hydrodynamic tail vein (HTV) injection. 62.5% of the HTV-treated mice showed a detectable gene correction (>1%) in the F9 alleles of hepatocytes, which was sufficient to remit the coagulation deficiency. Ex vivo, three different forms of Cas9 were microinjected into germline cells of HB mice to investigate their efficiency and safety in gene correction. Cas9 protein showed higher gene recovery rates, less embryo toxicity, and lower mosaic repair percentage, making it more suitable for germline gene therapy. Our study strongly supports that CRISPR/Cas9-mediated genome editing is feasible in gene therapy of genetic disorders.
Subject(s)
CRISPR-Cas Systems/genetics , Gene Editing , Hemophilia B/genetics , Alleles , Amino Acid Sequence , Animals , Base Sequence , Disease Models, Animal , Factor IX/genetics , Factor IX/metabolism , Female , Genetic Loci , Genetic Therapy , Germ Cells , Male , Mice , Mice, Inbred C57BL , Mutation , Plasmids , Sequence AlignmentABSTRACT
This paper investigates the stochastic stability of fuzzy Markovian jumping neural networks with mixed delays in mean square. The mixed delays include time-varying delay and continuously distributed delay. By using the Lyapunov functional method, Jensen integral inequality, the generalized Jensen integral inequality, linear convex combination technique and the free-weight matrix method, several novel sufficient conditions are derived to ensure the global asymptotic stability of the equilibrium point of the considered networks in mean square. The proposed results, which do not require the differentiability of the activation functions, can be easily checked via Matlab software. Finally, two numerical examples are given to demonstrate the effectiveness and less conservativeness of our theoretical results over existing literature.
Subject(s)
Fuzzy Logic , Markov Chains , Models, Statistical , Neural Networks, Computer , Signal Processing, Computer-Assisted , Stochastic Processes , Computer Simulation , Time FactorsABSTRACT
The globally exponential stabilization problem is investigated for a general class of stochastic Cohen-Grossberg neural networks with both Markovian jumping parameters and mixed mode-dependent time-delays. The mixed time-delays consist of both discrete and distributed delays. This paper aims to design a memoryless state feedback controller such that the closed-loop system is stochastically exponentially stable in the mean square sense. By introducing a new Lyapunov-Krasovskii functional that accounts for the mode-dependent mixed delays, stochastic analysis is conducted in order to derive delay-dependent criteria for the exponential stabilization problem. Three numerical examples are carried out to demonstrate the feasibility of our delay-dependent stabilization criteria.
Subject(s)
Feedback , Markov Chains , Neural Networks, Computer , Stochastic Processes , Algorithms , Computer Simulation , Humans , Time FactorsABSTRACT
The global exponential stability is analyzed for a class of high-order Hopfield-type neural networks with time-varying delays. Based on the Lyapunov stability theory, together with the linear matrix inequality approach and free-weighting matrix method, some less conservative delay-independent and delay-dependent sufficient conditions are presented for the global exponential stability of the equilibrium point of the considered neural networks. Two numerical examples are provided to demonstrate the effectiveness of the proposed stability criteria.
Subject(s)
Algorithms , Decision Support Techniques , Models, Theoretical , Neural Networks, Computer , Pattern Recognition, Automated/methods , Computer SimulationABSTRACT
An augmented Lyapunov-Krasovskii functional (LKF) approach is presented to derive sufficient conditions for the existence, uniqueness, and globally exponential stability of the equilibrium point of a class of cellular neural networks with time-varying delays. By dividing the variation interval of the time delay into several subintervals with equal length, a novel vector LKF is introduced and new conditions are obtained based on the homeomorphism mapping principle, free-weighting matrix method, and linear matrix inequality techniques. Since the criteria are involving derivative information of both state and delay, the obtained results are less conservative than some previous ones. Two examples are also given to show the effectiveness of the presented criteria.
