Risk of Adverse Pregnancy Outcomes Associated With Antiseizure Medications and Their Indications: A Systematic Review and Meta-Analysis.

Background And Objectives: Aside from congenital malformations and impaired postnatal neurodevelopment, risks associated with antiseizure medication (ASM) use during pregnancy have been sparsely investigated, particularly outside of epilepsy. We aimed to assess these risks through a systematic literature review and meta-analysis, including ASM exposure for indication.

Methods: We searched MEDLINE, EMBASE, and Cochrane for studies including pregnant women on ASMs for any indication and untreated pregnant women, investigating obstetric complications and fetal/neonatal complications other than congenital malformations and impaired neurodevelopment.

Gene-editing in patient and humanized-mice primary muscle stem cells rescues dysferlin expression in dysferlin-deficient muscular dystrophy.

Dystrophy-associated fer-1-like protein (dysferlin) conducts plasma membrane repair. Mutations in the DYSF gene cause a panoply of genetic muscular dystrophies. We targeted a frequent loss-of-function, DYSF exon 44, founder frameshift mutation with mRNA-mediated delivery of SpCas9 in combination with a mutation-specific sgRNA to primary muscle stem cells from two homozygous patients.

Hyperphenylalaninemia and serotonin deficiency in Dnajc12-deficient mice.

Serotonin exerts numerous neurological and physiological actions in the brain and in the periphery. It is generated by two different tryptophan hydroxylase enzymes, TPH1 and TPH2, in the periphery and in the brain, respectively, which are members of the aromatic amino acid hydroxylase (AAAH) family together with phenylalanine hydroxylase (PAH), degrading phenylalanine, and tyrosine hydroxylase (TH), generating dopamine. In this study, we show that the co-chaperone DNAJC12 is downregulated in serotonergic neurons in the brain of mice lacking TPH2 and thereby central serotonin.

Gaussian level-set percolation on complex networks.

We present a solution of the problem of level-set percolation for multivariate Gaussians defined in terms of weighted graph Laplacians on complex networks. It is achieved using a cavity or message passing approach, which allows one to obtain the essential ingredient required for the solution, viz. a self-consistent determination of locally varying percolation probabilities.