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      Exploring the involvement of TASK-1 in the control of isolated rat right atrium function from healthy animals and an experimental model of monocrotaline-induced pulmonary hypertension

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          Epidemiology of Atrial Fibrillation in the 21st Century: Novel Methods and New Insights

          Accompanying the aging of populations worldwide, and increased survival with chronic diseases, the incidence and prevalence of atrial fibrillation (AF) are rising, justifying the term global epidemic. This multifactorial arrhythmia is intertwined with common concomitant cardiovascular diseases, which share classical cardiovascular risk factors. Targeted prevention programs are largely missing. Prevention needs to start at an early age with primordial interventions at the population level. The public health dimension of AF motivates research in modifiable AF risk factors and improved precision in AF prediction and management. In this review, we summarize current knowledge in an attempt to untangle these multifaceted associations from an epidemiological perspective. We discuss disease trends, preventive opportunities offered by underlying risk factors and concomitant disorders, current developments in diagnosis and risk prediction, and prognostic implications of AF and its complications. Finally, we review current technological (eg, eHealth) and methodological (artificial intelligence) advances and their relevance for future prevention and disease management.
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            Atrial remodeling and atrial fibrillation: mechanisms and implications.

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              A novel channelopathy in pulmonary arterial hypertension.

              Pulmonary arterial hypertension is a devastating disease with high mortality. Familial cases of pulmonary arterial hypertension are usually characterized by autosomal dominant transmission with reduced penetrance, and some familial cases have unknown genetic causes. We studied a family in which multiple members had pulmonary arterial hypertension without identifiable mutations in any of the genes known to be associated with the disease, including BMPR2, ALK1, ENG, SMAD9, and CAV1. Three family members were studied with whole-exome sequencing. Additional patients with familial or idiopathic pulmonary arterial hypertension were screened for the mutations in the gene that was identified on whole-exome sequencing. All variants were expressed in COS-7 cells, and channel function was studied by means of patch-clamp analysis. We identified a novel heterozygous missense variant c.608 G→A (G203D) in KCNK3 (the gene encoding potassium channel subfamily K, member 3) as a disease-causing candidate gene in the family. Five additional heterozygous missense variants in KCNK3 were independently identified in 92 unrelated patients with familial pulmonary arterial hypertension and 230 patients with idiopathic pulmonary arterial hypertension. We used in silico bioinformatic tools to predict that all six novel variants would be damaging. Electrophysiological studies of the channel indicated that all these missense mutations resulted in loss of function, and the reduction in the potassium-channel current was remedied by the application of the phospholipase inhibitor ONO-RS-082. Our study identified the association of a novel gene, KCNK3, with familial and idiopathic pulmonary arterial hypertension. Mutations in this gene produced reduced potassium-channel current, which was successfully remedied by pharmacologic manipulation. (Funded by the National Institutes of Health.)
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                Author and article information

                Journal
                Naunyn-Schmiedeberg's Archives of Pharmacology
                Naunyn-Schmiedeberg's Arch Pharmacol
                Springer Science and Business Media LLC
                0028-1298
                1432-1912
                June 20 2023
                Article
                10.1007/s00210-023-02569-4
                b0c6dfba-170c-4dc8-a9a6-2373bf7ed447
                © 2023

                https://www.springernature.com/gp/researchers/text-and-data-mining

                https://www.springernature.com/gp/researchers/text-and-data-mining

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