Haak, M. C. | bioRxiv Science

bioRxiv2025

AAV6-HCN4t-mediated biological pacing as a potential life-saving therapy for congenital complete heart block

Congenital complete heart block (CCHB) is a life-threatening condition in fetuses due to severe bradycardia. Maternal administration of {beta}-adrenergic agonists is used to increase fetal heart rates, but its effectiveness is limited and lost over time most likely due to insufficient expression of HCN channels in some individuals. We report the development of an injectable gene therapy that produces reliable cardiac pacemaker function in the presence of {beta}-adrenergic stimulation. Intramyocardial injection of adeno-associated viral serotype 6 vectors expressing HCN4t (AAV6-HCN4t) into the left ventricular apex significantly increased ectopic pacing frequency and heart rate in response to isoproterenol in rats with complete heart block, and this effect remained stable throughout the 4 weeks of follow-up. Injection of AAV6-HCN4t showed similar reliable biological pacing in complete heart block pigs. These results suggest that AAV6-HCN4t generates robust biological pacing in the presence of isoproterenol, providing the foundation for a potentially life-saving therapy for in utero CCHB.

physiology↗

bioRxiv2021

Subcortical Segmentation of the Fetal Brain in 3D Ultrasound using Deep Learning

The quantification of subcortical volume development from 3D fetal ultrasound can provide important diagnostic information during pregnancy monitoring. However, manual segmentation of subcortical structures in ultra-sound volumes is time-consuming and challenging due to low soft tissue contrast, speckle and shadowing artifacts. For this reason, we developed a convolutional neural network (CNN) for the automated segmentation of the choroid plexus (CP), lateral posterior ventricle horns (LPVH), cavum septum pellucidum et vergae (CSPV), and cerebellum (CB) from 3D ultrasound. As ground-truth labels are scarce and expensive to obtain, we applied few-shot learning, in which only a small number of manual annotations (n = 9) are used to train a CNN. We compared training a CNN with only a few individually annotated volumes versus many weakly labelled volumes obtained from atlas-based segmentations. This showed that segmentation performance close to intra-observer variability can be obtained with only a handful of manual annotations. Finally, the trained models were applied to a large number (n = 278) of ultrasound image volumes of a diverse, healthy population, obtaining novel US-specific growth curves of the respective structures during the second trimester of gestation.

neuroscience↗