Boyle, A. | Search bioRxiv

bioRxiv2024

Carbonyl Reductase 1: a novel regulator of blood pressure in Down Syndrome

BackgroundApproximately one in every 800 children is born with the severe aneuploid condition of Down Syndrome (DS), a trisomy of chromosome 21. Low blood pressure (hypotension) is a common condition associated with DS and can have a significant impact on exercise tolerance and quality of life. Little is known about the factors driving this hypotensive phenotype and therefore therapeutic interventions are limited. Carbonyl reductase 1 (CBR1) is an enzyme contributing to the metabolism of prostaglandins, glucocorticoids, reactive oxygen species and neurotransmitters, encoded by a gene (CBR1) positioned on chromosome 21 with potential to impact blood pressure. MethodsUtilising genetically modified mice and telemetric blood pressure measurement, we tested the hypothesis that CBR1 influences blood pressure and that its overexpression contributes to hypotension in DS. ResultsIn a mouse model of DS (Ts65Dn), which exhibit hypotension, CBR1 activity was increased and pharmacological inhibition of CBR1 increased blood pressure. Mice heterozygous null for Cbr1 had reduced CBR1 enzyme activity and elevated blood pressure. Further experiments indicate that the underlying mechanisms include alterations in sympathetic tone and prostaglandin metabolism. ConclusionsWe conclude that CBR1 activity contributes to blood pressure homeostasis and inhibition of CBR1 may present a novel therapeutic opportunity to correct symptomatic hypotension in DS.

physiology↗

bioRxiv2021

Development and validation of a deep learning algorithm and open-source platform for the automatic labelling of motion capture markers

The purpose of this work was to develop an open-source deep learning-based algorithm for motion capture marker labelling that can be trained on measured or simulated marker trajectories. In the proposed algorithm, a deep neural network including recurrent layers is trained on measured or simulated marker trajectories. Labels are assigned to markers using the Hungarian algorithm and a predefined generic marker set is used to identify and correct mislabeled markers. The algorithm was first trained and tested on measured motion capture data. Then, the algorithm was trained on simulated trajectories and tested on data that included movements not contained in the simulated data set. The ability to improve accuracy using transfer learning to update the neural network weights based on labelled motion capture data was assessed. The effect of occluded and extraneous markers on labelling accuracy was also examined. Labelling accuracy was 99.6% when trained on measured data and 92.8% when trained on simulated trajectories, but could be improved to up to 98.8% through transfer learning. Missing or extraneous markers reduced labelling accuracy, but results were comparable to commercial software. The proposed labelling algorithm can be used to accurately label motion capture data in the presence of missing and extraneous markers and accuracy can be improved as data are collected, labelled, and added to the training set. The algorithm and user interface can reduce the time and manual effort required to label optical motion capture data, particularly for those with limited access to commercial software.

bioengineering↗