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Abstract The uterus exhibits intermittent electrophysiological activity in vivo. Although most active during labor, the non-pregnant uterus can exhibit activity of comparable magnitude to the early stages of labor. In this study, two types of flexible electrodes were utilized to measure the electrical activity of uterine smooth muscle in vivo in anesthetized, non-pregnant rats. Flexible printed circuit electrodes were placed on the serosal surface of the uterine horn of six anesthetized rats. Electrical activity was recorded for a duration of 20–30 min. Activity contained two components: high frequency activity (bursts) and an underlying low frequency ‘slow wave’ which occurred concurrently. These components had dominant frequencies of 6.82 ± 0.63 Hz for the burst frequency and 0.032 ± 0.0055 Hz for the slow wave frequency. There was a mean burst occurrence rate of 0.76 ± 0.23 bursts per minute and mean burst duration of 20.1 ± 6.5 s. The use of multiple high-resolution electrodes enabled 2D mapping of the initiation and propagation of activity along the uterine horn. This in vivo approach has the potential to provide the organ level detail to help interpret non-invasive body surface recordings.

In-silico studies are an ideal medium to model and improve our understanding of the mechanisms underlying gastric motility in health and disease. In this study, a workflow to create computational models of the stomach was developed using SPARC scaffolds. Three anatomically based finite element method (FEM) models of the rat stomach incorporating experimental measurements of muscle layer thickness and fiber orientations across the stomach were developed: (i) 2D (surface) FEM model with no thickness, (ii) 3D (volume) FEM model with a fixed thickness across the longitudinal and circular muscle layers, and (iii) 3D (volume) FEM model with varying thickness across the longitudinal and circular muscle layers. The three FEM models were subsequently used in whole-organ slow wave simulations and the impact of anatomical details on the simulation outcomes was investigated. The 3D FEM model with varying thickness was the most computationally expensive, while the 2D FEM model provided the fastest solution (a 200 s simulation took 8 min vs. 38 h to solve). The spatiotemporal profiles of the slow wave activation and propagation in the three FEM models were in good agreement. The largest temporal difference of 1 s in cellular activation was observed between the 2D FEM model and the varying thickness 3D FEM model in the most distal-stomach regions. These FEM models and developed workflow will be used in in-silico studies to improve our understanding of the structure-function relationship in the stomach and identify the optimal parameters of electrical therapies, an alternative treatment for the motility disorders in the stomach. In addition, the developed workflow can be readily used to generate computational models of other organs using SPARC scaffolds.

The stomach is extensively innervated by the vagus nerve and the enteric nervous system. The mechanisms through which this innervation affects gastric motility are being unraveled, motivating the first concerted steps towards the incorporation autonomic regulation into computational models of gastric motility. Computational modeling has been valuable in advancing clinical treatment of other organs, such as the heart. However, to date, computational models of gastric motility have made simplifying assumptions about the link between gastric electrophysiology and motility. Advances in experimental neuroscience mean that these assumptions can be reviewed, and detailed models of autonomic regulation can be incorporated into computational models. This review covers these advances, as well as a vision for the utility of computational models of gastric motility. Diseases of the nervous system, such as Parkinson’s disease, can originate from the brain-gut axis and result in pathological gastric motility. Computational models are a valuable tool for understanding the mechanisms of disease and how treatment may affect gastric motility. This review also covers recent advances in experimental neuroscience that are fundamental to the development of physiology-driven computational models. A vision for the future of computational modeling of gastric motility is proposed and modeling approaches employed for existing mathematical models of autonomic regulation of other gastrointestinal organs and other organ systems are discussed.