Objective: Phantoms that mimic healthy or diseased organ properties can complement animal models for surgical planning, training, and medical device development. If urodynamic studies rely on pressure-volume curves to assess lower urinary tract symptoms, there is an unsatisfied need for a bladder phantom that accurately mimics the bladder stretching capabilities and compliant behaviour during physiological filling. 
Approach: We demonstrate the suitability of water-soluble 3D-printed moulds as a versatile method to fabricate accurate phantoms with anatomical structures reconstructed from medical images. We report a phantom fabricated with silicone rubber. A wire net limits the silicone expansion to model the cystometric capacity. A mathematical model describes the pressure increase due to passive hyperelastic properties. 
Main results: The phantom reproduces the bladder’s mechanical properties during filling. The pressure-volume curve measured on the phantom is typical of cystometric studies, with a compliance of 25.2 ± 1 mL cmH_2O^(-1). The root-mean-square error between the theoretical model and experimental data is 2.7 cmH_2O. The compliance, bladder wall thickness, cystometric capacity and pressure near the cystometric capacity of the phantom can be tuned to mimic various pathologies or human variability. 
Significance: The manufacturing method is suitable for fabricating bladder and other soft and hollow organ phantoms. The mathematical model provides a method to determine design parameters to model healthy or diseased bladders. Soft hollow organ phantoms can be used to complement animal experimentations for developing and validating medical devices aiming to be anchored on these organs or monitor their activity through pressure and strain measurement. 
This work presents an automated analysis algorithm to detect action potentials (APs) in a nerve and quantify its activity. The algorithm is based on template matching. The templates are automatically adapted to individual AP shapes that vary depending on the nerve fibers from which the AP originates, and the recording setup used. The algorithm was validated by quantifying vagus nerve activity recorded during in vivo experiments in a rat model. The MATLAB version of the code is available in open access on GitHub 1 .
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