Experimental Demonstration of Compact Polymer Mass Transfer Device Manufactured by Additive Manufacturing with Hydrogel Integration to Bio-Mimic the Liver Functions.

In this paper, we designed and demonstrated a stimuli-responsive hydrogel that mimics the mass diffusion function of the liver. We have controlled the release mechanism using temperature and pH variations. Additive manufacturing technology was used to fabricate the device with nylon (PA-12), using selective laser sintering (SLS). The device has two compartment sections: the lower section handles the thermal management, and feeds temperature-regulated water into the mass transfer section of the upper compartment. The upper chamber has a two-layered serpentine concentric tube; the inner tube carries the temperature-regulated water to the hydrogel using the given pores. Here, the hydrogel is present in order to facilitate the release of the loaded methylene blue (MB) into the fluid. By adjusting the fluid's pH, flow rate, and temperature, the deswelling properties of the hydrogel were examined. The weight of the hydrogel was maximum at 10 mL/min and decreased by 25.29% to 10.12 g for the flow rate of 50 mL/min. The cumulative MB release at 30 °C increased to 47% for the lower flow rate of 10 mL/min, and the cumulative release at 40 °C climbed to 55%, which is 44.7% more than at 30 °C. The MB release rates considerably increased when the pH dropped from 12 to 8, showing that the lower pH had a major impact on the release of MB from the hydrogel. Only 19% of the MB was released at pH 12 after 50 min, and after that, the release rate remained nearly constant. At higher fluid temperatures, the hydrogels lost approximately 80% of their water in just 20 min, compared to a loss of 50% of their water at room temperature. The outcomes of this study may contribute to further developments in artificial organ design.

Estimation of tumor parameters using neural networks for inverse bioheat problem.

BACKGROUND AND OBJECTIVE: Some types of cancer cause rapid cell growth, while others cause cells to grow and divide at a slower rate. Certain forms of cancer result in visible growths called tumors. This work proposes an inverse estimation of the size and location of the tumor using a feedforward Neural Network (FFNN) model. METHODS: The forward model is a 3D model of the breast induced with a tumor of various sizes at different locations within the breast, and it is solved using the Pennes equation. The data obtained from the simulation of the bioheat transfer is used for training the neural network. In order to optimize the neural network architecture, the work proposes varying the number of neurons in the hidden layer and thus finding the best fit to create a relationship between the temperature profile and tumor parameters which can be used to estimate the tumor parameters given the temperature profile. RESULTS: These simulations resulted in a temperature distribution profile that could thus be used to locate and determine the parameters of the cancerous tumor within the breast. The prediction accuracy showed the capacity of the trained Feed Forward Neural Network to estimate the unknown parameters within an acceptable range of error. The model validations use the Root Mean Square Error method to quantify and minimize the prediction error. CONCLUSIONS: In this work, a non-intrusive method for the diagnosis of breast cancer was modelled, which yields conclusive results for the estimation of the tumor parameters.