Fabrication of flexible thin polyurethane membrane for tissue engineering applications.

Thin and flexible polymeric membranes play a critical role in tissue engineering applications for example organs-on-a-chip. These flexible membranes can enable mechanical stretch of the engineered tissue to mimic organ-specific biophysical features, such as breathing. In this work, we report the fabrication of thin (<20 µm), stretchable, and biocompatible polyurethane (PU) membranes. The membranes were fabricated using spin coating technique on silicon substrates and were mounted on a frame for ease of device integration and handling. The membranes were characterized for their optical and elastic properties and compatibility with cell/tissue culture. It was possible to apply up to 10 kilopascal (kPa) pressure to perform cyclic stretch on 4 mm-diameter membranes for a period of 2 weeks at 0.2 hertz (Hz) frequency without mechanical failure. Adenocarcinomic human alveolar basal epithelial (A549) cells were cultured on the apical side of the PU membrane. The morphology and viability of the cells were comparable to those of cells cultured on standard tissue culture plates. Our experiments suggest that the stretchable PU membrane will be broadly useful for various tissue engineering applications in vitro.

A Deep Learning-Based Automatic Segmentation and 3D Visualization Technique for Intracranial Hemorrhage Detection Using Computed Tomography Images.

Intracranial hemorrhage (ICH) occurs when blood leaks inside the skull as a result of trauma to the skull or due to medical conditions. ICH usually requires immediate medical and surgical attention because the disease has a high mortality rate, long-term disability potential, and other potentially life-threatening complications. There are a wide range of severity levels, sizes, and morphologies of ICHs, making accurate identification challenging. Hemorrhages that are small are more likely to be missed, particularly in healthcare systems that experience high turnover when it comes to computed tomography (CT) investigations. Although many neuroimaging modalities have been developed, CT remains the standard for diagnosing trauma and hemorrhage (including non-traumatic ones). A CT scan-based diagnosis can provide time-critical, urgent ICH surgery that could save lives because CT scan-based diagnoses can be obtained rapidly. The purpose of this study is to develop a machine-learning algorithm that can detect intracranial hemorrhage based on plain CT images taken from 75 patients. CT images were preprocessed using brain windowing, skull-stripping, and image inversion techniques. Hemorrhage segmentation was performed using multiple pre-trained models on preprocessed CT images. A U-Net model with DenseNet201 pre-trained encoder outperformed other U-Net, U-Net++, and FPN (Feature Pyramid Network) models with the highest Dice similarity coefficient (DSC) and intersection over union (IoU) scores, which were previously used in many other medical applications. We presented a three-dimensional brain model highlighting hemorrhages from ground truth and predicted masks. The volume of hemorrhage was measured volumetrically to determine the size of the hematoma. This study is essential in examining ICH for diagnostic purposes in clinical practice by comparing the predicted 3D model with the ground truth.