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Objective: Stress urinary incontinence (SUI) affects a third of the female population and is characterized by involuntary urine leakage during abdominal efforts such as sneezing, laughing, or coughing. Acute neuromodulation of the bulbospongiosus nerve (BsN) was shown to increase bladder efficiency in aged and multiparous rabbits. This study investigates the efficacy of sub-chronic BsN neuromodulation in alleviating SUI-like deficits in mature multiparous rabbits, characterized by increased urine leakage and reduced leak point pressure . Results: Using the voiding spot assay, we observed a 40% reduction in urine leakage events after 30 days of BsN stimulation, which correlated with a 60% increase in daily micturition volume, a 10-fold increase in voided volume, and improvements in voiding efficiency and leak point pressure compared to negative control animals. Conclusion: In multiparous rabbits, BsN neuromodulation improves important SUI-like metrics including bladder capacity and urethral closure, supporting the use of this bioelectronic modality as treatment for SUI., Competing Interests: MR-O owns shares in Juniper Biomedical, a medical device company. FSR is currently an intern at Juniper Biomedical but was not affiliated with the company during the study and data analysis period. Juniper Biomedical did not have any role in animal data collection, analysis, or in the manuscript., (© 2024 The Authors.)

Glioblastoma Multiforme (GBM) is the most malignant type of all brain tumors. Current GBM treatment options include surgery, followed by radiation and chemotherapy. However, GBM can become resistant to therapy, resulting in tumor recurrence. GBM cells develop resistance to treatments by either downregulating cell death pathways (CD95) or upregulating cell survival pathways (NF-κB (p65)). Healthy tissues can be affected by the increased therapeutic dose. Therefore, it is important to develop a method that can only target GBM tumor cells, thereby reducing the non-specific uptake which will reduce the side effects. Here we demonstrate an application of novel priori activation of apoptosis pathways of tumor technology (AAAPT), which has been used to demonstrate the effect of targeted tumor sensitizers to make chemotherapy work at lower doses in breast, lung and prostate cancers. Treatment of GBM spheroids with AAAPT in 3D PEGDA microwells, showed an increase in cell death, an upregulation of cell death pathways, and a downregulation of cell survival pathways, in comparison to Temozolomide (TMZ), an oral alkylating agent, which is a commonly used chemotherapy in the treatment of GBM. The dose of AAAPT sensitizers may provide a promising method to increase treatment efficacy and reduce off-target toxicity, as an alternative to existing methods which cause significant off-target damage., (© 2023 The Authors.)

The new era of space exploration is increasing the astronaut's number and diversity in low orbit and beyond. The influx of such a diverse crew population will also increase the need for medical technologies to ensure safe and productive missions. Such a need represents a unique opportunity to innovate and develop diagnostics and treatment tools to meet future needs. Historically, terrestrial regulatory oversight of biomedical design processes was considered separate from spaceflight regulatory processes because it did not address spaceflight constraints. These constraints challenge the creative development of unique solutions for use in space. Translation between healthcare innovation in spaceflight to healthcare on Earth and vice versa requires understanding the commonalities, unique needs and constraints. This manuscript provides a framework for comparing Earth-space design processes and a perspective on the best practices to improve healthcare equity and health outcomes., (© 2023 The Authors.)

Goal: Systemic sclerosis (SSc) is a rare autoimmune, systemic disease with prominent fibrosis of skin and internal organs. Early diagnosis of the disease is crucial for designing effective therapy and management plans. Machine learning algorithms, especially deep learning, have been found to be greatly useful in biology, medicine, healthcare, and biomedical applications, in the areas of medical image processing and speech recognition. However, the need for a large training data set and the requirement for a graphics processing unit (GPU) have hindered the wide application of machine learning algorithms as a diagnostic tool in resource-constrained environments (e.g., clinics). Methods: In this paper, we propose a novel mobile deep learning network for the characterization of SSc skin. The proposed network architecture consists of the UNet, a dense connectivity convolutional neural network (CNN) with added classifier layers that when combined with limited training data, yields better image segmentation and more accurate classification, and a mobile training module. In addition, to improve the computational efficiency and diagnostic accuracy, the highly efficient training model called "MobileNetV2," which is designed for mobile and embedded applications, was used to train the network. Results: The proposed network was implemented using a standard laptop (2.5 GHz Intel Core i7). After fine tuning, our results showed the proposed network reached 100% accuracy on the training image set, 96.8% accuracy on the validation image set, and 95.2% on the testing image set. The training time was less than 5 hours. We also analyzed the same normal vs SSc skin image sets using the CNN using the same laptop. The CNN reached 100% accuracy on the training image set, 87.7% accuracy on the validation image set, and 82.9% on the testing image set. Additionally, it took more than 14 hours to train the CNN architecture. We also utilized the MobileNetV2 model to analyze an additional dataset of images and classified them as normal, early (mid and moderate) SSc or late (severe) SSc skin images. The network reached 100% accuracy on the training image set, 97.2% on the validation set, and 94.8% on the testing image set. Using the same normal, early and late phase SSc skin images, the CNN reached 100% accuracy on the training image set, 87.7% accuracy on the validation image set, and 82.9% on the testing image set. These results indicated that the MobileNetV2 architecture is more accurate and efficient compared to the CNN to classify normal, early and late phase SSc skin images. Conclusions: Our preliminary study, intended to show the efficacy of the proposed network architecture, holds promise in the characterization of SSc. We believe that the proposed network architecture could easily be implemented in a clinical setting, providing a simple, inexpensive, and accurate screening tool for SSc.