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Alzheimer’s Disease (AD) is characterized by progressive cognitive decline, and the prevalence of the disease continues to rise as the population ages. With over 10 million new cases yearly, there is still no established way to quickly and accurately classify the stage a patient with Alzheimer’s has progressed to. Inaccurate classification leads to delayed or incorrect treatment, financial implications, and emotional distress, ultimately severely impacting both the patient and their family. This paper proposes a novel deep learning Convolutional Neural Network (CNN) model to assist in the classification of a patient's AD. The model development began with a dataset of over 6,000 pre-labeled Magnetic Resonance Imaging (MRI) scans categorized into four stages: normal, very-mild, mild, and moderate AD. Using transfer learning with InceptionV3, the framework analyzes class proportions and fine-tunes a CNN to leverage pre-trained features. The CNN then undergoes rigorous training with early stopping based on validation Area Under Curve (AUC). Upon completing the training, a comprehensive model evaluation is conducted, encompassing metrics such as AUC and confusion matrices. Using these methods, three different mini-models are constructed, all with different training parameters and number of epochs. Conclusively, the successful models are saved, and an array of evaluation metrics such as accuracy, precision, F1 score, and recall are then used to analyze the models' results. The results show that the proposed method achieves 95.0% or more across all metrics in seconds, demonstrating a promising classification performance that can greatly assist doctors in the classification of AD, helping achieve not only higher diagnostic accuracy but also allowing for faster diagnosis. The results of this model demonstrate significant potential to enhance patient care and highlight the impact of Artificial Intelligence (AI) on AD diagnosis. With the ability to classify MRIs with 20 percent greater accuracy and reduce diagnostic time from months to seconds, the promise of AI in not only AD diagnosis, but the whole of healthcare will enable us to take steps toward a better world. Envisioning a future where the healthcare system can prioritize patient wants rather than just needs. Abbreviations: AD - Alzheimer’s Disease; AI - Artificial Intelligence; AUC – Area Under Curve; CNN - Convolutional Neural Network; RNN - Recurrent Neural Networks; ROC - Receiver Operating Characteristic

Abstract Implantable devices interfacing with peripheral nerves exhibit limited longevity and resolution. Poor nerve-electrode interface quality, invasive surgical placement and development of foreign body reaction combine to limit research and clinical application of these devices. Here, we develop cuff implants with a conformable design that achieve high-quality and stable interfacing with nerves in chronic implantation scenarios. When implanted in sensorimotor nerves of the arm in awake rats for 21 days, the devices record nerve action potentials with fascicle-specific resolution and extract from these the conduction velocity and direction of propagation. The cuffs exhibit high biocompatibility, producing lower levels of fibrotic scarring than clinically equivalent PDMS silicone cuffs. In addition to recording nerve activity, the devices are able to modulate nerve activity at sub-nerve resolution to produce a wide range of paw movements. When used in a partial nerve ligation rodent model, the cuffs identify and characterise changes in nerve C fibre activity associated with the development of neuropathic pain in freely-moving animals. The developed implantable devices represent a platform enabling new forms of fine nerve signal sensing and modulation, with applications in physiology research and closed-loop therapeutics.

BackgroundSystemic lupus erythematosus is a chronic, multisystem, inflammatory disease of autoimmune etiology occurring predominantly in women. A major hurdle to the diagnosis, treatment, and therapeutic advancement of this disease is its heterogeneous nature, which presents as a wide range of symptoms such as fatigue, fever, musculoskeletal involvement, neuropsychiatric disorders, and cardiovascular involvement with varying severity. The current therapeutic approach to this disease includes the administration of immunomodulatory drugs that may produce unfavorable secondary effects. ObjectiveThis study explores the known relationship between the autonomic nervous system and inflammatory pathways to improve patient outcomes by treating autonomic nervous system dysregulation in patients via noninvasive vagus nerve stimulation. In this study, data including biomarkers, physiological signals, patient outcomes, and patient quality of life are being collected and analyzed. After completion of the clinical trial, a computer model will be developed to identify the biomarkers and physiological signals related to lupus activity in order to understand how they change with different noninvasive vagus nerve stimulation frequency parameters. Finally, we propose building a decision support system with integrated noninvasive wearable technologies for continuous cardiovascular and peripheral physiological sensing for adaptive, patient-specific optimization of the noninvasive vagus nerve stimulation frequency parameters in real time. MethodsThe protocol was designed to evaluate the efficacy and safety of transauricular vagus nerve stimulation in patients with systemic lupus erythematosus. This multicenter, national, randomized, double-blind, parallel-group, placebo-controlled study will recruit a minimum of 18 patients diagnosed with this disease. Evaluation and treatment of patients will be conducted in an outpatient clinic and will include 12 visits. Visit 1 consists of a screening session. Subsequent visits up to visit 6 involve mixing treatment and evaluation sessions. Finally, the remaining visits correspond with early and late posttreatment follow-ups. ResultsOn November 2022, data collection was initiated. Of the 10 participants scheduled for their initial appointment, 8 met the inclusion criteria, and 6 successfully completed the entire protocol. Patient enrollment and data collection are currently underway and are expected to be completed in December 2023. ConclusionsThe results of this study will advance patient-tailored vagus nerve stimulation therapies, providing an adjunctive treatment solution for systemic lupus erythematosus that will foster adoption of technology and, thus, expand the population with systemic lupus erythematosus who can benefit from improved autonomic dysregulation, translating into reduced costs and better quality of life. Trial RegistrationClinicalTrials.gov NCT05704153; https://clinicaltrials.gov/study/NCT05704153 International Registered Report Identifier (IRRID)DERR1-10.2196/48387

Abstract Systemic lupus erythematosus (SLE) is a chronic systemic autoimmune disorder that commonly affects the skin, joints, kidneys, and central nervous system. Although great progress has been made over the years, patients still experience unfavorable secondary effects from medications, increased economic burden, and higher mortality rates compared to the general population. To alleviate these current problems, non-invasive, non-pharmacological interventions are being increasingly investigated. One such intervention is non-invasive vagus nerve stimulation, which promotes the upregulation of the cholinergic anti-inflammatory pathway that reduces the activation and production of pro-inflammatory cytokines and reactive oxygen species, culpable processes in autoimmune diseases such as SLE. This review first provides a background on the important contribution of the autonomic nervous system to the pathogenesis of SLE. The gross and structural anatomy of the vagus nerve and its contribution to the inflammatory response are described afterwards to provide a general understanding of the impact of stimulating the vagus nerve. Finally, an overview of current clinical applications of invasive and non-invasive vagus nerve stimulation for a variety of diseases, including those with similar symptoms to the ones in SLE, is presented and discussed. Overall, the review presents neuromodulation as a promising strategy to alleviate SLE symptoms and potentially reverse the disease.

Abstract Coronavirus SARS-COV-2 infections continue to spread across the world, yet effective large-scale disease detection and prediction remain limited. COVID Control: A Johns Hopkins University Study, is a novel syndromic surveillance approach, which collects body temperature and COVID-like illness (CLI) symptoms across the US using a smartphone app and applies spatio-temporal clustering techniques and cross-correlation analysis to create maps of abnormal symptomatology incidence that are made publicly available. The results of the cross-correlation analysis identify optimal temporal lags between symptoms and a range of COVID-19 outcomes, with new taste/smell loss showing the highest correlations. We also identified temporal clusters of change in taste/smell entries and confirmed COVID-19 incidence in Baltimore City and County. Further, we utilized an extended simulated dataset to showcase our analytics in Maryland. The resulting clusters can serve as indicators of emerging COVID-19 outbreaks, and support syndromic surveillance as an early warning system for disease prevention and control.

Abstract Modulation of the nervous system by delivering electrical or pharmaceutical agents has contributed to the development of novel treatments to serious health disorders. Recent advances in multidisciplinary research has enabled the emergence of a new powerful therapeutic approach called bioelectronic medicine. Bioelectronic medicine exploits the fact that every organ in our bodies is neurally innervated and thus electrical interfacing with peripheral nerves can be a potential pathway for diagnosing or treating diseases such as diabetes. In this context, a plethora of studies have confirmed the important role of the nervous system in maintaining a tight regulation of glucose homeostasis. This has initiated new research exploring the opportunities of bioelectronic medicine for improving glucose control in people with diabetes, including regulation of gastric emptying, insulin sensitivity, and secretion of pancreatic hormones. Moreover, the development of novel closed-loop strategies aims to provide effective, specific and safe interfacing with the nervous system, and thereby targeting the organ of interest. This is especially valuable in the context of chronic diseases such as diabetes, where closed-loop bioelectronic medicine promises to provide real-time, autonomous and patient-specific therapies. In this article, we present an overview of the state-of-the-art for closed-loop neuromodulation systems in relation to diabetes and discuss future related opportunities for management of this chronic disease.

Abstract Diabetes is a disease caused by a breakdown in the glucose metabolic process resulting in abnormal blood glucose fluctuations. Traditionally, control has involved external insulin injection in response to elevated blood glucose to substitute the role of the beta cells in the pancreas which would otherwise perform this function in a healthy individual. The central nervous system (CNS), however, also plays a vital role in glucose homoeostasis through the control of pancreatic secretion and insulin sensitivity which could potentially be used as a pathway for enhancing glucose control. In this review, we present an overview of the brain regions, peripheral nerves and molecular mechanisms by which the CNS regulates glucose metabolism and the potential benefits of modulating them for diabetes management. Development of technologies to interface to the nervous system will soon become a reality through bioelectronic medicine and we present the emerging opportunities for the treatment of type 1 and type 2 diabetes.