Chronic Migraine May Be Associated With Postprandial Hypoglycemia in Adult Men: A Case Series.

Migraine is a common neurological disorder that significantly impacts patients around the world. In the United States, one in six individuals suffers from a migraine disorder. Despite its high prevalence, the etiology of migraine is not well understood. Multiple factors likely contribute to the development of both acute and chronic migraine, making the consensus as to the cause and treatment difficult. Presented here are three case studies involving adult males suffering from chronic migraine. Each subject provided a medical history and underwent physical, psychological, and neurological examinations. In addition, relevant bloodwork and cervical spine X-rays were obtained. Physical examination, laboratory studies, imaging, and psychological metrics were unremarkable with the notable exception of the three-hour oral glucose tolerance tests. All three patients displayed hypoglycemia at three hours. Furthermore, their symptoms markedly improved with the initiation of a ketogenic diet. These data are suggestive of a potential link between postprandial hypoglycemia and chronic migraine. Despite the small sample size, we feel that this report presents possible evidence for a connection between postprandial hypoglycemia and chronic migraine. Furthermore, properly controlled studies of larger sample sizes are required, but we suggest that clinicians consider screening patients for this easily overlooked metabolic disturbance, especially in the absence of other options.

An Overview of Machine Learning Applications in Sports Injury Prediction.

Use injuries, i.e., injuries caused by repetitive strain on the body, represent a serious problem in athletics that has traditionally relied on historic datasets and human experience for prevention. Existing methodologies have been frustratingly slow at developing higher precision prevention practices. Technological advancements have permitted the emergence of artificial intelligence and machine learning (ML) as promising toolsets to enhance both injury mitigation and rehabilitation protocols. This article provides a comprehensive overview of recent advances in ML techniques as they have been applied to sports injury prediction and prevention. A comprehensive literature review was conducted searching PubMed/Medline, Institute of Electrical and Electronics Engineers (IEEE)/Institute of Engineering and Technology (IET), and ScienceDirect. Ovid Discovery and Google Scholar were used to provide additional aggregate results and a grey literature search. A focus was placed on papers published from 2017 to 2022. Algorithms of interest were limited to K-Nearest Neighbor (KNN), K-means, decision tree, random forest, gradient boosting and AdaBoost, and neural networks. A total of 42 original research papers were included, and their results were summarized. We conclude that given the current lack of open source, uniform data sets, as well as a reliance on dated regression models, no strong conclusions about the real-world efficacy of ML as it applies to sports injury prediction can be made. However, it is suggested that addressing these two issues will allow powerful, novel ML architectures to be deployed, thus rapidly advancing the state of this field, and providing validated clinical tools.

LSTM-based recurrent neural network provides effective short term flu forecasting.

BACKGROUND: Influenza virus is responsible for a yearly epidemic in much of the world. To better predict short-term, seasonal variations in flu infection rates and possible mechanisms of yearly infection variation, we trained a Long Short-Term Memory (LSTM)-based deep neural network on historical Influenza-Like-Illness (ILI), climate, and population data. METHODS: Data were collected from the Centers for Disease Control and Prevention (CDC), the National Center for Environmental Information (NCEI), and the United States Census Bureau. The model was initially built in Python using the Keras API and tuned manually. We explored the roles of temperature, precipitation, local wind speed, population size, vaccination rate, and vaccination efficacy. The model was validated using K-fold cross validation as well as forward chaining cross validation and compared to several standard algorithms. Finally, simulation data was generated in R and used for further exploration of the model. RESULTS: We found that temperature is the strongest predictor of ILI rates, but also found that precipitation increased the predictive power of the network. Additionally, the proposed model achieved a +1 week prediction mean absolute error (MAE) of 0.1973. This is less than half of the MAE achieved by the next best performing algorithm. Additionally, the model accurately predicted simulation data. To test the role of temperature in the network, we phase-shifted temperature in time and found a predictable reduction in prediction accuracy. CONCLUSIONS: The results of this study suggest that short term flu forecasting may be effectively accomplished using architectures traditionally reserved for time series analysis. The proposed LSTM-based model was able to outperform comparison models at the +1 week time point. Additionally, this model provided insight into the week-to-week effects of climatic and biotic factors and revealed potential patterns in data series. Specifically, we found that temperature is the strongest predictor of seasonal flu infection rates. This information may prove to be especially important for flu forecasting given the uncertain long-term impact of the SARS-CoV-2 pandemic on seasonal influenza.

Telemedicine, Isolation, and Anxiety: The Impact of COVID-19 Lockdowns on an 87-Year-Old Woman.

This report describes an 87-year-old female who received cognitive behavioral therapy and medication for anxiety before, during, and after the coronavirus disease 2019 (COVID-19) lockdowns. Our objective is to highlight the impact of isolation, examine the use of telemedicine during the pandemic, and stress the importance of early implementation of this technology. To this end, a chart review of psychotherapy and psychiatry progress notes from 2019 to 2022 and a patient interview were utilized to assess the impact of COVID-19 and telemedicine on the patient's anxiety symptoms, feelings of isolation, and treatment plan. Feelings of isolation, especially, were exacerbated. Prior to the pandemic, the patient was extremely physically and socially active. The reduced ability to interact with others and maintain her independence was detrimental. As a result, COVID-19 impacted the patient's progress significantly and caused regression of symptoms. However, telemedicine allowed for the continuation of therapy and follow-up to the present time. Though telemedicine allowed the patient to receive regular care for the duration of the lockdown and to regain control of anxiety symptoms, she only recently became comfortable with the technology. Now, the patient prefers the convenience and ease of telemedicine, continues to receive care through this modality, and feels that her current care is equivalent to in-person therapy. This case report should serve as a reminder of the effect that isolation can have on older adults with pre-existing anxiety. Notably, isolation may be related to the recent COVID-19 pandemic or other factors, such as reduced mobility or limited access to social services. In any case, isolation has a substantial impact on older patients' mental health. And, despite the availability of telemedicine, clinicians should be aware of the technical challenges surrounding emergency implementation. We suggest early exposure to telemedicine for patients, as well as staff training focused on the potential technical limitations of those patients. We also suggest an assessment of technical literacy, conducted early on as part of a patient's initial intake. The main limitation of this report, and the conclusions drawn herein, is the lack of quantitative measures available. Thus, assessment of the patient's condition and symptoms was restricted to clinician assessment and self-reported measures. We feel though that this remains a useful example of the long-term benefit of telemedicine for older individuals.