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.

Background: Craniosacral osteopathic manipulative medicine—also known as craniosacral therapy (CST)—is a widely taught and used component of osteopathic medicine. This paper seeks to systematically review and conduct a meta-analysis of randomized controlled trials assessing the clinical effectiveness of CST compared to standard care, sham treatment, or no treatment in adults and children.Methods: A search of Embase, PubMed, and Scopus was conducted on 10/29/2023 and updated on 5/8/2024. There was no restriction placed on the date of publication. A Google Scholar search was conducted to capture grey literature. Backward citation searching was also implemented. All randomized controlled trials employing CST for any clinical outcome were included. Studies not available in English as well as studies that did not report adequate data were excluded. Multiple reviewers were used to assess for inclusions, disagreements were settled by consensus. PRISMA guidelines were followed in the reporting of this meta-analysis. Cochrane’s Risk of Bias 2 tool was used to assess for risk of bias. All data were extracted by multiple independent observers. Effect sizes were calculated using a Hedge’s G value (standardized mean difference) and aggregated using random effects models. The GRADE system was used to assess quality of evidence.Results: The primary study outcome was the effectiveness of CST for selected outcomes as applied to non-healthy adults or children and measured by standardized mean difference effect size. Twenty-four RCTs were included in the final meta-analysis with a total of 1,613 participants. When subgroup analyses were performed by primary outcome only, no significant effects were found. When secondary outcomes were included in subgroup analyses, results showed that only Neonate health, structure (g = 0.66, 95% CI [0.30; 1.02], Prediction Interval [−0.73; 2.05]) and Pain, chronic somatic (g = 0.34, 95% CI [0.18; 0.50], Prediction Interval [−0.41; 1.09]) show reliable, statistically significant effect. However, these should not be interpreted as positive results as wide prediction intervals, high bias, and statistical limitations temper the real-world implications of this finding.Conclusions and relevance: CST demonstrated no significant effects in this meta-analysis, indicating a lack of usefulness in patient care for any of the studied indications.Pre-registration available at https://doi.org/10.17605/OSF.IO/54K6G.Systematic Review Registration: https://osf.io/54k6g.

Cortical spreading depression (CSD) is an electrophysiologic pathological state in which a wave of depolarization in the cerebral cortex is followed by the suppression of spontaneous neuronal activity. This transient spread of neuronal depolarization on the surface of the cortex is the hallmark of CSD. Numerous investigations have demonstrated that transmembrane ion transport, astrocytic ion clearing and fatigue, glucose metabolism, the presence of certain genetic markers, point mutations, and the expression of the enzyme responsible for the production of various arachidonic acid derivatives that participate in the inflammatory response, namely, cyclooxygenase (COX), all influence CSD. Here, we explore the associations between CSD occurrence in the cortex and various factors, including how CSD is related to migraines, how the glucose state affects CSD, the effect of TBI and its relationship with CSD and glucose metabolism, how different markers can be measured to determine the severity of CSD, and possible connections to oligemia, orexin, and leptin.

Background: Influenza virus is responsible for a recurrent, yearly epidemic in most temperate regions of the world. Flu has been responsible for a high disease burden in recent years, despite the confounding presence of SARS‐CoV‐2. However, the mechanisms behind seasonal variance in flu burden are not well understood. This study seeks to expand understanding of the impact of variable climate regions on seasonal flu trends. To that end, three climate regions have been selected. Each region represents a different ecological zone and provides different weather patterns. Methods: A long short‐term memory (LSTM)‐based recurrent neural network was used to predict influenza‐like‐illness trends for three separate locations: Hawaii, Vermont, and Nevada. Flu data were gathered from the Center for Disease Control as weekly influenza‐like‐illness (ILI) percentages. Weather data were collected from Visual Crossing and included temperature, wind speed, UV index, solar radiation, precipitation, and humidity. Data were prepared and the model was trained as described previously. Results: All three regions showed strong seasonality of flu trends with Hawaii having the largest absolute ILI values. Temperature showed a moderate negative correlation with ILI in all three regions (Vermont = −0.54, Nevada = −0.56, Hawaii = −0.44). Humidity was moderately correlated in Nevada (0.47) and weakly correlated with ILI in Hawaii (0.22). Vermont ILI did not correlate with humidity. Precipitation and wind speed were weakly correlated in all three regions. Solar radiation and UV index showed moderate correlation in Vermont (−0.33, −0.36) and Nevada (−0.53, −0.55), but only a weak correlation in Hawaii (−0.15, −0.18). When trained on the complete data sets, baseline model performances for all three datasets at +1 week were equivalent. Models trained on one region and used to predict cross‐regional data performed uniformly and equivalent to baseline. Conclusions: Results indicate that climate variables were weak to moderate predictors in all regions. Initial modeling attempts revealed uniform performance in all regions. Despite strong climate differences, cross‐regional LSTM models performed comparably, suggesting that seasonal patterns, rather than absolute climate variables, drive ILI trends. Additionally, this data suggests that absolute climate changes may not be influential as relative seasonal changes.

Context: Research and scholarship are core drivers of medicine in the modern era. Evidence-based practice continues to replace expert opinion and long-held practice beliefs. Involvement in the development and writing of these guidelines is critical for Doctors of Osteopathic Medicine (DOs) to maintain a seat at the academic table. According to the American Osteopathic Association (AOA), 11 % of practicing physicians in the US are DOs. This number is growing, as nearly 25 % of current medical students attend an osteopathic medical school. Without involvement in guideline development, DOs risk giving up control of their own practice of medicine. Objectives: To quantify the contribution of DOs to the body of literature guiding practice, author information was extracted from all US-based guidelines published in the years 2021, 2022, and 2023 listed in the Emergency Care Research Institute (ECRI) Guidelines Trust database. Methods: Authors of US-based guidelines listed in the ECRI Trust database from the years 2021–2023 were counted and categorized into one of three groups based on terminal degree: MD holders, DO holders, and Other-degree holders. Authors whose degrees could not be identified were counted as “Unidentifiable.” Additional data including sponsoring organization, organization type, and specialty were collected. Results: A total of 674 guidelines were published by US organizations in 2021, 2022, and 2023, with 604 reporting author information. A total of 9,376 authors were counted. Of that, 7,253 held an MD (77 %), 110 held a DO (1.2 %), and 1,848 held another terminal degree (19.7 %); meanwhile, 1.66 % of counted authors did not have an identifiable degree. A total of 604 guidelines published by US organizations were identified. Of these, 88 (14.6 %) contained at least one DO author in their author list. Sixty-two unique specialties were identified, along with 130 unique sponsoring organizations. Of those specialties, 28 (44.4 %) had at least one DO author of at least one guideline. Of 130 sponsoring organizations, 44 (33.8 %) developed at least one guideline with at least one DO author. No osteopathic sponsoring organizations were identified. Conclusions: Based on these results, we conclude that DOs are underrepresented in the development of guidelines.

Use injuries represent a serious and intractable 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 ML techniques as they have been applied to sports injury prediction and prevention to date. Literature from the last five years has been compiled and the findings presented. 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.