Heterogeneous gene expression during early arteriovenous fistula remodeling suggests that downregulation of metabolism predicts adaptive venous remodeling.

Clinical outcomes of arteriovenous fistulae (AVF) for hemodialysis remain inadequate since biological mechanisms of AVF maturation and failure are still poorly understood. Aortocaval fistula creation (AVF group) or a sham operation (sham group) was performed in C57BL/6 mice. Venous limbs were collected on postoperative day 7 and total RNA was extracted for high throughput RNA sequencing and bioinformatic analysis. Genes in metabolic pathways were significantly downregulated in the AVF, whereas significant sex differences were not detected. Since gene expression patterns among the AVF group were heterogenous, the AVF group was divided into a 'normal' AVF (nAVF) group and an 'outliers' (OUT) group. The gene expression patterns of the nAVF and OUT groups were consistent with previously published data showing venous adaptive remodeling, whereas enrichment analyses showed significant upregulation of metabolism, inflammation and coagulation in the OUT group compared to the nAVF group, suggesting the heterogeneity during venous remodeling reflects early gene expression changes that may correlate with AVF maturation or failure. Early detection of these processes may be a translational strategy to predict fistula failure and reduce patient morbidity.

Common Data Elements and Databases Essential for the Study of Musculoskeletal Injuries in Military Personnel.

INTRODUCTION: Injuries are the leading cause of medical encounters with over 2 million medical encounters for musculoskeletal (MSK) conditions and over 700,000 acute injuries per year. Musculoskeletal injuries (MSKIs) are by far the leading health and readiness problem of the U.S. Military. The Proceedings of the International Collaborative Effort on Injury Statistics published a list of 12 data elements deemed necessary for injury prevention in the civilian population; however, there are no standardized list of common data elements (CDEs) across the DoD specifically designed to study MSKIs in the Military Health System (MHS). This study aims to address this gap in knowledge by defining CDEs across the DoD for MSKIs, establishing a CDE dictionary, and compiling other necessary information to quantify MSKI disease burden in the MHS. MATERIALS AND METHODS: Between November 2022 and March 2023, we conducted an environmental scan of current MSKI data metrics across the DoD. We used snowball sampling with active engagement of groups housing datasets that contained MSKI data elements to determine CDEs as well as information on readiness databases across the DoD containing up-to-date personnel information on disease, hospitalizations, limited duty days (LDDs), and deployability status for all military personnel, as well as MSKI-specific measures from the MHS Dashboard which tracks key performance measures. RESULTS: We identified 8 unique databases: 5 containing demographic and diagnostic information (Defense Medical Surveillance System, Medical Assessment and Readiness Systems, Military Health System Data Repository, Person-Data Environment, and Soldier Performance, Health, and Readiness Database); and 3 containing LDD information (Aeromedical Services Information Management System, eProfile, and Limited Duty Sailor Marines Readiness Tracker). Nine CDEs were identified: DoD number, sex, race, ethnicity, branch of service, rank, diagnosis, Common Procedural Terminology coding, and cause codes, as they may be captured in any database that is a derivative of the Military Health System Data Repository. Medical Assessment and Readiness Systems contained most variables of interest, excluding injury/place of region and time in service. The Limited Duty Sailor Marines Readiness Tracker contains a variable corresponding to "days on limited duty." The Aeromedical Services Information Management System uses the "release date" and "profile date" to calculate LDDs. The eProfile system determines LDDs by the difference between the "expiration date" and "approved date." In addition, we identified 2 measures on the MHS Dashboard. One measures the percentage of service members (SMs) who are on limited duty for longer than 90 days because of an MSKI and the other tracks the percentage of SMs that are not medically ready for deployment because of a deployment-limiting medical condition. CONCLUSIONS: This article identifies core data elements needed to understand and prevent MSKIs and where these data elements can be found. These elements should inform researchers and result in evidence-informed policy decisions supporting SM health to optimize military force readiness.

Emergent Applications of Machine Learning for Diagnosing and Managing Appendicitis: A State-of-the-Art Review.

Background: Appendicitis is an inflammatory condition that requires timely and effective intervention. Despite being one of the most common surgically treated diseases, the condition is difficult to diagnose because of atypical presentations. Ultrasound and computed tomography (CT) imaging improve the sensitivity and specificity of diagnoses, yet these tools bear the drawbacks of high operator dependency and radiation exposure, respectively. However, new artificial intelligence tools (such as machine learning) may be able to address these shortcomings. Methods: We conducted a state-of-the-art review to delineate the various use cases of emerging machine learning algorithms for diagnosing and managing appendicitis in recent literature. The query ("Appendectomy" OR "Appendicitis") AND ("Machine Learning" OR "Artificial Intelligence") was searched across three databases for publications ranging from 2012 to 2022. Upon filtering for duplicates and based on our predefined inclusion criteria, 39 relevant studies were identified. Results: The algorithms used in these studies performed with an average accuracy of 86% (18/39), a sensitivity of 81% (16/39), a specificity of 75% (16/39), and area under the receiver operating characteristic curves (AUROCs) of 0.82 (15/39) where reported. Based on accuracy alone, the optimal model was logistic regression in 18% of studies, an artificial neural network in 15%, a random forest in 13%, and a support vector machine in 10%. Conclusions: The identified studies suggest that machine learning may provide a novel solution for diagnosing appendicitis and preparing for patient-specific post-operative complications. However, further studies are warranted to assess the feasibility and advisability of implementing machine learning-based tools in clinical practice.