Publications by authors named "R S Walz"
COVID-19 has significant long-term impacts, including a chronic syndrome known as long-COVID, characterized by persistent symptoms post-recovery. The inflammatory response during acute infection is hypothesized to influence long-term outcomes. This study aimed to identify inflammatory biomarkers predictive of functional outcomes one year after hospital discharge.
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- * Cannabis or cannabinoid compounds have emerged as potential treatments for Alcohol Use Disorder (AUD), but their use alongside alcohol may lead to heightened risks and negative effects.
- * The chapter evaluates the benefits and drawbacks of using cannabis in conjunction with alcohol, considering the recent trends in cannabis legalization and their implications for treatment of AUD.
Objective: To investigate the relationship between the levels of adipokines and other endocrine biomarkers and patient outcomes in hospitalized patients with COVID-19.
Methods: In a prospective study that included 213 subjects with COVID-19 admitted to the intensive care unit, we measured the levels of cortisol, C-peptide, glucagon-like peptide-1, insulin, peptide YY, ghrelin, leptin, and resistin.; their contributions to patient clustering, disease severity, and predicting in-hospital mortality were analyzed.
The epileptic heart: Cardiac comorbidities and complications of epilepsy. Atrial and ventricular structure and function by echocardiography in individuals with epilepsy - From clinical implications to individualized assessment.
Epilepsy Behav Rep
April 2024
Article Synopsis
- * Individuals with epilepsy face heightened risks for various heart-related problems, including arrhythmias, heart attacks, and even sudden death, largely due to a mix of traditional risk factors, genetic issues, and effects of medications.
- * The text emphasizes the need for better cardiac risk assessments in epilepsy patients, discussing echocardiographic findings and proposing future research and risk stratification models to tackle these concerns.
Article Synopsis
- The study evaluated the effectiveness of a machine learning algorithm for detecting focal epileptic seizures using heart rate variability data, with previous success seen in a Danish cohort.
- A total of 34 patients were analyzed, revealing the algorithm's ability to detect 84.8% of seizures, with high sensitivity for generalized tonic-clonic seizures at 96.2%.
- Results suggest this algorithm could be reliably used for real-time seizure detection in diverse patient populations, potentially integrating into wearable technology for epilepsy management.