Publications by authors named "M Brvar"
Article Synopsis
- Quetiapine has become a widely used drug in cases of acute intoxication and a literature review was conducted to understand its effects and risks.
- The study analyzed 134 cases of quetiapine ingestion, revealing a common median dose of 10 g and a peak serum concentration (c) of 4 mg/L, with a longer half-life during acute intoxication compared to therapeutic use.
- Higher doses and concentrations of quetiapine were linked to more severe clinical symptoms like central nervous system depression and tachycardia, with doses above 3 g or concentrations above 2 mg/L indicating a high risk for severe complications.
Profound and transient thrombocytopenia of functional platelets without bleeding was observed in patients envenomed by (). This condition was rapidly reversed by administration of F(ab) fragments of immunoglobulin G targeting the whole venom, leaving platelets fully functional. To investigate the potential role of snake venom C-type lectin-like proteins (snaclecs) in this process, -snaclecs were isolated from the crude venom using different liquid chromatographies.
View Article and Find Full Text PDFAlcohol hangover (veisalgia) is a fairly common phenomenon. The pathogenesis of veisalgia is not understood and treatment has not yet been established. Occasionally, students take N-acetylcysteine (NAC) before binge drinking to alleviate hangover.
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- This study evaluates the clinical features and outcomes of acute cannabis toxicity versus acute synthetic cannabinoid receptor agonist toxicity in European emergency departments from 2013-2020.
- Using data from the European Drug Emergencies Network Plus, researchers analyzed 2,657 cases of cannabis exposure and 503 cases of synthetic cannabinoid exposure to compare their effects.
- The findings indicate that synthetic cannabinoid exposures are linked to more severe neuropsychiatric symptoms, while cannabis exposures are associated with cardiovascular issues.
The growing threat of antibiotic resistance necessitates accurate differentiation between bacterial and viral infections for proper antibiotic administration. In this study, a Virus vs. Bacteria machine learning model was developed to distinguish between these infection types using 16 routine blood test results, C-reactive protein concentration (CRP), biological sex, and age.
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