ABSTRACT
Screening and diagnosing abdominal aortic aneurysms (AAA) are currently dependent on imaging studies such as ultrasound or computed tomography angiography. All imaging studies offer distinct advantages but also suffer from inherent limitations such as examiner dependency or ionizing radiation. Bioelectrical impedance analysis has previously been investigated with respect to its use in the detection of several cardiovascular and renal pathologies. The present pilot study assessed the feasibility of AAA detection based on bioimpedance analysis. In this single-center exploratory pilot study, measurements were conducted among three different cohorts: patients with AAA, end-stage renal disease patients without AAA, and healthy controls. The device used in the study, CombynECG, is an open-market accessible device for segmental bioelectrical impedance analysis. The data was preprocessed and used to train four different machine learning models on a randomized training sample (80% of the full dataset). Each model was then evaluated on a test set (20% of the full dataset). The total sample included 22 patients with AAA, 16 chronic kidney disease patients, and 23 healthy controls. All four models showed strong predictive performance in the test partitions. Specificity ranged from 71.4 to 100%, while sensitivity ranged from 66.7 to 100%. The best-performing model had 100% accuracy for classification when applied to the test sample. Additionally, an exploratory analysis to approximate the maximum AAA diameter was conducted. An association analysis revealed several impedance parameters that might possess predictive ability with respect to aneurysm size. AAA detection via bioelectrical impedance analysis is technically feasible and appears to be a promising technology for large-scale clinical studies and routine clinical screening assessments.
ABSTRACT
To investigate long COVID-19 syndrome (LCS) pathophysiology, we performed an exploratory study with blood plasma derived from three groups: 1) healthy vaccinated individuals without SARS-CoV-2 exposure; 2) asymptomatic recovered patients at least three months after SARS-CoV-2 infection and; 3) symptomatic patients at least 3 months after SARS-CoV-2 infection with chronic fatigue syndrome or similar symptoms, here designated as patients with long COVID-19 syndrome (LCS). Multiplex cytokine profiling indicated slightly elevated pro-inflammatory cytokine levels in recovered individuals in contrast to patients with LCS. Plasma proteomics demonstrated low levels of acute phase proteins and macrophage-derived secreted proteins in LCS. High levels of anti-inflammatory oxylipins including omega-3 fatty acids in LCS were detected by eicosadomics, whereas targeted metabolic profiling indicated high levels of anti-inflammatory osmolytes taurine and hypaphorine, but low amino acid and triglyceride levels and deregulated acylcarnitines. A model considering alternatively polarized macrophages as a major contributor to these molecular alterations is presented.
ABSTRACT
Despite the similar clinical outcomes after renin-angiotensin system (RAS) inhibitor (RASi) continuation or withdrawal in COVID-19, the effects on angiotensin-converting enzyme 2 (ACE2) and RAS metabolites remain unclear. In a substudy of the randomized controlled Austrian Corona Virus Adaptive Clinical Trial (ACOVACT), patients with hypertension and COVID-19 were randomized 1:1 to either RASi continuation (n = 30) or switch to a non-RASi medication (n = 29). RAS metabolites were analyzed using a mixed linear regression model (n = 30). Time to a sustained clinical improvement was equal and ACE2 did not differ between the groups but increased over time in both. Overall ACE2 was higher with severe COVID-19. ACE-S and Ang II levels increased as expected with ACE inhibitor discontinuation. These data support the safety of RASi continuation in COVID-19, although RASi were frequently discontinued in our post hoc analysis. The study was not powered to draw definite conclusions on clinical outcomes using small sample sizes.