0000000000598306
AUTHOR
Andrzej Tukiendorf
Clinical manifestations of cancer in patients with acute pulmonary embolism
Background Neoplasmatic disease increases the risk of acute pulmonary embolism (APE) by different pathophysiological mechanisms that favor thrombosis in patients with cancer. Recently, the role of cancer (active and occult) in the prevalence of venous thromboembolism has been discussed more thoroughly in the subject literature. Material Medical records of 366 consecutive patients with a diagnosis of APE (aged: mean = 65.0 ± 16.6, median = 68, range = 19–94; men = 41%/women = 59%) were collected with a wide range of demographic data, medical history of coexisting diseases, computer examination, and laboratory tests. Methods The APE patients were analyzed in two groups: negative cancer cases …
Angiotensin II Type 1 Receptor Antibodies Are Higher in Lupus Nephritis and Vasculitis than Other Glomerulonephritis Patients
AbstractAngiotensin II type 1 receptor (AT1R) antibodies are considered non-HLA (human leukocyte antigen) antibodies connected with humoral rejection after kidney transplantation. The role of AT1R antibodies in the pathogenesis of glomerular diseases and systemic vasculitis is unknown. We assessed the level of AT1R antibodies in 136 patients with different types of glomerulonephritis and systemic vasculitis and we observed kidney function and proteinuria, serum albumin and total protein levels for 2 years. The mean levels of AT1R antibodies were the following: 6.00 ± 1.31 U/ml in patients with membranous nephropathy (n = 18), 5.67 ± 1.31 U/ml with focal and segmental glomerulosclerosis (n =…
Overlapped Bayesian spatio-temporal models to detect crime spots and their possible risk factors based on the Opole Province, Poland, in the years 2015–2019
AbstractGeostatistical methods currently used in modern epidemiology were adopted in crime science using the example of the Opole province, Poland, in the years 2015–2019. In our research, we applied the Bayesian spatio-temporal random effects models to detect ‘cold-spots’ and ‘hot-spots’ of the recorded crime numbers (all categories), and to ascertain possible risk factors based on the available statistical population (demographic), socio-economic and infrastructure area characteristics. Overlapping two popular geostatistical models in the analysis, ‘cold-spot’ and ‘hot-spot’ administrative units were detected which displayed extreme differences in crime and growth rates over time. Additio…