BackgroundPublished data regarding the impact of obesity on COVID-19 outcomes are inconsistent. However, in most studies, body composition was assessed using body mass index (BMI) alone, thus neglecting the presence and distribution of adipose tissue. Therefore, we aimed to investigate the impact of body and visceral fat on COVID-19 outcomes.MethodsObservational, prospective cohort study included 216 consecutive COVID-19 patients hospitalized at University Clinical Center Kragujevac (Serbia) from October to December 2021. Body composition was assessed using the BMI, body fat percentage (%BF), and visceral fat (VF) via bioelectrical impedance analysis (BIA). In addition to anthropometric measurements, variables in the research were socio-demographic and medical history data, as well as admission inflammatory biomarkers. Primary end-points were fatal outcomes and intensive care unit (ICU) admission.ResultsThe overall prevalence of obesity was 39.3% according to BMI and 50.9% according to % BF, while 38.4% of patients had very high VF levels. After adjusting odds ratio values for cofounding variables and obesity-related conditions, all three anthropometric parameters were significant predictors of primary end-points. However, we note that % BF and VF, compared to BMI, were stronger predictors of both mortality (aOR 3.353, aOR 3.05, and aOR 2.387, respectively) and ICU admission [adjusted odds ratio (aOR) 7.141, aOR 3.424, and aOR 3.133, respectively].ConclusionObesity is linked with COVID-19 mortality and ICU admission, with BIA measurements being stronger predictors of outcome compared to BMI use alone.

Chimeric antigen receptor T (CAR T) cell therapy achieved remarkable success in B-cell leukemia and lymphoma which led to its incorporation in treatment protocols for these diseases. CAR T cell therapy for chronic lymphocytic leukemia (CLL) patients showed less success compared to other malignant tumors. In this review, we discuss the published results regarding CAR T cell therapy of CLL, possible mechanisms of failures and expected developments.

Th rombotic thrombocytopenic purpura (TTP) is a clinical syndrome that manifests with thrombocytopenia, microangiopathic haemolytic anaemia and symptoms and signs of kidney and brain damage, but it rarely involves other organs. Th e main pathophysiological cause of TTP is diminished metalloproteinase ADAMTS13 activity; the main function of ADAMTS13 is to degrade large multimers of the von SAŽETAK Trombotična trombocitopenijska purpura (TTP) je klinički sindrom koji se odlikuje trombocitopenijom, mikroangiopatskom hemoliznom anemijom i simptomima i znacima oštećenja bubrega i mozga, ređe drugih organa. Glavni patofi ziološki mehanizam nastanka TTP-a je smanjena aktivnost metaloproteinaze ADAMTS13 čija je osnovna uloga razgradnja velikih multimera von Willebrand-ovog faktora. Smanjena aktivnost metaloproteinaze ADAMTS13 nastaje usled mutacije u genu za ADAMTS13 (urođeni TTP) ili usled nastanka antitela koja blokiraju ili antitela koja ubrzavaju razgradnju ADAMTS13 (stečeni TTP

We set out to examine the material, psychological, and sociological pathways mediating the income gradient in health and mortality. We used the 2008 General Social Survey-National Death Index dataset (N = 26,870), which contains three decades of social survey data in the US linked to thirty years of mortality follow-up. We grouped a large number of variables into 3 domains: material, psychological, and sociological using factor analysis. We then employed discrete-time hazard models to examine the extent to which these three domains mediated the income-mortality association among men and women. Overall, the gradient was weaker for females than for males. While psychological and material factors explained mortality hazards among females, hazards among males were explained only by social capital. Poor health significantly predicted both income and mortality, particularly among females, suggesting a strong role for reverse causation. We also find that many traditional associations between income and mortality are absent in this dataset, such as perceived social status.