Effect of pathologic fractures on survival in multiple myeloma patients: a case control study

<p>Abstract</p> <p>Background</p> <p>Multiple Myeloma (MM) is a B cell neoplasm characterized by the clonal proliferation of plasma cells. Skeletal complications are found in up to 80% of myeloma patients at presentation and are major cause of morbidity.</p> <p>Methods</p> <p>49 patients were enrolled with MM admitted to Black Sea Technical University Hospital between 2002–2005. Pathologic fractures (PFs) were determined and the patients with or without PF were followed up minumum 3 years for survival analysis.</p> <p>Results</p> <p>PF was observed in 24 patients (49%) and not observed in 25 patients (51%). The risk of death was increased in the patients with PF compared with patients who had no fractures. While overall survival was 17.6 months in the patients with PFs, it was 57.3 months in the patients with no PFs.</p> <p>Conclusion</p> <p>These findings suggest that PFs may induce reduced survival and increased mortality in the MM patients, however, larger sample size is essential to draw clearer conclusions added to these data.</p

Crimean-Congo Hemorrhagic Fever in Turkey

Nineteen cases of suspected Crimean-Congo hemorrhagic fever reported from Turkey

Contemporary Challenges and Solutions

CA18131 CP16/00163 NIS-3317 NIS-3318 decision 295741 C18/BM/12585940The human microbiome has emerged as a central research topic in human biology and biomedicine. Current microbiome studies generate high-throughput omics data across different body sites, populations, and life stages. Many of the challenges in microbiome research are similar to other high-throughput studies, the quantitative analyses need to address the heterogeneity of data, specific statistical properties, and the remarkable variation in microbiome composition across individuals and body sites. This has led to a broad spectrum of statistical and machine learning challenges that range from study design, data processing, and standardization to analysis, modeling, cross-study comparison, prediction, data science ecosystems, and reproducible reporting. Nevertheless, although many statistics and machine learning approaches and tools have been developed, new techniques are needed to deal with emerging applications and the vast heterogeneity of microbiome data. We review and discuss emerging applications of statistical and machine learning techniques in human microbiome studies and introduce the COST Action CA18131 “ML4Microbiome” that brings together microbiome researchers and machine learning experts to address current challenges such as standardization of analysis pipelines for reproducibility of data analysis results, benchmarking, improvement, or development of existing and new tools and ontologies.publishersversionpublishe

Statistical and Machine Learning Techniques in Human Microbiome Studies: Contemporary Challenges and Solutions

The human microbiome has emerged as a central research topic in human biology and biomedicine. Current microbiome studies generate high-throughput omics data across different body sites, populations, and life stages. Many of the challenges in microbiome research are similar to other high-throughput studies, the quantitative analyses need to address the heterogeneity of data, specific statistical properties, and the remarkable variation in microbiome composition across individuals and body sites. This has led to a broad spectrum of statistical and machine learning challenges that range from study design, data processing, and standardization to analysis, modeling, cross-study comparison, prediction, data science ecosystems, and reproducible reporting. Nevertheless, although many statistics and machine learning approaches and tools have been developed, new techniques are needed to deal with emerging applications and the vast heterogeneity of microbiome data. We review and discuss emerging applications of statistical and machine learning techniques in human microbiome studies and introduce the COST Action CA18131 "ML4Microbiome" that brings together microbiome researchers and machine learning experts to address current challenges such as standardization of analysis pipelines for reproducibility of data analysis results, benchmarking, improvement, or development of existing and new tools and ontologies

Effects of dietary mannan oligosaccharides (MOS) on growth, body composition, and intestine and liver histology of the hybrid tilapia (Oreochromis niloticus x 0. aureus)

This is the first study on the effects of dietary mannan oligosaccharides (MOS) on growth, body composition, and intestine and liver histology of hybrid tilapia (Oreochromis niloticus x O. aureus). Experimental diets were prepared from commercial trout diet, supplemented with MOS at levels of 0, 1.5, 3.0, or 4.5 g MOS/kg feed and randomly assigned to triplicate groups. At the end of the trial, there were no significant differences between treatment groups (p>0.05) in growth parameters (live weight gain, specific growth rate, feed conversion ratio, protein efficien- cy ratio) or body indices (hepatosomatic and viscerosomatic). Dry matter and protein contents increased with increasing rates of dietary MOS (p<0.05) while the mean villi length of fish fed the diet containing 1.5‰ MOS was significantly longer (p<0.05) than that of the fish fed 4.5‰ dietary MOS. The different levels of dietary MOS had no detrimental effects on liver tissue or general fish health

Effects of dietary fish oil, soy-acid oil, and yellow grease on growth and hepatic lipidosis of hybrid tilapia fry

The objective of this study was to compare the effects of dietary lipids on growth and liver histopathology of hybrid tilapia, Oreochromis niloticus x O. aureus, fry (6.0 g). Fish were fed one of six diets containing 8.4% fish oil (control), 8.4% soy-acid oil, 8.4% yellow grease, 5.6% yellow grease plus 2.8% soy-acid oil, 2.8% yellow grease plus 4.6% soy-acid oil, or 4.2% soy-acid oil plus 4.2% yellow grease for 60 days. Growth was similar in all groups and retarded in compari- son to earlier studies. Lipid accumulation as well as microvesicular (foamy degeneration) and macrovesicular degeneration in the liver were histopathologically detected

Effects of dietary mannan oligosaccharides (MOS) on growth, body composition, and intestine and liver histology of the hybrid Tilapia (Oreochromis niloticus x O-aureus)

This is the first study on the effects of dietary mannan oligosaccharides (MOS) on growth, body composition, and intestine and liver histology of hybrid tilapia (Oreochromis niloticus x O. aureus). Experimental diets were prepared from commercial trout diet, supplemented with MOS at levels of 0, 1.5, 3.0, or 4.5 g MOS/kg feed and randomly assigned to triplicate groups. At the end of the trial, there were no significant differences between treatment groups (p>0.05) in growth parameters (live weight gain, specific growth rate, feed conversion ratio, protein efficiency ratio) or body indices (hepatosomatic and viscerosomatic). Dry matter and protein contents increased with increasing rates of dietary MOS (p<0.05) while the mean villi length of fish fed the diet containing 1.5 parts per thousand. MOS was significantly longer (p<0.05) than that of the fish fed 4.5%. dietary MOS. The different levels of dietary MOS had no detrimental effects on liver tissue or general fish health

SPADIS: an algorithm for selecting predictive and diverse SNPs in GWAS

Phenotypic heritability of complex traits and diseases is seldom explained by individual genetic variants identified in genome-wide association studies (GWAS). Many methods have been developed to select a subset of variant loci, which are associated with or predictive of the phenotype. Selecting connected SNPs on SNP-SNP networks have been proven successful in finding biologically interpretable and predictive SNPs. However, we argue that the connectedness constraint favors selecting redundant features that affect similar biological processes and therefore does not necessarily yield better predictive performance. In this paper, we propose a novel method called SPADIS that favors the selection of remotely located SNPs in order to account for their complementary effects in explaining a phenotype. SPADIS selects a diverse set of loci on a SNP-SNP network. This is achieved by maximizing a submodular set function with a greedy algorithm that ensures a constant factor approximation to the optimal solution. We compare SPADIS to the state-of-the-art method SConES, on a dataset of Arabidopsis Thaliana with continuous flowering time phenotypes. SPADIS has better average phenotype prediction performance in 15 out of 17 phenotypes when the same number of SNPs are selected and provides consistent improvements across multiple networks and settings on average. Moreover, it identifies more candidate genes and runs faster

Implantation of a permanent pacemaker in a patient with severe Parkinson's disease and a preexisting bilateral deep brain stimulator

Cardiac pacemakers and implantable defibrillators are commonly used therapeutic modalities in cardiac arrhythmias. Thalamic deep brain stimulation has also become an important modality in the treatment of drug-refractory tremors and other complications in advanced Parkinson's disease. Concerns exist about the potential electrical interaction and interference between these 2 devices in the same patient. There are only a limited number of reports that have investigated this issue. We describe a patient with advanced Parkinson's disease and a previously implanted deep brain stimulator, who subsequently needed a permanent cardiac pacemaker due to severe bradyarrhythmia. Despite the probability of interference between the devices, there were no problems during implantation of the cardiac pacemaker; both the deep brain stimulator and cardiac pacemaker functioned appropriately afterwards

Relationship between disease severity and D-dimer levels measured with two different methods in pulmonary embolism patients

<p>Abstract</p> <p>Pulmonary embolism (PE) is diagnosed with increasing frequency nowadays due to advances in the diagnostic methods and the increased awareness of the disease. There is a tendency to use non invasive diagnostic methods for all diseases. D-dimer is a fibrin degradation product. We aimed to detect the relationship between disease severity and the D-dimer levels measured with two different methods. We compared D-dimer levels in cases of massive vs. non-massive PE. A total of 89 patients who were diagnosed between 2006 and 2008 were included in the study. Group 1 included patients whose D-dimer levels were measured with the immunoturbidimetric polyclonal antibody method (D-dimerPLUS^®), while Group 2 patients made use of the immunoturbidimetric monoclonal antibody method (InnovanceD-DIMER^®). In each group, the D-dimer levels of those with massive and non-massive PE were compared, using the Mann Whitney U test. The mean age of Group 1 (25 F/26 M) was 56.0 ± 17.9 years, and that of Group 2 (22 F/16 M) was 52.9 ± 17.9 years. There was no statistical difference in gender and mean age between the two groups (p > 0.05). In Group 1, the mean D-dimer level of massive cases (n = 7) was 1444.9 ± 657.9 μg/L and that of nonmassive PE (n = 34) was 1304.7 ± 350.5 μg/L (p > 0.05). In Group 2, the mean D-dimer level of massive cases (n = 6) was 9.7 ± 2.2 mg/L and that of non-massive PE (n = 32) was 5.9 ± 1.3 mg/L (p < 0.05). The mean D-dimer levels of massive cases as measured with the immunoturbidimetric monoclonal antibody method were significantly higher. Pulmonary embolism patients whose D-dimer levels are higher (especially higher than 6.6 mg/L) should be considered as possibly having massive embolism. Diagnostic procedures and management can be planned according to this finding.</p