Vancomycin versus Placebo for Treating Persistent Fever in Patients with Neutropenic Cancer Receiving Piperacillin-Tazobactam Monotherapy

This prospective, double-blind trial assessed whether the addition of a glycopeptide would be able to reduce the time to defervescence in neutropenic patients with cancer who had persistent fever 48-60 h after the initiation of empirical piperacillin-tazobactam monotherapy. Of 763 eligible patients, 165 with persistent fever were randomized to receive piperacillin-tazobactam therapy plus either vancomycin therapy or placebo. Defervescence was observed in 82 (95%) of 86 patients in the vancomycin group and in 73 (92%) of 79 patients in the placebo group (P = .52). The distributions of the time to defervescence were not statistically significant between the 2 groups (estimated hazard ratio, 1.03; 95% confidence interval, 0.75-1.43; P = .75). The number of additional episodes of gram-positive bacteremia and the percentage of patients for whom amphotericin B was empirically added to their therapy regimen were also similar in both groups. This study failed to demonstrate that the empirical addition of vancomycin therapy to the treatment regimen is of benefit to persistently febrile neutropenic patients with cance

HLA Genes, Islet Autoantibodies and Residual C-Peptide at the Clinical Onset of Type 1 Diabetes Mellitus and the Risk of Retinopathy 15 Years Later

HLA genes, islet autoantibodies and residual C-peptide were studied to determine the independent association of each exposure with diabetic retinopathy (DR), 15 years after the clinical onset of type 1 diabetes in 15-34 year old individuals.The cohort was identified in 1992 and 1993 by the Diabetes Incidence Study in Sweden (DISS), which investigates incident cases of diabetes for patients between 15 and 34 years of age. Blood samples at diagnosis were analyzed to determine HLA genotype, islet autoantibodies and serum C-peptide. In 2009, fundus photographs were obtained from patient records. Study measures were supplemented with data from the Swedish National Diabetes Registry.The prevalence of DR was 60.2% (148/246). Autoantibodies against the 65 kD isoform of glutamate decarboxylase (GADA) at the onset of clinical diabetes increased the risk of DR 15 years later, relative risk 1.12 for each 100 WHO units/ml, [95% CI 1.02 to 1.23]. This equates to risk estimates of 1.27, [95% CI 1.04 to 1.62] and 1.43, [95% CI 1.06 to 1.94] for participants in the highest 25(th) (GADA>233 WHO units/ml) and 5(th) percentile (GADA>319 WHO units/ml) of GADA, respectively. These were adjusted for duration of diabetes, HbA(1c), treated hypertension, sex, age at diagnosis, HLA and C-peptide. Islet cell autoantibodies, insulinoma-antigen 2 autoantibodies, residual C-peptide and the type 1 diabetes associated haplotypes DQ2, DQ8 and DQ6 were not associated with DR.Increased levels of GADA at the onset of type 1 diabetes were associated with DR 15 years later. These results, if confirmed, could provide additional insights into the pathogenesis of the most common microvascular complication of diabetes and lead to better risk stratification for both patient screenings and DR treatment trials

Reproducible radiomics through automated machine learning validated on twelve clinical applications

Radiomics uses quantitative medical imaging features to predict clinical outcomes. Currently, in a new clinical application, findingthe optimal radiomics method out of the wide range of available options has to be done manually through a heuristic trial-anderror process. In this study we propose a framework for automatically optimizing the construction of radiomics workflows perapplication. To this end, we formulate radiomics as a modular workflow and include a large collection of common algorithms foreach component. To optimize the workflow per application, we employ automated machine learning using a random search andensembling. We evaluate our method in twelve different clinical applications, resulting in the following area under the curves: 1)liposarcoma (0.83); 2) desmoid-type fibromatosis (0.82); 3) primary liver tumors (0.80); 4) gastrointestinal stromal tumors (0.77);5) colorectal liver metastases (0.61); 6) melanoma metastases (0.45); 7) hepatocellular carcinoma (0.75); 8) mesenteric fibrosis(0.80); 9) prostate cancer (0.72); 10) glioma (0.71); 11) Alzheimer’s disease (0.87); and 12) head and neck cancer (0.84). Weshow that our framework has a competitive performance compared human experts, outperforms a radiomics baseline, and performssimilar or superior to Bayesian optimization and more advanced ensemble approaches. Concluding, our method fully automaticallyoptimizes the construction of radiomics workflows, thereby streamlining the search for radiomics biomarkers in new applications.To facilitate reproducibility and future research, we publicly release six datasets, the software implementation of our framework,and the code to reproduce this study

Bipolar disorders

Bipolar disorder is characterized by (hypo)manic episodes and depressive episodes which alternate with euthymic periods. It causes serious disability with poor outcome, increased suicidality risk, and significant societal costs. This chapter describes the findings of the PET/SPECT research efforts and the current ideas on the pathophysiology of bipolar disorder. First, the cerebral blood flow and cerebral metabolism findings in the prefrontal cortex, limbic system, subcortical structures, and other brain regions are discussed, followed by an overview of the corticolimbic theory of mood disorders that explains these observations. Second, the neurotransmitter studies are discussed. The serotonin transporter alterations are described, and the variation in study results is explained, followed by an overview of the results of the various dopamine receptor and transporter molecules studies, taking into account also the relation to psychosis. Third, a concise overview is given of dominant bipolar disorder pathophysiological models, proposing starting points for future molecular imaging studies. Finally, the most important conclusions are summarized, followed by remarks about the observed molecular imaging study designs specific for bipolar disorder.</p

Distinct monocyte gene-expression profiles in autoimmune diabetes

OBJECTIVE-There is evidence that monocytes of patients with type 1 diabetes show proinflammatory activation and disturbed migration/adhesion, but the evidence is inconsistent. Our hypothesis is that monocytes are distinctly activated/disturbed in different subforms of autoimmune diabetes. RESEARCH DESIGN AND METHODS-We studied patterns of inflammatory gene expression in monocytes of patients with type 1 diabetes (juvenile onset, n = 30; adult onset, n = 30) and latent autoimmune diabetes of the adult (LADA) (n = 30) (controls subjects, n = 49; type 2 diabetic patients, n = 30) using quantitative PCR. We tested 25 selected genes: 12 genes detected in a prestudy via whole-genome analyses plus an additional 13 genes identified as part of a monocyte inflammatory signature previously reported. RESULTS-We identified two distinct monocyte gene expression clusters in autoimmune diabetes. One cluster (comprising 12 proinflammatory cytokine/compound genes with a putative key gene PDE4B) was detected in 60% of LADA and 28% of adult-onset type 1 diabetic patients but in only 10% of juvenile - onset type 1 diabetic patients. A second cluster (comprising 10 chemotaxis, adhesion, motility, and metabolism genes) was detected in 43% of juvenile-onset type 1 diabetic and 33% of LADA patients but in only 9% of adult-onset type 1 diabetic patients. CONCLUSIONS-Subgroups of type 1 diabetic patients show an abnormal monocyte gene expression with two profiles, supporting a concept of heterogeneity in the pathogenesis of autoimmune diabetes only partly overlapping with the presently known diagnostic categories