Context Previous studies indicate that industry-sponsored trials tend to draw proindustry conclusions. Objective To explore whether the association between funding and conclusions in randomized drug trials reflects treatment effects or adverse events. Design Observational study of 370 randomized drug trials included in metaanalyses from Cochrane reviews selected from the Cochrane Library, May 2001. From a random sample of 167 Cochrane reviews, 25 contained eligible meta-analyses (assessed a binary outcome; pooled at least 5 full-paper trials of which at least 1 reported adequate and 1 reported inadequate allocation concealment). The primary binary outcome from each meta-analysis was considered the primary outcome for all trials included in each meta-analysis. The association between funding and conclusions was analyzed by logistic regression with adjustment for treatment effect, adverse events, and additional confounding factors (methodological quality, control intervention, sample size, publication year, and place of publication). Main Outcome Measure Conclusions in trials, classified into whether the experimental drug was recommended as the treatment of choice or not. Results The experimental drug was recommended as treatment of choice in 16% of trials funded by nonprofit organizations, 30% of trials not reporting funding, 35% of trials funded by both nonprofit and for-profit organizations, and 51% of trials funded by for-profit organizations (PϽ.001; 2 test). Logistic regression analyses indicated that funding, treatment effect, and double blinding were the only significant predictors of conclusions. Adjusted analyses showed that trials funded by for-profit organizations were significantly more likely to recommend the experimental drug as treatment of choice (odds ratio, 5.3; 95% confidence interval, 2.0-14.4) compared with trials funded by nonprofit organizations. This association did not appear to reflect treatment effect or adverse events. Conclusions Conclusions in trials funded by for-profit organizations may be more positive due to biased interpretation of trial results. Readers should carefully evaluate whether conclusions in randomized trials are supported by data.

Recent studies suggest that diabetes mellitus increases the risk of developing hepatocellular carcinoma (HCC). The aim of this study is to quantify the risk of HCC among patients with both diabetes mellitus and hepatitis C in a large cohort of patients with chronic hepatitis C and advanced fibrosis. We included 541 patients of whom 85 (16%) had diabetes mellitus. The median age at inclusion was 50 years. The prevalence of diabetes mellitus was 10.5% for patients with Ishak fibrosis score 4, 12.5% for Ishak score 5, and 19.1% for Ishak score 6. Multiple logistic regression analysis showed an increased risk of diabetes mellitus for patients with an elevated body mass index (BMI) (odds ratio R ecent epidemiological studies suggest that the presence of diabetes mellitus increases the risk of hepatocellular carcinoma (HCC). 1,2 An explanation for this association may be that diabetes often occurs as part of the metabolic syndrome, which increases the risk of nonalcoholic steatohepatitis (NASH), and that HCC can be a late complication of NASH. 3 Diabetes mellitus is more prevalent among patients with chronic hepatitis C than in the general population. 4 Liver disease contributes to insulin resistance because it leads to a progressive impairment of insulin secretion and it induces hepatic insulin resistance. 5 Studies in transgenic mouse models that harbored the hepatitis C core gene have shown that hepatic insulin resistance may be caused by elevated levels of tumor necrosis factor-alpha, which disturbs the tyrosine phosphorylation of insulin receptor substrate-1. 6 Chronic hepatitis C virus (HCV) infection itself also increases the risk of HCC. It leads to chronic inflammation of the liver, to liver fibrosis, and it may eventually progress to cirrhosis. For patients with hepatitis C cirrhosis the risk for development of HCC is 0.54 to 2.0% per year. 7,8 Abbreviations: anti-HBc, anti-hepatitis B core antigen; BMI, body mass index; CI, confidence interval; HCC, hepatocellular carcinoma; HCV, hepatitis C virus; HR, hazard ratio; IQR, interquartile range; NASH, nonalcoholic steatohepatitis; OR, odds ratio. From the 1 Erasmus MC University Medical Center, Department of Gastroenterology and Hepatology, Rotterdam, the Netherlands; 2 Toronto Western Hospital, University Health Network, Toronto, Ontario, Canada; 3 Department of Gastroenterology, Hepatology, and Endocrinology, Medical School Hannover, Hannover, Germany;4 Research and Development (ZonMw) (to B.J.V.).[Stefan Zeuzem is currently affiliated with Johann Wolfgang

Great efforts have been taken for developing high-sensitive mass spectrometry (MS)-based proteomic technologies, among which sample preparation is one of the major focus. Here, a simple and integrated spintip-based proteomics technology (SISPROT) consisting of strong cation exchange beads and C18 disk in one pipet tip was developed. Both proteomics sample preparation steps, including protein preconcentration, reduction, alkylation, and digestion, and reversed phase (RP)-based desalting and high-pH RP-based peptide fractionation can be achieved in a fully integrated manner for the first time. This easy-to-use technology achieved high sensitivity with negligible sample loss. Proteomic analysis of 2000 HEK 293 cells readily identified 1270 proteins within 1.4 h of MS time, while 7826 proteins were identified when 100000 cells were processed and analyzed within only 22 h of MS time. More importantly, the SISPROT can be easily multiplexed on a standard centrifuge with good reproducibility (Pearson correlation coefficient > 0.98) for both single-shot analysis and deep proteome profiling with five-step high-pH RP fractionation. The SISPROT was exemplified by the triplicate analysis of 100000 stem cells from human exfoliated deciduous teeth (SHED). This led to the identification of 9078 proteins containing 3771 annotated membrane proteins, which was the largest proteome data set for dental stem cells reported to date. We expect that the SISPROT will be well suited for deep proteome profiling for fewer than 100000 cells and applied for translational studies where multiplexed technology with good label-free quantification precision is required.

Host-microbiome interactions have been shown to play important roles in human health and diseases. Most of the current studies of the microbiome have been performed by genomic approaches through next-generation sequencing. Technologies, such as metaproteomics, for functional analysis of the microbiome are needed to better understand the intricate host-microbiome interactions. However, significant efforts to improve the depth and resolution of gut metaproteomics are still required. In this study, we combined an efficient sample preparation technique, high resolution mass spectrometry, and metaproteomic bioinformatics tools to perform ultradeep metaproteomic analysis of human gut microbiome from stool. We reported the deepest analysis of the microbiome to date with an average of 20 558 protein groups identified per sample analysis. Moreover, strain resolution taxonomic and pathway analysis using deep metaproteomics revealed strain level variations, in particular for Faecalibacterium prausnitzii, in the microbiome from the different individuals. We also reported that the human proteins identified in stool samples are functionally enriched in extracellular region pathways and in particular those proteins involved in defense response against microbial organisms. Deep metaproteomics is a promising approach to perform in-depth microbiome analysis and simultaneously reveals both human and microbial changes that are not readily apparent using the standard genomic approaches.

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Signaling complexes are often organized in a spatiotemporal manner and on a minute timescale. Proximity labeling based on engineered ascorbate peroxidase APEX2 pioneered in situ capture of spatiotemporal membrane protein complexes in living cells, but its application to cytosolic proteins remains limited due to the high labeling background. Here, we develop proximity labeling probes with increased labeling selectivity. These probes, in combination with label-free quantitative proteomics, allow exploring cytosolic protein assemblies such as phosphotyrosine-mediated protein complexes formed in response to minute-scale EGF stimulation. As proof-of-concept, we systematically profile the spatiotemporal interactome of the EGFR signaling component STS1. For STS1 core complexes, our proximity proteomics approach shows comparable performance to affinity purification-mass spectrometry-based temporal interactome profiling, while also capturing additional—especially endosomally-located—protein complexes. In summary, we provide a generic approach for exploring the interactome of mobile cytosolic proteins in living cells at a temporal resolution of minutes.