Diagnosis of pericardial cysts using diffusion weighted magnetic resonance imaging: A case series

<p>Abstract</p> <p>Introduction</p> <p>Congenital pericardial cysts are benign lesions that arise from the pericardium during embryonic development. The diagnosis is based on typical imaging features, but atypical locations and signal magnetic resonance imaging sequences make it difficult to exclude other lesions. Diffusion-weighted magnetic resonance imaging is a novel method that can be used to differentiate tissues based on their restriction to proton diffusion. Its use in differentiating pericardial cysts from other pericardial lesions has not yet been described.</p> <p>Case presentation</p> <p>We present three cases (a 51-year-old Caucasian woman, a 66-year-old Caucasian woman and a 77-year-old Caucasian woman) with pericardial cysts evaluated with diffusion-weighted imaging using cardiac magnetic resonance imaging. Each lesion demonstrated a high apparent diffusion coefficient similar to that of free water.</p> <p>Conclusion</p> <p>This case series is the first attempt to investigate the utility of diffusion-weighted magnetic resonance imaging in the assessment of pericardial cysts. Diffusion-weighted imaging may be a useful noninvasive diagnostic tool for pericardial cysts when conventional imaging findings are inconclusive.</p

Stable isotope dilution assay for the accurate determination of mycotoxins in maize by UHPLC-MS/MS

A fast, easy-to-handle and cost-effective analytical method for 11 mycotoxins currently regulated in maize and other cereal-based food products in Europe was developed and validated for maize. The method is based on two extraction steps using different acidified acetonitrile–water mixtures. Separation is achieved using ultrahigh-performance liquid chromatography (UHPLC) by a linear water–methanol gradient. After electrospray ionisation, tandem mass spectrometric detection is performed in dynamic multiple reaction monitoring mode. Since accurate mass spectrometric quantification is hampered by matrix effects, uniformly [13C]-labelled mycotoxins for each of the 11 compounds were added to the sample extracts prior to UHPLC-MS/MS analysis. Method performance parameters were obtained by spiking blank maize samples with mycotoxins before as well as after extraction on six levels in triplicates. The twofold extraction led to total recoveries of the extraction steps between 97% and 111% for all target analytes, including fumonisins. The [13C]-labelled internal standards efficiently compensated all matrix effects in electrospray ionisation, leading to apparent recoveries between 88% and 105% with reasonable additional costs. The relative standard deviations of the whole method were between 4% and 11% for all analytes. The trueness of the method was verified by the measurement of several maize test materials with well-characterized concentrations. In conclusion, the developed method is capable of determining all regulated mycotoxins in maize and presuming similar matrix effects and extraction recovery also in other cereal-based foods

The REASON score: an epigenetic and clinicopathologic score to predict risk of poor survival in patients with early stage oral squamous cell carcinoma

Background Oral squamous cell carcinoma (OSCC) is a capricious cancer with poor survival rates, even for early-stage patients. There is a pressing need to develop more precise risk assessment methods to appropriately tailor clinical treatment. Genome-wide association studies have not produced a viable biomarker. However, these studies are limited by using heterogeneous cohorts, not focusing on methylation although OSCC is a heavily epigenetically-regulated cancer, and not combining molecular data with clinicopathologic data for risk prediction. In this study we focused on early-stage (I/II) OSCC and created a risk score called the REASON score, which combines clinicopathologic characteristics with a 12-gene methylation signature, to predict the risk of 5-year mortality. Methods We combined data from an internal cohort (n = 515) and The Cancer Genome Atlas (TCGA) cohort (n = 58). We collected clinicopathologic data from both cohorts to derive the non-molecular portion of the REASON score. We then analyzed the TCGA cohort DNA methylation data to derive the molecular portion of the risk score. Results 5-year disease specific survival was 63% for the internal cohort and 86% for the TCGA cohort. The clinicopathologic features with the highest predictive ability among the two the cohorts were age, race, sex, tobacco use, alcohol use, histologic grade, stage, perineural invasion (PNI), lymphovascular invasion (LVI), and margin status. This panel of 10 non-molecular features predicted 5-year mortality risk with a concordance (c)-index = 0.67. Our molecular panel consisted of a 12-gene methylation signature (i.e., HORMAD2, MYLK, GPR133, SOX8, TRPA1, ABCA2, HGFAC, MCPH1, WDR86, CACNA1H, RNF216, CCNJL), which had the most significant differential methylation between patients who survived vs. died by 5 years. All 12 genes have already been linked to survival in other cancers. Of the genes, only SOX8 was previously associated with OSCC; our study was the first to link the remaining 11 genes to OSCC survival. The combined molecular and non-molecular panel formed the REASON score, which predicted risk of death with a c-index = 0.915. Conclusions The REASON score is a promising biomarker to predict risk of mortality in early-stage OSCC patients. Validation of the REASON score in a larger independent cohort is warranted

Large expert-curated database for benchmarking document similarity detection in biomedical literature search

Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency-Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical research.Peer reviewe