Metabonomic fingerprints of fasting plasma and spot urine reveal human pre-diabetic metabolic traits

Impaired glucose tolerance (IGT) which precedes overt type 2 diabetes (T2DM) for decades is associated with multiple metabolic alterations in insulin sensitive tissues. In an UPLC-qTOF-mass spectrometry-driven non-targeted metabonomics approach we investigated plasma as well as spot urine of 51 non-diabetic, overnight fasted individuals aiming to separate subjects with IGT from controls thereby identify pathways affected by the pre-diabetic metabolic state. We could clearly demonstrate that normal glucose tolerant (NGT) and IGT subjects clustered in two distinct groups independent of the investigated metabonome. These findings reflect considerable differences in individual metabolite fingerprints, both in plasma and urine. Pre-diabetes associated alterations in fatty acid-, tryptophan-, uric acid-, bile acid-, and lysophosphatidylcholine-metabolism, as well as the TCA cycle were identified. Of note, individuals with IGT also showed decreased levels of gut flora-associated metabolites namely hippuric acid, methylxanthine, methyluric acid, and 3-hydroxyhippuric acid. The findings of our non-targeted UPLC-qTOF-MS metabonomics analysis in plasma and spot urine of individuals with IGT vs NGT offers novel insights into the metabolic alterations occurring in the long, asymptomatic period preceding the manifestation of T2DM thereby giving prospects for new intervention targets

Addition of ultrasound to mammography in the case of dense breast tissue: systematic review and meta-analysis.

BACKGROUND: Mammography is less effective in detecting cancer in dense than in fatty breasts. METHODS: We undertook a systematic search in PubMed to identify studies on women with dense breasts who underwent screening with mammography supplemented with ultrasound. A meta-analysis was undertaken on the proportion of cancers detected only by ultrasound, out of all screen-detected cancers, and the proportion of women with negative mammography who were referred for assessment following ultrasound screening. RESULTS: Twenty-nine studies satisfied our inclusion criteria. The proportion of total cancers detected only by ultrasound was 0.29 (95% CI: 0.27-0.31), consistent with an approximately 40% increase in the detection of cancers compared to mammography. In the studied populations, this translated into an additional 3.8 (95% CI: 3.4-4.2) screen-detected cases per 1000 mammography-negative women. About 13% (32/248) of cancers were in situ from 17 studies with information on this subgroup. Ultrasound approximately doubled the referral for assessment in three studies with these data. CONCLUSIONS: Studies have consistently shown an increased detection of breast cancer by supplementary ultrasound screening. An inclusion of supplementary ultrasound into routine screening will need to consider the availability of ultrasound and diagnostic assessment capacities.Department of Health Policy Research Programme (106/0001). Cancer Research UK (grants C8162/A16892 and C569/A16891)

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