Summary Background Acute severe ulcerative colitis (ASUC) is a life‐threatening condition. Mortality in ASUC decreased in published series but there is uncertainty as to whether this also applies to the real‐life setting. Aim To perform a systematic review and meta‐analysis of mortality in ASUC in studies from referral centres and in population‐based studies, separately and combined. A second aim was to identify risk factors of mortality in ASUC. Methods We searched pubmed and embase from 1998 to 2016, to identify studies that reported 3‐month or 12‐month mortalities of acute UC in adult patients treated in referral centres, and in population‐based studies. Results Six population‐based studies with 741 743 patients and 47 referral centre‐based studies with 2556 patients were included. The pooled 3‐month and 12‐month mortalities were respectively 0.84% and 1.01%. Advanced age was significantly associated with both 3 month and 12 month mortalities (OR = 1.15 per year, 95% CI: 1.10‐1.20 and OR = 1.19 per year, 95% CI: 1.15‐1.23 respectively). The pooled 3‐month and 12‐month mortalities were 0.78% and 0.85% in studies with median age of less than 50 and 2.81% and 4.17% in studies with median age of 50 or more, respectively. After adjustment for age, 3‐month and 12‐month mortalities did not differ between population‐based and referral centre‐based studies. Conclusions Mortality in acute severe ulcerative colitis is approximately 1%; it is higher in older patients. Efforts should be made to improve the care of elderly patients with severe UC.

We applied deep learning to create an algorithm for breathing phase detection in lung sound recordings, and we compared the breathing phases detected by the algorithm and manually annotated by two experienced lung sound researchers. Our algorithm uses a convolutional neural network with spectrograms as the features, removing the need to specify features explicitly. We trained and evaluated the algorithm using three subsets that are larger than previously seen in the literature. We evaluated the performance of the method using two methods. First, discrete count of agreed breathing phases (using 50% overlap between a pair of boxes), shows a mean agreement with lung sound experts of 97% for inspiration and 87% for expiration. Second, the fraction of time of agreement (in seconds) gives higher pseudo-kappa values for inspiration (0.73–0.88) than expiration (0.63–0.84), showing an average sensitivity of 97% and an average specificity of 84%. With both evaluation methods, the agreement between the annotators and the algorithm shows human level performance for the algorithm. The developed algorithm is valid for detecting breathing phases in lung sound recordings.

Transcriptome measurements and other -omics type data are increasingly more used in epidemiological studies. Most of omics studies to date are small with samples sizes in the tens, or sometimes low hundreds, but this is changing. Our Norwegian Woman and Cancer (NOWAC) datasets are to date one or two orders of magnitude larger. The NOWAC biobank contains about 50000 blood samples from a prospective study. Around 125 breast cancer cases occur in this cohort each year. The large biological variation in gene expression means that many observations are needed to draw scientific conclusions. This is true for both microarray and RNA-seq type data. Hence, larger datasets are likely to become more common soon.Technical outliers are observations that somehow were distorted at the lab or during sampling. If not removed these observations add bias and variance in later statistical analyses, and may skew the results. Hence, quality assessment and data cleaning are important. We find common quality assessment libraries difficult to work with for large datasets for two reasons: slow execution speed and unsuitable visualizations.In this paper, we present our standard operating procedure (SOP) for large-sample transcriptomics datasets. Our SOP combines automatic outlier detection with manual evaluation to avoid removing valuable observations. We use laboratory quality measures and statistical measures of deviation to aid the analyst. These are available in the nowaclean R package, currently available on GitHub (https://github.com/3inar/nowaclean). Finally, we evaluate our SOP on one of our larger datasets with 832 observations.

Breast cancer patients with metastatic disease have a higher incidence of deaths from breast cancer than patients with early-stage cancers. Recent findings suggest that there are differences in immune cell function between metastatic and non-metastatic cases, even years before diagnosis. We have analyzed whole blood gene expression by Illumina bead chips in blood samples taken using the PAXgene blood collection system up to two years before diagnosis. The final study sample included 197 breast cancer cases and 197 age-matched controls. We defined a causal directed acyclic graph to guide a Bayesian data analysis to estimate the risk of metastasis associated with the expression of all genes and with relevant sets of genes. We ranked genes and gene sets according to the sign probability for excess risk. Among the screening detected cancers, 82% were without metastasis, compared to 53% of between-screening detected cancers. Among the highest ranking genes and gene sets associated with metastasis risk, we identified plasmacytiod dentritic cell function, the SLC22 family of transporters, and glutamine metabolism as potential links between the immune system and metastasis. We conclude that there may be potentially wide-reaching differences in blood gene expression profiles between metastatic and non-metastatic breast cancer cases up to two years before diagnosis, which warrants future study.