BackgroundVariability in endoscopic assessment necessitates rigorous investigation of descriptors for scoring severity of ulcerative colitis (UC).ObjectiveTo evaluate variation in the overall endoscopic assessment of severity, the intra- and interindividual variation of descriptive terms and to create an Ulcerative Colitis Endoscopic Index of Severity which could be validated.DesignA two-phase study used a library of 670 video sigmoidoscopies from patients with Mayo Clinic scores 0–11, supplemented by 10 videos from five people without UC and five hospitalised patients with acute severe UC. In phase 1, each of 10 investigators viewed 16/24 videos to assess agreement on the Baron score with a central reader and agreed definitions of 10 endoscopic descriptors. In phase 2, each of 30 different investigators rated 25/60 different videos for the descriptors and assessed overall severity on a 0–100 visual analogue scale. κ Statistics tested inter- and intraobserver variability for each descriptor. A general linear mixed regression model based on logit link and β distribution of variance was used to predict overall endoscopic severity from descriptors.ResultsThere was 76% agreement for ‘severe’, but 27% agreement for ‘normal’ appearances between phase I investigators and the central reader. In phase 2, weighted κ values ranged from 0.34 to 0.65 and 0.30 to 0.45 within and between observers for the 10 descriptors. The final model incorporated vascular pattern, (normal/patchy/complete obliteration) bleeding (none/mucosal/luminal mild/luminal moderate or severe), erosions and ulcers (none/erosions/superficial/deep), each with precise definitions, which explained 90% of the variance (pR2, Akaike Information Criterion) in the overall assessment of endoscopic severity, predictions varying from 4 to 93 on a 100-point scale (from normal to worst endoscopic severity).ConclusionThe Ulcerative Colitis Endoscopic Index of Severity accurately predicts overall assessment of endoscopic severity of UC. Validity and responsiveness need further testing before it can be applied as an outcome measure in clinical trials or clinical practice.

The UCEIS and its components show satisfactory intrainvestigator and interinvestigator reliability. Among investigators, the UCEIS accounted for a median of 86% of the variability in evaluation of overall severity on the VAS when assessing the endoscopic severity of UC and was unaffected by knowledge of clinical details.

This position paper summarizes relevant theory and current practice regarding the analysis of longitudinal clinical trials intended to support regulatory approval of medicinal products, and it reviews published research regarding methods for handling missing data. It is one strand of the PhRMA initiative to improve efficiency of late-stage clinical research and gives recommendations from a cross-industry team. We concentrate specifically on continuous response measures analyzed using a linear model, when the goal is to estimate and test treatment differences at a given time point. Traditionally, the primary analysis of such trials handled missing data by simple imputation using the last, or baseline, observation cam'ed forward method (LOCF, B O G ) followed by analysis of (co)variance at the chosen time point. However, thegeneral statistical and scientific community has moved away from these simple methods in favor of joint analysis of data from all time points based on a multivariate model (eg. of a mixed-effects type). One such newer method, a likelihood-based mixedefiects model repeated measures (MMRM) approach, has received considerable attention in the clinical trials literature. We discuss specific concerns raised by regulatory agencies with regard to MMRM and review published evidence comparing LOCF and MMRM in terms of validi9, bias, power, and type I error. Our main conclusion is that the mixed model approach is more eficient and reliable as a method of primary analysis, and should be preferred to the inherently biased and statistically invalid simple imputation approaches. We also summarize other methods of handling missing data that are useful as sensitivity analyses for assessing the potential effect of data missing not at random.

BackgroundEven though human sweat is odorless, bacterial growth and decomposition of specific odor precursors in it is believed to give rise to body odor in humans. While mechanisms of odor generation have been widely studied in adults, little is known for teenagers and pre-pubescent children who have distinct sweat composition from immature apocrine and sebaceous glands, but are arguably more susceptible to the social and psychological impact of malodor.ResultsWe integrated information from whole microbiome analysis of multiple skin sites (underarm, neck, and head) and multiple time points (1 h and 8 h after bath), analyzing 180 samples in total to perform the largest metagenome-wide association study to date on malodor. Significant positive correlations were observed between odor intensity and the relative abundance of Staphylococcus hominis, Staphylococcus epidermidis, and Cutibacterium avidum, as well as negative correlation with Acinetobacter schindleri and Cutibacterium species. Metabolic pathway analysis highlighted the association of isovaleric and acetic acid production (sour odor) from enriched S. epidermidis (teen underarm) and S. hominis (child neck) enzymes and sulfur production from Staphylococcus species (teen underarm) with odor intensity, in good agreement with observed odor characteristics in pre-pubescent children and teenagers. Experiments with cultures on human and artificial sweat confirmed the ability of S. hominis and S. epidermidis to independently produce malodor with distinct odor characteristics.ConclusionsThese results showcase the power of skin metagenomics to study host-microbial co-metabolic interactions, identifying distinct pathways for odor generation from sweat in pre-pubescent children and teenagers and highlighting key enzymatic targets for intervention.Electronic supplementary materialThe online version of this article (10.1186/s40168-018-0588-z) contains supplementary material, which is available to authorized users.

Introduction At present, there are no well-accepted reference ranges for serum testosterone concentrations in women. Aim The aim of this study was to determine the reference ranges for serum testosterone and sex hormone-binding globulin (SHBG) in premenopausal women with normal menstrual cycles. Methods We measured serum total, free, and bioavailable testosterone and SHBG concentrations in 161 healthy, normally cycling women (18–49 years). Morning blood samples were collected during follicular, mid-cycle, and luteal phases of the menstrual cycle and analyzed using validated methods. Mean, median, and weighted average hormone levels across menstrual cycle phases as well as percentiles for a typical 30-year-old woman were determined. Main Outcome Measures Age-related serum levels of total, free, and bioavailable testosterone and SHBG levels in normally cycling premenopausal women. Results Serum testosterone concentrations exhibited an age-related decline, whereas SHBG remained relatively stable across studied age ranges. Reference ranges for total, free, and bioavailable testosterone and SHBG were established using 5th and 95th percentiles. The estimated 5th and 95th percentiles for a 30-year-old woman were: testosterone, 15–46 ng/dL (520–1595 pmol/L); free testosterone, 1.2–6.4 pg/mL (4.16–22.2 pmol/L); calculated free testosterone, 1.3–5.6 pg/mL (4.5–19.4 pmol/L); bioavailable testosterone, 1.12–7.62 ng/dL (38.8–264.21 pmol/L); and SHBG 18–86 nmol/L. The variations of hormones and SHBG across menstrual cycle were consistent with previous literature. Conclusions Reference ranges for free, total, and bioavailable testosterone and SHBG were established in premenopausal women using validated immunoassays and an adequate number of subjects consistent with recommendations by the National Committee for Clinical Laboratory Standards. The increase in testosterone in the mid-cycle period is relatively small compared with the overall variability, so these reference ranges can be applied irrespective of the day in the menstrual cycle the sample has been taken.