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Abstract The volume of epicardial adipose tissue (EATV) is increased in type-2 diabetes (T2D), while its attenuation (EATA) appears to be decreased. Similar patterns have been suggested in pre-diabetes, but data is scarce. In both pre-diabetes and T2D, any independent role of EATV and EATA in disease development remains to be proven, a task complicated by their substantial co-variation with other anthropometrics, e.g. BMI, waist circumference, and abdominal visceral adipose tissue (VAT). EATV and EATA was quantified in computed tomography (CT) images in a population study (n = 1948) using an automatic technique. Data was available on BMI, waist circumference, abdominal visceral adipose tissue (VAT) area, insulin resistance (IR) and glucose tolerance, the latter ranging from normal (NGT), over pre-diabetes (impaired fasting glucose [IFG, n = 414] impaired glucose tolerance [IGT, n = 321] and their combination [CGI, n = 128]), to T2D. EATV was increased in pre-diabetes, T2D and IR in univariable analyses and when adjusting for BMI, however not when adjusting for waist or VAT. EATA was reduced in pre-diabetes, T2D and IR in univariable analyses and when adjusting for BMI and waist, however not when adjusting for VAT. Adjustment for other co-variates had little influence on the results. In conclusion, EATV is increased and EATA reduced in pre-diabetes, T2D and IR, however, significant co-variation with other anthropometrics, especially VAT, obscures their function in disease development. The current results do not exclude a pathophysiological role of epicardial fat, but future studies need to adjust for anthropometrics, or focus on the microenvironment within the pericardial sac.

Abstract To develop a fully automatic model capable of reliably quantifying epicardial adipose tissue (EAT) volumes and attenuation in large scale population studies to investigate their relation to markers of cardiometabolic risk. Non-contrast cardiac CT images from the SCAPIS study were used to train and test a convolutional neural network based model to quantify EAT by: segmenting the pericardium, suppressing noise-induced artifacts in the heart chambers, and, if image sets were incomplete, imputing missing EAT volumes. The model achieved a mean Dice coefficient of 0.90 when tested against expert manual segmentations on 25 image sets. Tested on 1400 image sets, the model successfully segmented 99.4% of the cases. Automatic imputation of missing EAT volumes had an error of less than 3.1% with up to 20% of the slices in image sets missing. The most important predictors of EAT volumes were weight and waist, while EAT attenuation was predicted mainly by EAT volume. A model with excellent performance, capable of fully automatic handling of the most common challenges in large scale EAT quantification has been developed. In studies of the importance of EAT in disease development, the strong co-variation with anthropometric measures needs to be carefully considered.

Although numbers of older people are increasing, nursing students have negative attitudes towards older people and do not plan to care for them following graduation. Multiple strategies have been implemented to reverse students' attitudes with mixed results. The purpose of this pilot quasi-experimental study was to test a Creative-Bonding Intervention (CBI) with students implementing art activities with older people to promote students' willingness to take care of them. Using a self-transcendence conceptual framework, control (𝑛=56) and experimental (𝑛=14) student groups were pre- and post-tested on attitudes toward older people, self-transcendence, and willingness to serve. The CBI improved attitudes towards older people with negative attitudes significantly changed (𝑃=.008) but with no significant differences on self-transcendence and willingness to serve. However, willingness to serve results approached significance (𝑃=.08). The willingness measure (one question) should be expanded. Curricula changes that incorporate creative activities such as the CBI with larger and equal numbers in student groups and longitudinal follow up to determine long-term results after graduation are suggested.

The Drosophila transcription factor Tramtrack (Ttk) is involved in a wide range of developmental decisions, ranging from early embryonic patterning to differentiation processes in organogenesis. Given the wide spectrum of functions and pleiotropic effects that hinder a comprehensive characterisation, many of the tissue specific functions of this transcription factor are only poorly understood. We recently discovered multiple roles of Ttk in the development of the tracheal system on the morphogenetic level. Here, we sought to identify some of the underlying genetic components that are responsible for the tracheal phenotypes of Ttk mutants. We therefore profiled gene expression changes after Ttk loss- and gain-of-function in whole embryos and cell populations enriched for tracheal cells. The analysis of the transcriptomes revealed widespread changes in gene expression. Interestingly, one of the most prominent gene classes that showed significant opposing responses to loss- and gain-of-function was annotated with functions in chitin metabolism, along with additional genes that are linked to cellular responses, which are impaired in ttk mutants. The expression changes of these genes were validated by quantitative real-time PCR and further functional analysis of these candidate genes and other genes also expected to control tracheal tube size revealed at least a partial explanation of Ttk's role in tube size regulation. The computational analysis of our tissue-specific gene expression data highlighted the sensitivity of the approach and revealed an interesting set of novel putatively tracheal genes.