New genetic loci link adipose and insulin biology to body fat distribution.

Body fat distribution is a heritable trait and a well-established predictor of adverse metabolic outcomes, independent of overall adiposity. To increase our understanding of the genetic basis of body fat distribution and its molecular links to cardiometabolic traits, here we conduct genome-wide association meta-analyses of traits related to waist and hip circumferences in up to 224,459 individuals. We identify 49 loci (33 new) associated with waist-to-hip ratio adjusted for body mass index (BMI), and an additional 19 loci newly associated with related waist and hip circumference measures (P < 5 × 10(-8)). In total, 20 of the 49 waist-to-hip ratio adjusted for BMI loci show significant sexual dimorphism, 19 of which display a stronger effect in women. The identified loci were enriched for genes expressed in adipose tissue and for putative regulatory elements in adipocytes. Pathway analyses implicated adipogenesis, angiogenesis, transcriptional regulation and insulin resistance as processes affecting fat distribution, providing insight into potential pathophysiological mechanisms

Spatially Offset Raman Spectroscopy (SORS) as analytical tool for the sustainable analysis of packed meat

As a highly sensitive food, the safety of meat is an important issue in the context of food safety. The current analytical methods for detection are based on different, invasive proce-dures. The analysis of these foods therefore causes avoidable food losses in the case of foods that are still suitable for consumption. Spatially offset Raman spectroscopy (SORS) is a po-tential alternative analytical method, as it offers the possibility to measure through packag-ing. Using packaged chicken breast as an example, we simulated two different storage condi-tions and compared these samples to control samples using SORS. To validate the group as-signment and to monitor the resulting changes, total bacterial count and NMR spectra of the meat extracts were measured and evaluated. We overcame the various difficulties in the multivariate data evaluation of the through package measured Raman spectra and were able to classify samples deviating from the control group with very high accuracies and simulta-neous absence of false positive results

Family involvement in psychotherapy for depression in children and adolescents: Systematic review and meta‐analysis

Abstract Purpose Depressive disorders in children and adolescents have an enormous impact on their general quality of life. There is a clear need to effectively treat depression in this age group. Effects of psychotherapy can be enhanced by involving caregivers. In our systematic review and meta‐analysis, we examine for the first time the effects of caregiver involvement in depression‐specific interventions for children and adolescents. Methods We included randomized controlled trials examining the effects of interventions for children and adolescents with depression involving their caregivers or families compared to interventions without including caregivers. Primary outcome was the severity of childhood and adolescent depression. Results Overall, 19 randomized controlled trials could be included (N = 1553) that were highly heterogeneous regarding outcome measures or the extent of caregiver integration. We were able to include k = 17 studies in our meta‐analysis and find a small but significant effect for family‐involved interventions against active control conditions without family‐involvement at post intervention (α = 0.05, d = 0.34; [0.07; 0.60]; p = .01). Conclusions We detected an overall significant but small effect of family/caregivers’ involvement compared to control groups without it. Structured, guideline‐based research is urgently needed to identify for which children/adolescents with depression, under what circumstances, and in what form the family should be effectively involved in their psychotherapy

Home-Based Measurements of Nocturnal Cardiac Parasympathetic Activity in Athletes during Return to Sport after Sport-Related Concussion

Sport-related concussions (SRC) are characterized by impaired autonomic control. Heart rate variability (HRV) offers easily obtainable diagnostic approaches to SRC-associated dysautonomia, but studies investigating HRV during sleep, a crucial time for post-traumatic cerebral regeneration, are relatively sparse. The aim of this study was to assess nocturnal HRV in athletes during their return to sports (RTS) after SRC in their home environment using wireless wrist sensors (E4, Empatica, Milan, Italy) and to explore possible relations with clinical concussion-associated sleep symptoms. Eighteen SRC athletes wore a wrist sensor obtaining photoplethysmographic data at night during RTS as well as one night after full clinical recovery post RTS (>3 weeks). Nocturnal heart rate and parasympathetic activity of HRV (RMSSD) were calculated and compared using the Mann–Whitney U Test to values of eighteen; matched by sex, age, sport, and expertise, control athletes underwent the identical protocol. During RTS, nocturnal RMSSD of SRC athletes (Mdn = 77.74 ms) showed a trend compared to controls (Mdn = 95.68 ms, p = 0.021, r = −0.382, p adjusted using false discovery rate = 0.126) and positively correlated to “drowsiness” (r = 0.523, p = 0.023, p adjusted = 0.046). Post RTS, no differences in RMSSD between groups were detected. The presented findings in nocturnal cardiac parasympathetic activity during nights of RTS in SRC athletes might be a result of concussion, although its relation to recovery still needs to be elucidated. Utilization of wireless sensors and wearable technologies in home-based settings offer a possibility to obtain helpful objective data in the management of SRC

Using a Further Planning MRI after Neoadjuvant Androgen Deprivation Therapy Significantly Reduces the Radiation Exposure of Organs at Risk in External Beam Radiotherapy of Prostate Cancer

Radiotherapy for prostate cancer is often preceded by neoadjuvant androgen deprivation therapy (ADT), which leads to a reduction in the size of the prostate. This study examines whether it is relevant for treatment planning to acquire a second planning magnetic resonance imaging (MRI) after ADT (=MRI 2) or whether it can be planned without disadvantage based on an MRI acquired before starting ADT (=MRI 1). The imaging data for the radiotherapy treatment planning of 17 patients with prostate cancer who received two planning MRIs (before and after neoadjuvant ADT) were analyzed as follows: detailed comparable radiation plans were created separately, each based on the planning CT scan and either MRI 1 or MRI 2. After ADT for an average of 17.2 weeks, the prostate was reduced in size by an average of 24%. By using MRI 2 for treatment planning, the V60Gy of the rectum could be significantly relieved by an average of 15% with the same coverage of the target volume, and the V70Gy by as much as 33% (compared to using MRI 1 alone). Using a second MRI for treatment planning after neoadjuvant ADT in prostate cancer leads to a significant relief for the organs at risk, especially in the high dose range, with the same irradiation of the target volume, and should therefore be carried out regularly. Waiting for the prostate to shrink after a few months of ADT contributes to relief for the organs at risk and to lowering the toxicity. However, the use of reduced target volumes requires an image-guided application, and the oncological outcome needs to be verified in further studies

Synthetic Image Rendering Solves Annotation Problem in Deep Learning Nanoparticle Segmentation

Nanoparticles occur in various environments as a consequence of man-made processes, which raises concerns about their impact on the environment and human health. To allow for proper risk assessment, a precise and statistically relevant analysis of particle characteristics (such as size, shape, and composition) is required that would greatly benefit from automated image analysis procedures. While deep learning shows impressive results in object detection tasks, its applicability is limited by the amount of representative, experimentally collected and manually annotated training data. Here, an elegant, flexible, and versatile method to bypass this costly and tedious data acquisition process is presented. It shows that using a rendering software allows to generate realistic, synthetic training data to train a state-of-the art deep neural network. Using this approach, a segmentation accuracy can be derived that is comparable to man-made annotations for toxicologically relevant metal-oxide nanoparticle ensembles which were chosen as examples. The presented study paves the way toward the use of deep learning for automated, high-throughput particle detection in a variety of imaging techniques such as in microscopies and spectroscopies, for a wide range of applications, including the detection of micro- and nanoplastic particles in water and tissue samples