Trampoline related injuries in children: risk factors and radiographic findings

Background: Backyard trampolines are immensely popular among children, but are associated with an increase of trampoline-related injuries. The aim of this study was to evaluate radiographs of children with trampoline related injuries and to determine the risk factors. Methods: Between 2003 and 2009, 286 children under the age of 16 with backyard trampoline injuries were included in the study. The number of injuries increased from 13 patients in 2003 to 86 in 2009. The median age of the 286 patients was 7 years (range: 1-15 years). Totally 140 (49%) patients were males, and 146 (51%) females. Medical records and all available diagnostic imaging were reviewed. A questionnaire was sent to the parents to evaluate the circumstances of each injury, the type of trampoline, the protection equipment and the experience of the children using the trampoline. The study was approved by the Institutional Ethics Committee of the University Hospital of Bern. Results: The questionnaires and radiographs of the 104 patients were available for evaluation. A fracture was sustained in 51 of the 104 patients. More than 75% of all patients sustaining injuries and in 90% of patients with fractures were jumping on the trampoline with other children at the time of the accident. The most common fractures were supracondylar humeral fractures (29%) and forearm fractures (25%). Fractures of the proximal tibia occurred especially in younger children between 2-5 years of age. Conclusions: Children younger than 5 years old are at risk for specific proximal tibia fractures ("Trampoline Fracture”). A child jumping simultaneously with other children has a higher risk of suffering from a fractur

Atomic mutagenesis of stop codon nucleotides reveals the chemical prerequisites for release factor-mediated peptide release.

Termination of protein synthesis is triggered by the recognition of a stop codon at the ribosomal A site and is mediated by class I release factors (RFs). Whereas in bacteria, RF1 and RF2 promote termination at UAA/UAG and UAA/UGA stop codons, respectively, eukaryotes only depend on one RF (eRF1) to initiate peptide release at all three stop codons. Based on several structural as well as biochemical studies, interactions between mRNA, tRNA, and rRNA have been proposed to be required for stop codon recognition. In this study, the influence of these interactions was investigated by using chemically modified stop codons. Single functional groups within stop codon nucleotides were substituted to weaken or completely eliminate specific interactions between the respective mRNA and RFs. Our findings provide detailed insight into the recognition mode of bacterial and eukaryotic RFs, thereby revealing the chemical groups of nucleotides that define the identity of stop codons and provide the means to discriminate against noncognate stop codons or UGG sense codons

Integrated Placental Modelling of Histology with Gene Expression to Identify Functional Impact on Fetal Growth

Fetal growth restriction (FGR) is a leading cause of perinatal morbidity and mortality. Altered placental formation and functional capacity are major contributors to FGR pathogenesis. Relating placental structure to function across the placenta in healthy and FGR pregnancies remains largely unexplored but could improve understanding of placental diseases. We investigated integration of these parameters spatially in the term human placenta using predictive modelling. Systematic sampling was able to overcome heterogeneity in placental morphological and molecular features. Defects in villous development, elevated fibrosis, and reduced expression of growth and functional marker genes (IGF2, VEGA, SLC38A1, and SLC2A3) were seen in age-matched term FGR versus healthy control placentas. Characteristic histopathological changes with specific accompanying molecular signatures could be integrated through computational modelling to predict if the placenta came from a healthy or FGR pregnancy. Our findings yield new insights into the spatial relationship between placental structure and function and the etiology of FGR

A Survey of Attitudes towards the Clinical Application of Systemic Inflammation Based Prognostic Scores in Cancer

Introduction. The systemic inflammatory response (SIR) plays a key role in determining nutritional status and survival of patients with cancer. A number of objective scoring systems have been shown to have prognostic value; however, their application in routine clinical practice is not clear. The aim of the present survey was to examine the range of opinions internationally on the routine use of these scoring systems. Methods. An online survey was distributed to a target group consisting of individuals worldwide who have reported an interest in systemic inflammation in patients with cancer. Results. Of those invited by the survey ( = 238), 65% routinely measured the SIR, mainly for research and prognostication purposes and clinically for allocation of adjuvant therapy or palliative chemotherapy. 40% reported that they currently used the Glasgow Prognostic Score/modified Glasgow Prognostic Score (GPS/mGPS) and 81% reported that a measure of systemic inflammation should be incorporated into clinical guidelines, such as the definition of cachexia. Conclusions. The majority of respondents routinely measured the SIR in patients with cancer, mainly using the GPS/mGPS for research and prognostication purposes. The majority reported that a measure of the SIR should be adopted into clinical guidelines

Future climate change and its impact on runoff generation from the debris-covered Inylchek glaciers, central Tian Shan, Kyrgyzstan

The heavily debris-covered Inylchek glaciers in the central Tian Shan are the largest glacier system in the Tarim catchment. It is assumed that almost 50% of the discharge of Tarim River are provided by glaciers. For this reason, climatic changes, and thus changes in glacier mass balance and glacier discharge are of high impact for the whole region. In this study, a conceptual hydrological model able to incorporate discharge from debris-covered glacier areas is presented. To simulate glacier melt and subsequent runoff in the past (1970/1971-1999/2000) and future (2070/2071-2099/2100), meteorological input data were generated based on ECHAM5/MPI-OM1 global climate model projections. The hydrological model HBV-LMU was calibrated by an automatic calibration algorithm using runoff and snow cover information as objective functions. Manual fine-tuning was performed to avoid unrealistic results for glacier mass balance. The simulations show that annual runoff sums will increase significantly under future climate conditions. A sensitivity analysis revealed that total runoff does not decrease until the glacier area is reduced by 43%. Ice melt is the major runoff source in the recent past, and its contribution will even increase in the coming decades. Seasonal changes reveal a trend towards enhanced melt in spring, but a change from a glacial-nival to a nival-pluvial runoff regime will not be reached until the end of this century.Publisher PDFPeer reviewe

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear un derstanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5–7 vast areas of the tropics remain understudied.8–11 In the American tropics, Amazonia stands out as the world’s most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepre sented in biodiversity databases.13–15 To worsen this situation, human-induced modifications16,17 may elim inate pieces of the Amazon’s biodiversity puzzle before we can use them to understand how ecological com munities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple or ganism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region’s vulnerability to environmental change. 15%–18% of the most ne glected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lostinfo:eu-repo/semantics/publishedVersio