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Background:
Endomyocardial biopsy is considered as the gold standard in patients with suspected myocarditis. We aimed to evaluate the impact of bioptic findings on prediction of successful return to work.
Methods:
In 1153 patients (48.9 ± 12.4 years, 66.2% male), who were hospitalized due to symptoms of left heart failure between 2005 and 2012, an endomyocardial biopsy was performed. Routine clinical and laboratory data, sociodemographic parameters, and noninvasive and invasive cardiac variables including endomyocardial biopsy were registered. Data were linked with return to work data from the German statutory pension insurance program and analyzed by Cox regression.
Results:
A total of 220 patients had a complete data set of hospital and insurance information. Three quarters of patients were virus-positive (54.2% parvovirus B19, other or mixed infection 16.7%). Mean invasive left ventricular ejection fraction was 47.1% ± 18.6% (left ventricular ejection fraction <45% in 46.3%). Return to work was achieved after a mean interval of 168.8 ± 347.7 days in 220 patients (after 6, 12, and 24 months in 61.3%, 72.2%, and 76.4%). In multivariate regression analysis, only age (per 10 years, hazard ratio, 1.27; 95% confidence interval, 1.10–1.46; p = 0.001) and left ventricular ejection fraction (per 5% increase, hazard ratio, 1.07; 95% confidence interval, 1.03–1.12; p = 0.002) were associated with increased, elevated work intensity (heavy vs light, congestive heart failure, 0.58; 95% confidence interval, 0.34–0.99; p < 0.049) with decreased probability of return to work. None of the endomyocardial biopsy–derived parameters was significantly associated with return to work in the total group as well as in the subgroup of patients with biopsy-proven myocarditis.
Conclusion:
Added to established predictors, bioptic data demonstrated no additional impact for return to work probability. Thus, socio-medical evaluation of patients with suspected myocarditis furthermore remains an individually oriented process based primarily on clinical and functional parameters.
Background
In health research, indicators of socioeconomic status (SES) are often used interchangeably and often lack theoretical foundation. This makes it difficult to compare results from different studies and to explore the relationship between SES and health outcomes. To aid researchers in choosing appropriate indicators of SES, this article proposes and tests a theory-based selection of SES indicators using chronic back pain as a health outcome.
Methods
Strength of relationship predictions were made using Brunner & Marmot’s model of ‘social determinants of health’. Subsequently, a longitudinal study was conducted with 66 patients receiving in-patient treatment for chronic back pain. Sociodemographic variables, four SES indicators (education, job position, income, multidimensional index) and back pain intensity and disability were obtained at baseline. Both pain dimensions were assessed again 6 months later. Using linear regression, the predictive strength of each SES indicator on pain intensity and disability was estimated and compared to the theory based prediction.
Results
Chronic back pain intensity was best predicted by the multidimensional index (beta = 0.31, p < 0.05), followed by job position (beta = 0.29, p < 0.05) and education (beta = −0.29, p < 0.05); whereas, income exerted no significant influence. Back pain disability was predicted strongest by education (beta = −0.30, p < 0.05) and job position (beta = 0.29, p < 0.05). Here, multidimensional index and income had no significant influence.
Conclusions
The choice of SES indicators influences predictive power on both back pain dimensions, suggesting SES predictors cannot be used interchangeably. Therefore, researchers should carefully consider prior to each study which SES indicator to use. The introduced framework can be valuable in supporting this decision because it allows for a stable prediction of SES indicator influence and their hierarchy on a specific health outcomes.
Background Low back pain (LBP) is a common pain syndrome in athletes, responsible for 28% of missed training days/year. Psychosocial factors contribute to chronic pain development. This study aims to investigate the transferability of psychosocial screening tools developed in the general population to athletes and to define athlete-specific thresholds.
Methods Data from a prospective multicentre study on LBP were collected at baseline and 1-year follow-up (n=52 athletes, n=289 recreational athletes and n=246 non-athletes). Pain was assessed using the Chronic Pain Grade questionnaire. The psychosocial Risk Stratification Index (RSI) was used to obtain prognostic information regarding the risk of chronic LBP (CLBP). Individual psychosocial risk profile was gained with the Risk Prevention Index – Social (RPI-S). Differences between groups were calculated using general linear models and planned contrasts. Discrimination thresholds for athletes were defined with receiver operating characteristics (ROC) curves.
Results Athletes and recreational athletes showed significantly lower psychosocial risk profiles and prognostic risk for CLBP than non-athletes. ROC curves suggested discrimination thresholds for athletes were different compared with non-athletes. Both screenings demonstrated very good sensitivity (RSI=100%; RPI-S: 75%–100%) and specificity (RSI: 76%–93%; RPI-S: 71%–93%). RSI revealed two risk classes for pain intensity (area under the curve (AUC) 0.92(95% CI 0.85 to 1.0)) and pain disability (AUC 0.88(95% CI 0.71 to 1.0)).
Conclusions Both screening tools can be used for athletes. Athlete-specific thresholds will improve physicians’ decision making and allow stratified treatment and prevention.
The general purpose of this systematic review was to summarize, structure and evaluate the findings on automatic evaluations of exercising. Studies were eligible for inclusion if they reported measuring automatic evaluations of exercising with an implicit measure and assessed some kind of exercise variable. Fourteen nonexperimental and six experimental studies (out of a total N = 1,928) were identified and rated by two independent reviewers. The main study characteristics were extracted and the grade of evidence for each study evaluated. First, results revealed a large heterogeneity in the applied measures to assess automatic evaluations of exercising and the exercise variables. Generally, small to large-sized significant relations between automatic evaluations of exercising and exercise variables were identified in the vast majority of studies. The review offers a systematization of the various examined exercise variables and prompts to differentiate more carefully between actually observed exercise behavior (proximal exercise indicator) and associated physiological or psychological variables (distal exercise indicator). Second, a lack of transparent reported reflections on the differing theoretical basis leading to the use of specific implicit measures was observed. Implicit measures should be applied purposefully, taking into consideration the individual advantages or disadvantages of the measures. Third, 12 studies were rated as providing first-grade evidence (lowest grade of evidence), five represent second-grade and three were rated as third-grade evidence. There is a dramatic lack of experimental studies, which are essential for illustrating the cause-effect relation between automatic evaluations of exercising and exercise and investigating under which conditions automatic evaluations of exercising influence behavior. Conclusions about the necessity of exercise interventions targeted at the alteration of automatic evaluations of exercising should therefore not be drawn too hastily.
Regional snow-avalanche detection using object-based image analysis of near-infrared aerial imagery
(2017)
Snow avalanches are destructive mass movements in mountain regions that continue to claim lives and cause infrastructural damage and traffic detours. Given that avalanches often occur in remote and poorly accessible steep terrain, their detection and mapping is extensive and time consuming. Nonetheless, systematic avalanche detection over large areas could help to generate more complete and up-to-date inventories (cadastres) necessary for validating avalanche forecasting and hazard mapping. In this study, we focused on automatically detecting avalanches and classifying them into release zones, tracks, and run-out zones based on 0.25 m near-infrared (NIR) ADS80-SH92 aerial imagery using an object-based image analysis (OBIA) approach. Our algorithm takes into account the brightness, the normalised difference vegetation index (NDVI), the normalised difference water index (NDWI), and its standard deviation (SDNDWI) to distinguish avalanches from other land-surface elements. Using normalised parameters allows applying this method across large areas. We trained the method by analysing the properties of snow avalanches at three 4 km−2 areas near Davos, Switzerland. We compared the results with manually mapped avalanche polygons and obtained a user's accuracy of > 0.9 and a Cohen's kappa of 0.79–0.85. Testing the method for a larger area of 226.3 km−2, we estimated producer's and user's accuracies of 0.61 and 0.78, respectively, with a Cohen's kappa of 0.67. Detected avalanches that overlapped with reference data by > 80 % occurred randomly throughout the testing area, showing that our method avoids overfitting. Our method has potential for large-scale avalanche mapping, although further investigations into other regions are desirable to verify the robustness of our selected thresholds and the transferability of the method.
High Mountain Asia (HMA) - encompassing the Tibetan Plateau and surrounding mountain ranges - is the primary water source for much of Asia, serving more than a billion downstream users. Many catchments receive the majority of their yearly water budget in the form of snow, which is poorly monitored by sparse in situ weather networks. Both the timing and volume of snowmelt play critical roles in downstream water provision, as many applications - such as agriculture, drinking-water generation, and hydropower - rely on consistent and predictable snowmelt runoff. Here, we examine passive microwave data across HMA with five sensors (SSMI, SSMIS, AMSR-E, AMSR2, and GPM) from 1987 to 2016 to track the timing of the snowmelt season - defined here as the time between maximum passive microwave signal separation and snow clearance. We validated our method against climate model surface temperatures, optical remote-sensing snow-cover data, and a manual control dataset (n = 2100, 3 variables at 25 locations over 28 years); our algorithm is generally accurate within 3-5 days. Using the algorithm-generated snowmelt dates, we examine the spatiotemporal patterns of the snowmelt season across HMA. The climatically short (29-year) time series, along with complex interannual snowfall variations, makes determining trends in snowmelt dates at a single point difficult. We instead identify trends in snowmelt timing by using hierarchical clustering of the passive microwave data to determine trends in self-similar regions. We make the following four key observations. (1) The end of the snowmelt season is trending almost universally earlier in HMA (negative trends). Changes in the end of the snowmelt season are generally between 2 and 8 days decade 1 over the 29-year study period (5-25 days total). The length of the snowmelt season is thus shrinking in many, though not all, regions of HMA. Some areas exhibit later peak signal separation (positive trends), but with generally smaller magnitudes than trends in snowmelt end. (2) Areas with long snowmelt periods, such as the Tibetan Plateau, show the strongest compression of the snowmelt season (negative trends). These trends are apparent regardless of the time period over which the regression is performed. (3) While trends averaged over 3 decades indicate generally earlier snowmelt seasons, data from the last 14 years (2002-2016) exhibit positive trends in many regions, such as parts of the Pamir and Kunlun Shan. Due to the short nature of the time series, it is not clear whether this change is a reversal of a long-term trend or simply interannual variability. (4) Some regions with stable or growing glaciers - such as the Karakoram and Kunlun Shan - see slightly later snowmelt seasons and longer snowmelt periods. It is likely that changes in the snowmelt regime of HMA account for some of the observed heterogeneity in glacier response to climate change. While the decadal increases in regional temperature have in general led to earlier and shortened melt seasons, changes in HMA's cryosphere have been spatially and temporally heterogeneous.
High precipitation quantiles tend to rise with temperature, following the so-called Clausius–Clapeyron (CC) scaling. It is often reported that the CC-scaling relation breaks down and even reverts for very high temperatures. In our study, we investigate this reversal using observational climate data from 142 stations across Germany. One of the suggested meteorological explanations for the breakdown is limited moisture supply. Here we argue that, instead, it could simply originate from undersampling. As rainfall frequency generally decreases with higher temperatures, rainfall intensities as dictated by CC scaling are less likely to be recorded than for moderate temperatures. Empirical quantiles are conventionally estimated from order statistics via various forms of plotting position formulas. They have in common that their largest representable return period is given by the sample size. In small samples, high quantiles are underestimated accordingly. The small-sample effect is weaker, or disappears completely, when using parametric quantile estimates from a generalized Pareto distribution (GPD) fitted with L moments. For those, we obtain quantiles of rainfall intensities that continue to rise with temperature.
The all-female Amazon molly (Poecilia formosa) is the result of a hybridization of the Atlantic molly (P. mexicana) and the sailfin molly (P. latipinna) approximately 120,000 years ago. As a gynogenetic species, P. formosa needs to copulate with heterospecific males including males from one of its bisexual ancestral species. However, the sperm only triggers embryogenesis of the diploid eggs. The genetic information of the sperm donor typically will not contribute to the next generation of P. formosa. Hence, P. formosa possesses generally one allele from each of its ancestral species at any genetic locus. This raises the question whether both ancestral alleles are equally expressed in P. formosa. Allele-specific expression (ASE) has been previously assessed in various organisms, e.g., human and fish, and ASE was found to be important in the context of phenotypic variability and disease. In this study, we utilized Real-Time PCR techniques to estimate ASE of the androgen receptor alpha (arα) gene in several distinct tissues of Amazon mollies. We found an allelic bias favoring the maternal ancestor (P. mexicana) allele in ovarian tissue. This allelic bias was not observed in the gill or the brain tissue. Sequencing of the promoter regions of both alleles revealed an association between an Indel in a known CpG island and differential expression. Future studies may reveal whether our observed cis-regulatory divergence is caused by an ovary-specific trans-regulatory element, preferentially activating the allele of the maternal ancestor.
The El Nino-Southern Oscillation (ENSO) is the main driver of the interannual variability in eastern African rainfall, with a significant impact on vegetation and agriculture and dire consequences for food and social security. In this study, we identify and quantify the ENSO contribution to the eastern African rainfall variability to forecast future eastern African vegetation response to rainfall variability related to a predicted intensified ENSO. To differentiate the vegetation variability due to ENSO, we removed the ENSO signal from the climate data using empirical orthogonal teleconnection (EOT) analysis. Then, we simulated the ecosystem carbon and water fluxes under the historical climate without components related to ENSO teleconnections. We found ENSO-driven patterns in vegetation response and confirmed that EOT analysis can successfully produce coupled tropical Pacific sea surface temperature-eastern African rainfall teleconnection from observed datasets. We further simulated eastern African vegetation response under future climate change as it is projected by climate models and under future climate change combined with a predicted increased ENSO intensity. Our EOT analysis highlights that climate simulations are still not good at capturing rainfall variability due to ENSO, and as we show here the future vegetation would be different from what is simulated under these climate model outputs lacking accurate ENSO contribution. We simulated considerable differences in eastern African vegetation growth under the influence of an intensified ENSO regime which will bring further environmental stress to a region with a reduced capacity to adapt effects of global climate change and food security.
Increased Achilles (AT) and Patellar tendon (PT) thickness in adolescent athletes compared to non-athletes could be shown. However, it is unclear, if changes are of pathological or physiological origin due to training. The aim of this study was to determine physiological AT and PT thickness adaptation in adolescent elite athletes compared to non-athletes, considering sex and sport. In a longitudinal study design with two measurement days (M1/M2) within an interval of 3.2 ± 0.8 years, 131 healthy adolescent elite athletes (m/f: 90/41) out of 13 different sports and 24 recreationally active controls (m/f: 6/18) were included. Both ATs and PTs were measured at standardized reference points. Athletes were divided into 4 sport categories [ball (B), combat (C), endurance (E) and explosive strength sports (S)]. Descriptive analysis (mean ± SD) and statistical testing for group differences was performed (α = 0.05). AT thickness did not differ significantly between measurement days, neither in athletes (5.6 ± 0.7 mm/5.6 ± 0.7 mm) nor in controls (4.8 ± 0.4 mm/4.9 ± 0.5 mm, p > 0.05). For PTs, athletes presented increased thickness at M2 (M1: 3.5 ± 0.5 mm, M2: 3.8 ± 0.5 mm, p < 0.001). In general, males had thicker ATs and PTs than females (p < 0.05). Considering sex and sports, only male athletes from B, C, and S showed significant higher PT-thickness at M2 compared to controls (p ≤ 0.01). Sport-specific adaptation regarding tendon thickness in adolescent elite athletes can be detected in PTs among male athletes participating in certain sports with high repetitive jumping and strength components. Sonographic microstructural analysis might provide an enhanced insight into tendon material properties enabling the differentiation of sex and influence of different sports.