Early Energy Deficit in Huntington Disease: Identification of a Plasma Biomarker Traceable during Disease Progression

Huntington disease (HD) is a fatal neurodegenerative disorder, with no effective treatment. The pathogenic mechanisms underlying HD have not been elucidated, but weight loss, associated with chorea and cognitive decline, is a characteristic feature of the disease that is accessible to investigation. We, therefore, performed a multiparametric study exploring body weight and the mechanisms of its loss in 32 presymptomatic carriers and HD patients in the early stages of the disease, compared to 21 controls. We combined this study with a multivariate statistical analysis of plasma components quantified by proton nuclear magnetic resonance (1H NMR) spectroscopy. We report evidence of an early hypermetabolic state in HD. Weight loss was observed in the HD group even in presymptomatic carriers, although their caloric intake was higher than that of controls. Inflammatory processes and primary hormonal dysfunction were excluded. 1H NMR spectroscopy on plasma did, however, distinguish HD patients at different stages of the disease and presymptomatic carriers from controls. This distinction was attributable to low levels of the branched chain amino acids (BCAA), valine, leucine and isoleucine. BCAA levels were correlated with weight loss and, importantly, with disease progression and abnormal triplet repeat expansion size in the HD1 gene. Levels of IGF1, which is regulated by BCAA, were also significantly lower in the HD group. Therefore, early weight loss in HD is associated with a systemic metabolic defect, and BCAA levels may be used as a biomarker, indicative of disease onset and early progression. The decreased plasma levels of BCAA may correspond to a critical need for Krebs cycle energy substrates in the brain that increased metabolism in the periphery is trying to provide

The First Post-Kepler Brightness Dips of KIC 8462852

We present a photometric detection of the first brightness dips of the unique variable star KIC 8462852 since the end of the Kepler space mission in 2013 May. Our regular photometric surveillance started in October 2015, and a sequence of dipping began in 2017 May continuing on through the end of 2017, when the star was no longer visible from Earth. We distinguish four main 1-2.5% dips, named "Elsie," "Celeste," "Skara Brae," and "Angkor", which persist on timescales from several days to weeks. Our main results so far are: (i) there are no apparent changes of the stellar spectrum or polarization during the dips; (ii) the multiband photometry of the dips shows differential reddening favoring non-grey extinction. Therefore, our data are inconsistent with dip models that invoke optically thick material, but rather they are in-line with predictions for an occulter consisting primarily of ordinary dust, where much of the material must be optically thin with a size scale <<1um, and may also be consistent with models invoking variations intrinsic to the stellar photosphere. Notably, our data do not place constraints on the color of the longer-term "secular" dimming, which may be caused by independent processes, or probe different regimes of a single process

Teaching the science of learning

The science of learning has made a considerable contribution to our understanding of effective teaching and learning strategies. However, few instructors outside of the field are privy to this research. In this Tutorial Review, we focus on six specific cognitive strategies that have received robust support from decades of research: spaced practice, interleaving, retrieval practice, elaboration, concrete examples, and dual coding. We describe the basic research behind each strategy and relevant applied research, present examples of existing and suggested implementation, and make recommendations for further research that would broaden the reach of these strategies

Nanotechnology advances towards development of targeted-treatment for obesity

Obesity through its association with type 2 diabetes (T2D), cancer and cardiovascular diseases (CVDs), poses a serious health threat, as these diseases contribute to high mortality rates. Pharmacotherapy alone or in combination with either lifestyle modifcation or surgery, is reliable in maintaining a healthy body weight, and preventing progression to obesity-induced diseases. However, the anti-obesity drugs are limited by non-specifcity and unsustainable weight loss efects. As such, novel and improved approaches for treatment of obesity are urgently needed. Nanotechnology-based therapies are investigated as an alternative strategy that can treat obesity and be able to overcome the drawbacks associated with conventional therapies. The review presents three nanotechnology-based anti-obesity strategies that target the white adipose tissues (WATs) and its vasculature for the reversal of obesity. These include inhibition of angiogenesis in the WATs, transformation of WATs to brown adipose tissues (BATs), and photothermal lipolysis of WATs. Compared to conventional therapy, the targeted-nanosystems have high tolerability, reduced side efects, and enhanced efcacy. These efects are reproducible using various nanocarriers (liposomes, polymeric and gold nanoparticles), thus providing a proof of concept that targeted nanotherapy can be a feasible strategy that can combat obesity and prevent its comorbiditie

.How much may I eat? Calorie estimates based upon energy expenditure prediction equations.

How much may I eat? Most healthcare workers, when asked this question, have insufficient knowledge to educate their patients on a healthy energy intake level. In this review we examine the available methods for estimating adult energy requirements with a focus on the newly developed National Academy of Sciences/Institute of Medicine (NAS/IOM) doubly-labelled water total energy expenditure (TEE) prediction equations. An overview is first provided of the traditional factorial method of estimating energy requirements. We then extend this overview by exploring the development of the NAS/IOM TEE prediction models and their role in estimating energy requirements as a function of sex, age, weight, height and physical activity level. The NAS/IOM prediction models were developed for evaluating group energy requirements, although the formulas can be applied in individual 'example' patients for educational purposes. Potential limitations and interpretation issues of both the factorial and NAS/IOM methods are examined. This information should provide healthcare professionals with the tools and understanding to appropriately answer the question, 'How much may I eat?

Why do obese patients not lose more weight when treated with low-calorie diets? A mechanistic perspective.

Maximal weight loss observed in low-calorie diet (LCD) studies tends to be small, and the mechanisms leading to this low treatment efficacy have not been clarified. Less-than-expected weight loss with LCDs can arise from an increase in fractional energy absorption (FEA), adaptations in energy expenditure, or incomplete patient diet adherence. We systematically reviewed studies of FEA and total energy expenditure (TEE) in obese patients undergoing weight loss with LCDs and in patients with reduced obesity (RO), respectively. This information was used to support an energy balance model that was then applied to examine patient adherence to prescribed LCD treatment programs. In the limited available literature, FEA was unchanged from baseline in short-term (or=26 wk) studies were found. Review of doubly labeled water and respiratory chamber studies identified 10 reports of TEE in RO patients (n = 150) with long-term weight loss. These patients, who were weight stable, had a TEE almost identical to measured or predicted values in never-obese subjects (weighted mean difference: 1.3%; range: -1.7-8.5%). Modeling of energy balance, as supported by reviewed FEA and TEE studies, suggests that obese subjects participating in LCD programs have a weight loss less than half of that predicted. The small maximal weight loss observed with LCD treatments thus is likely not due to gastrointestinal adaptations but may be attributed, by deduction, to difficulties with patient adherence or, to a lesser degree, to metabolic adaptations induced by negative energy balance that are not captured by the current models