Unleashing the potential of dance: a neuroplasticity-based approach bridging from older adults to Parkinson’s disease patients

Parkinson’s disease (PD) is a neurodegenerative disorder that affects >1% of individuals worldwide and is manifested by motor symptoms such as tremor, rigidity, and bradykinesia, as well as non-motor symptoms such as cognitive impairment and depression. Non-pharmacological interventions such as dance therapy are becoming increasingly popular as complementary therapies for PD, in addition to pharmacological treatments that are currently widely available. Dance as a sensorimotor activity stimulates multiple layers of the neural system, including those involved in motor planning and execution, sensory integration, and cognitive processing. Dance interventions in healthy older people have been associated with increased activation of the prefrontal cortex, as well as enhanced functional connectivity between the basal ganglia, cerebellum, and prefrontal cortex. Overall, the evidence suggests that dance interventions can induce neuroplastic changes in healthy older participants, leading to improvements in both motor and cognitive functions. Dance interventions involving patients with PD show better quality of life and improved mobility, whereas the literature on dance-induced neuroplasticity in PD is sparse. Nevertheless, this review argues that similar neuroplastic mechanisms may be at work in patients with PD, provides insight into the potential mechanisms underlying dance efficacy, and highlights the potential of dance therapy as a non-pharmacological intervention in PD. Further research is warranted to determine the optimal dance style, intensity, and duration for maximum therapeutic benefit and to determine the long-term effects of dance intervention on PD progression

Erythritol and xylitol differentially impact brain networks involved in appetite regulation in healthy volunteers

Background: There is a growing consensus that sugar consumption should be reduced and the naturally occurring, low-calorie sweeteners xylitol and erythritol are gaining popularity as substitutes, but their effect on brain circuitry regulating appetite is unknown. Aim: The study’s objective was to examine the effects of the two sweeteners on cerebral blood flow (rCBF) and resting functional connectivity in brain networks involved in appetite regulation, and test whether these effects are related to gut hormone release. Methods: The study was performed as a randomized, double-blind, placebo-controlled, cross-over trial. Twenty volunteers received intragastric (ig) loads of 50g xylitol, 75g erythritol, 75g glucose dissolved in 300mL tap water or 300mL tap water. Resting perfusion and blood oxygenation level-dependent data were acquired to assess rCBF and functional connectivity. Blood samples were collected for determination of CCK, PYY, insulin and glucose. Results: We found: (i) xylitol, but not erythritol, increased rCBF in the hypothalamus, whereas glucose had the opposite effect; (ii) graph analysis of resting functional connectivity revealed a complex pattern of similarities and differences in brain network properties following xylitol, erythritol, and glucose; (iii) erythritol and xylitol induced a rise in CCK and PYY, (iv) erythritol had no and xylitol only minimal effects on glucose and insulin. Conclusion: Xylitol and erythritol have a unique combination of properties: no calories, virtually no effect on glucose and insulin while promoting the release of gut hormones, and impacting appetite-regulating neurocircuitry consisting of both similarities and differences with glucose

Decay of Classical Chaotic Systems - the Case of the Bunimovich Stadium

The escape of an ensemble of particles from the Bunimovich stadium via a small hole has been studied numerically. The decay probability starts out exponentially but has an algebraic tail. The weight of the algebraic decay tends to zero for vanishing hole size. This behaviour is explained by the slow transport of the particles close to the marginally stable bouncing ball orbits. It is contrasted with the decay function of the corresponding quantum system.Comment: 16 pages, RevTex, 3 figures are available upon request from [email protected], to be published in Phys.Rev.

Effect of the natural sweetener xylitol on gut hormone secretion and gastric emptying in humans:A pilot dose-ranging study

Sugar consumption is associated with a whole range of negative health effects and should be reduced and the natural sweetener xylitol might be helpful in achieving this goal. The present study was conducted as a randomized, placebo-controlled, double-blind, cross-over trial. Twelve healthy, lean volunteers received intragastric solutions with 7, 17 or 35 g xylitol or tap water on four separate days. We examined effects on: gut hormones, glucose, insulin, glucagon, uric acid, lipid profile, as well as gastric emptying rates, appetite-related sensations and gastrointestinal symptoms. We found: (i) a dose-dependent stimulation of cholecystokinin (CCK), active glucagon-like peptide-1 (aGLP-1), peptide tyrosine tyrosine (PYY)-release, and decelerated gastric emptying rates, (ii) a dose-dependent increase in blood glucose and insulin, (iii) no effect on motilin, glucagon, or glucose-dependent insulinotropic peptide (GIP)-release, (iv) no effect on blood lipids, but a rise in uric acid, and (v) increased bowel sounds as only side effects. In conclusion, low doses of xylitol stimulate the secretion of gut hormones and induce a deceleration in gastric emptying rates. There is no effect on blood lipids and only little effect on plasma glucose and insulin. This combination of properties (low-glycemic sweetener which stimulates satiation hormone release) makes xylitol an attractive candidate for sugar replacement

Secretory granule neuroendocrine protein 1 (SGNE1) genetic variation and glucose intolerance in severe childhood and adult obesity

<p>Abstract</p> <p>Background</p> <p>7B2 is a regulator/activator of the prohormone convertase 2 which is involved in the processing of numerous neuropeptides, including insulin, glucagon and pro-opiomelanocortin. We have previously described a suggestive genetic linkage peak with childhood obesity on chr15q12-q14, where the 7B2 encoding gene, <it>SGNE1 </it>is located. The aim of this study is to analyze associations of <it>SGNE1 </it>genetic variation with obesity and metabolism related quantitative traits.</p> <p>Methods</p> <p>We screened <it>SGNE1 </it>for genetic variants in obese children and genotyped 12 frequent single nucleotide polymorphisms (SNPs). Case control analyses were performed in 1,229 obese (534 children and 695 adults), 1,535 individuals with type 2 diabetes and 1,363 controls, all French Caucasians. We also studied 4,922 participants from the D.E.S.I.R prospective population-based cohort.</p> <p>Results</p> <p>We did not find any association between <it>SGNE1 </it>SNPs and childhood or adult obesity. However, the 5' region SNP -1,701A>G associated with higher area under glucose curve after oral glucose tolerance test (p = 0.0005), higher HOMA-IR (p = 0.005) and lower insulinogenic index (p = 0.0003) in obese children. Similar trends were found in obese adults. SNP -1,701A>G did not associate with risk of T2D but tends to associate with incidence of type 2 diabetes (HR = 0.75 95%CI [0.55–1.01]; p = 0.06) in the prospective cohort.</p> <p>Conclusion</p> <p><it>SGNE1 </it>genetic variation does not contribute to obesity and common forms of T2D but may worsen glucose intolerance and insulin resistance, especially in the background of severe and early onset obesity. Further molecular studies are required to understand the molecular bases involved in this process.</p

Can we predict Λ\Lambda for the Non-SUSY sector of the Landscape ?

We propose a new selection criteria for predicting the most probable wavefunction of the universe that propagates on the string landscape background, by studying its dynamics from a quantum cosmology view. Previously we applied this proposal to the $SUSY$ sector of the landscape. In this work the dynamic selection criterion is applied to the investigation of the non-$SUSY$ sector.In the absence of detailed information about its structure, it is assumed that this sector has a stochastic distribution of vacua energies.The calculation of a distribution probability for the cosmological constants $\Lambda_{eff}$, obtained from the density of states $\rho$, indicates that the most probable wavefunction is peaked around universes with zero $\Lambda_{eff}$. In contrast to the {\it extended wavefunction} solutions found for the $SUSY$ sector with $N$-vacua and peaked around $\Lambda_{eff}\simeq \frac{1}{N^2}$, wavefunctions residing on the non-$SUSY$ sector exhibit {\it Anderson localization}.Although minisuperspace is a limited approach it presently provides a dynamical quantum selection rule for the most probable vacua solution from the landscape.Comment: 6 pages, 1 figur

Gene expression profiling for molecular distinction and characterization of laser captured primary lung cancers

<p>Abstract</p> <p>Methods</p> <p>We examined gene expression profiles of tumor cells from 29 untreated patients with lung cancer (10 adenocarcinomas (AC), 10 squamous cell carcinomas (SCC), and 9 small cell lung cancer (SCLC)) in comparison to 5 samples of normal lung tissue (NT). The European and American methodological quality guidelines for microarray experiments were followed, including the stipulated use of laser capture microdissection for separation and purification of the lung cancer tumor cells from surrounding tissue.</p> <p>Results</p> <p>Based on differentially expressed genes, different lung cancer samples could be distinguished from each other and from normal lung tissue using hierarchical clustering. Comparing AC, SCC and SCLC with NT, we found 205, 335 and 404 genes, respectively, that were at least 2-fold differentially expressed (estimated false discovery rate: < 2.6%). Different lung cancer subtypes had distinct molecular phenotypes, which also reflected their biological characteristics. Differentially expressed genes in human lung tumors which may be of relevance in the respective lung cancer subtypes were corroborated by quantitative real-time PCR.</p> <p>Genetic programming (GP) was performed to construct a classifier for distinguishing between AC, SCC, SCLC, and NT. Forty genes, that could be used to correctly classify the tumor or NT samples, have been identified. In addition, all samples from an independent test set of 13 further tumors (AC or SCC) were also correctly classified.</p> <p>Conclusion</p> <p>The data from this research identified potential candidate genes which could be used as the basis for the development of diagnostic tools and lung tumor type-specific targeted therapies.</p