Universidade Federal de Minas Gerais

About: Universidade Federal de Minas Gerais is a education organization based out in Belo Horizonte, Minas Gerais, Brazil. It is known for research contribution in the topics: Population & Immune system. The organization has 41631 authors who have published 75688 publications receiving 1249905 citations.

Cost-effectiveness of guideline-endorsed treatments for low back pain: a systematic review

Abstract: Healthcare costs for low back pain (LBP) are increasing rapidly. Hence, it is important to provide treatments that are effective and cost-effective. The purpose of this systematic review was to investigate the cost-effectiveness of guideline-endorsed treatments for LBP. We searched nine clinical and economic electronic databases and the reference list of relevant systematic reviews and included studies for eligible studies. Economic evaluations conducted alongside randomised controlled trials investigating treatments for LBP endorsed by the guideline of the American College of Physicians and the American Pain Society were included. Two independent reviewers screened search results and extracted data. Data extracted included the type and perspective of the economic evaluation, the treatment comparators, and the relative cost-effectiveness of the treatment comparators. Twenty-six studies were included. Most studies found that interdisciplinary rehabilitation, exercise, acupuncture, spinal manipulation or cognitive-behavioural therapy were cost-effective in people with sub-acute or chronic LBP. Massage alone was unlikely to be cost-effective. There were inconsistent results on the cost-effectiveness of advice, insufficient evidence on spinal manipulation for people with acute LBP, and no evidence on the cost-effectiveness of medications, yoga or relaxation. This review found evidence supporting the cost-effectiveness of the guideline-endorsed treatments of interdisciplinary rehabilitation, exercise, acupuncture, spinal manipulation and cognitive-behavioural therapy for sub-acute or chronic LBP. There is little or inconsistent evidence for other treatments endorsed in the guideline.

Cannabinoids and anxiety.

Abstract: The term cannabinoids encompasses compounds produced by the plant Cannabis sativa, such as Δ 9-tetrahydrocannabinol, and synthetic counterparts. Their actions occur mainly through activation of cannabinoid type 1 (CB1) receptors. Arachidonoyl ethanolamide (anandamide) and 2-arachidonoyl glycerol (2-AG) serve as major endogenous ligands (endocannabinoids) of CB1 receptors. Hence, the cannabinoid receptors, the endocannabinoids, and their metabolizing enzymes comprise the endocannabinoid system. Cannabinoids induce diverse responses on anxiety- and fear-related behaviors. Generally, low doses tend to induce anxiolytic-like effects, whereas high doses often cause the opposite. Inhibition of endocannabinoid degradation seems to circumvent these biphasic effects by enhancing CB1 receptor signaling in a temporarily and spatially restricted manner, thus reducing anxiety-like behaviors. Pharmacological blockade or genetic deletion of CB1 receptors, in turn, primarily exerts anxiogenic-like effects and impairments in extinction of aversive memories. Interestingly, pharmacological blockade of Transient Receptor Potential Vanilloid Type-1 (TRPV1) channel, which can be activated by anandamide as well, has diametrically opposite consequences. This book chapter summarizes and conceptualizes our current knowledge about the role of (endo)cannabinoids in fear and anxiety and outlines implications for an exploitation of the endocannabinoid system as a target for new anxiolytic drugs.

Multivariable Gaussian Evolving Fuzzy Modeling System

Abstract: This paper introduces a class of evolving fuzzy rule-based system as an approach for multivariable Gaussian adaptive fuzzy modeling. The system is an evolving Takagi-Sugeno (eTS) functional fuzzy model, whose rule base can be continuously updated using a new recursive clustering algorithm based on participatory learning. The fuzzy sets of the rule antecedents are multivariable Gaussian membership functions, which have been adopted to preserve information between input variable interactions. The parameters of the membership functions are estimated by the clustering algorithm. A weighted recursive least-squares algorithm updates the parameters of the rule consequents. Experiments considering time-series forecasting and nonlinear system identification are performed to evaluate the performance of the approach proposed. The multivariable Gaussian evolving fuzzy models are compared with alternative evolving fuzzy models and classic models with fixed structures. The results suggest that multivariable Gaussian evolving fuzzy modeling is a promising approach for adaptive system modeling.

Changes in tree and liana communities along a successional gradient in a tropical dry forest in south-eastern Brazil

Abstract: We investigated changes in species composition and structure of tree and liana communities along a successional gradient in a seasonally dry tropical forest. There was a progressive increase in tree richness and all tree structural traits from early to late stages, as well as marked changes in tree species composition and dominance. This pattern is probably related to pasture management practices such as ploughing, which remove tree roots and preclude regeneration by resprouting. On the other hand, liana density decreased from intermediate to late stages, showing a negative correlation with tree density. The higher liana abundance in intermediate stage is probably due to a balanced availability of support and light availability, since these variables may show opposite trends during forest growth. Predicted succession models may represent extremes in a continuum of possible successional pathways strongly influenced by land use history, climate, soil type, and by the outcomes of tree–liana interactions.