L-Glutamine Supplementation Enhances Strength and Power of Knee Muscles and Improves Glycemia Control and Plasma Redox Balance in Exercising Elderly Women.

We investigated the effects of oral L-glutamine (Gln) supplementation, associated or not with physical exercises, in control of glycemia, oxidative stress, and strength/power of knee muscles in elderly women. Physically active (n = 21) and sedentary (n = 23) elderly women aged 60 to 80 years were enrolled in the study. Plasma levels of D-fructosamine, insulin, reduced (GSH) and oxidized (GSSG) glutathione, iron, uric acid, and thiobarbituric acid-reactive substances (TBARs) (lipoperoxidation product), as well as knee extensor/flexor muscle torque peak and average power (isokinetic test), were assessed pre- and post-supplementation with Gln or placebo (30 days). Higher plasma D-fructosamine, insulin, and iron levels, and lower strength/power of knee muscles were found pre-supplementation in the NPE group than in the PE group. Post-supplementation, Gln subgroups showed higher levels of GSH, GSSG, and torque peak, besides lower D-fructosamine than pre-supplementation values. Higher muscle average power and plasma uric acid levels were reported in the PE + Gln group, whereas lower insulin levels were found in the NPE + Gln than pre-supplementation values. TBARs levels were diminished post-supplementation in all groups. Gln supplementation, mainly when associated with physical exercises, improves strength and power of knee muscles and glycemia control, besides boosting plasma antioxidant capacity of elderly women.

A simple mathematical model for the evaluation of the long first wave of the COVID-19 pandemic in Brazil.

We propose herein a mathematical model to predict the COVID-19 evolution and evaluate the impact of governmental decisions on this evolution, attempting to explain the long duration of the pandemic in the 26 Brazilian states and their capitals well as in the Federative Unit. The prediction was performed based on the growth rate of new cases in a stable period, and the graphics plotted with the significant governmental decisions to evaluate the impact on the epidemic curve in each Brazilian state and city. Analysis of the predicted new cases was correlated with the total number of hospitalizations and deaths related to COVID-19. Because Brazil is a vast country, with high heterogeneity and complexity of the regional/local characteristics and governmental authorities among Brazilian states and cities, we individually predicted the epidemic curve based on a specific stable period with reduced or minimal interference on the growth rate of new cases. We found good accuracy, mainly in a short period (weeks). The most critical governmental decisions had a significant temporal impact on pandemic curve growth. A good relationship was found between the predicted number of new cases and the total number of inpatients and deaths related to COVID-19. In summary, we demonstrated that interventional and preventive measures directly and significantly impact the COVID-19 pandemic using a simple mathematical model. This model can easily be applied, helping, and directing health and governmental authorities to make further decisions to combat the pandemic.