Brain Age from the Electroencephalogram of Sleep

The human electroencephalogram (EEG) of sleep undergoes profound changes with age. These changes can be conceptualized as "brain age", which can be compared to an age norm to reflect the deviation from normal aging process. Here, we develop an interpretable machine learning model to predict brain age based on two large sleep EEG datasets: the Massachusetts General Hospital sleep lab dataset (MGH, N = 2,621) covering age 18 to 80; and the Sleep Hearth Health Study (SHHS, N = 3,520) covering age 40 to 80. The model obtains a mean absolute deviation of 8.1 years between brain age and chronological age in the healthy participants in the MGH dataset. As validation, we analyze a subset of SHHS containing longitudinal EEGs 5 years apart, which shows a 5.5 years difference in brain age. Participants with neurological and psychiatric diseases, as well as diabetes and hypertension medications show an older brain age compared to chronological age. The findings raise the prospect of using sleep EEG as a biomarker for healthy brain aging

Determinação das áreas de potencial de riscos de precipitações intensas em Belo Horizonte.

A áreas urbanas têm sido objeto de estudos climatológicos por causa da formação de "ilhas de calor", entretanto, as chuvas intensas são as responsáveis por causar os principais desastres. Eventos extremos de precipitações têm aumentado significativamente nas últimas duas décadas no município de Belo Horizonte, ocasionando enchentes urbanas, desabamentos de casas e desmoronamentos. A instalação de uma rede de pluviômetros para a coleta de dados durante a estação chuvosa 2003/04 na região metropolitana, permitiu a análise espacial das precipitações, regionalização dos dados, elaboração de um mapa de regiões com alto potencial de chuvas intensas e a elaboração de alertas diários de tempestades severas

Dynamic Resistance Training Improves Cardiac Autonomic Modulation and Oxidative Stress Parameters in Chronic Stroke Survivors: A Randomized Controlled Trial

Stroke survivors are at substantial risk of recurrent cerebrovascular event or cardiovascular disease. Exercise training offers nonpharmacological treatment for these subjects; however, the execution of the traditional exercise protocols and adherence is constantly pointed out as obstacles. Based on these premises, the present study investigated the impact of an 8-week dynamic resistance training protocol with elastic bands on functional, hemodynamic, and cardiac autonomic modulation, oxidative stress markers, and plasma nitrite concentration in stroke survivors. Twenty-two patients with stroke were randomized into control group (CG, n=11) or training group (TG, n=11). Cardiac autonomic modulation, oxidative stress markers, plasma nitrite concentration, physical function and hemodynamic parameters were evaluated before and after 8 weeks. Results indicated that functional parameters (standing up from the sitting position (P=0.011) and timed up and go (P=0.042)) were significantly improved in TG. Although not statistically different, both systolic blood pressure (Δ=-10.41 mmHg) and diastolic blood pressure (Δ=-8.16 mmHg) were reduced in TG when compared to CG. Additionally, cardiac autonomic modulation (sympathovagal balance-LF/HF ratio) and superoxide dismutase were improved, while thiobarbituric acid reactive substances and carbonyl levels were reduced in TG when compared to the CG subjects. In conclusion, our findings support the hypothesis that dynamic resistance training with elastic bands may improve physical function, hemodynamic parameters, autonomic modulation, and oxidative stress markers in stroke survivors. These positive changes would be associated with a reduced risk of a recurrent stroke or cardiac event in these subjects