Gustavo Zampier dos Santos Lima

Probabilidade de hipertensão arterial a partir de indicadores antropométricos em adultos

OBJECTIVE: To associate anthropometric measures with arterial hypertension and develop a mathematical model to estimate this probability. SUBJECTS AND METHODS: A cross-sectional study was carried out with 3,445 adults of both sexes, between 18 and 60 years of age. Body mass index (BMI), waist-to-height ratio (WHR), waist circumference (HC), in addition age and sex were assessed. Blood pressure (BP) was measured and classified according to World Health Organization (WHO) and Brazilian Society of Hypertension (SBH) recommendations, using frequency analyses, correlation and logistic regression between variables, with p < 0.05. RESULTS: Males were twice as likely as woman to have hypertension, and for each year of life, the chance of hypertension increased 1.04 times. Odds ratio of hypertension in overweight individual increased two times, and in obese subjects, four times. CONCLUSION: Changes in body composition with advancing age, especially in men, were associated with hypertension, and assessment of these changes was relevant in the identification of this disease.

Predictability of arousal in mouse slow wave sleep by accelerometer data

Arousals can be roughly characterized by punctual intrusions of wakefulness into sleep. In a standard perspective, using human electroencephalography (EEG) data, arousals are associated to slow-wave rhythms and K-complex brain activity. The physiological mechanisms that give rise to arousals during sleep are not yet fully understood. Moreover, subtle body movement patterns, which may characterize arousals both in human and in animals, are usually not detectable by eye perception and are not in general present in sleep studies. In this paper, we focus attention on accelerometer records (AR) to characterize and predict arousal during slow wave sleep (SWS) stage of mice. Furthermore, we recorded the local field potentials (LFP) from the CA1 region in the hippocampus and paired with accelerometer data. The hippocampus signal was also used here to identify the SWS stage. We analyzed the AR dynamics of consecutive arousals using recurrence technique and the determinism (DET) quantifier. Recurrence is a fundamental property of dynamical systems, which can be exploited to characterize time series properties. The DET index evaluates how similar are the evolution of close trajectories: in this sense, it computes how accurate are predictions based on past trajectories. For all analyzed mice in this work, we observed, for the first time, the occurrence of a universal dynamic pattern a few seconds that precedes the arousals during SWS sleep stage based only on the AR signal. The predictability success of an arousal using DET from AR is nearly 90%, while similar analysis using LFP of hippocampus brain region reveal 88% of success. Noteworthy, our findings suggest an unique dynamical behavior pattern preceding an arousal of AR data during sleep. Thus, the employment of this technique applied to AR data may provide useful information about the dynamics of neuronal activities that control sleep-waking switch during SWS sleep period. We argue that the predictability of arousals observed through DET(AR) can be functionally explained by a respiratory-driven modification of neural states. Finally, we believe that the method associating AR data with other physiologic events such as neural rhythms can become an accurate, convenient and non-invasive way of studying the physiology and physiopathology of movement and respiratory processes during sleep.

Optimizing the detection of nonstationary signals by using recurrence analysis

Recurrence analysis and its quantifiers are strongly dependent on the evaluation of the vicinity threshold parameter, i.e., the threshold to regard two points close enough in phase space to be considered as just one. We develop a new way to optimize the evaluation of the vicinity threshold in order to assure a higher level of sensitivity to recurrence quantifiers to allow the detection of even small changes in the dynamics. It is used to promote recurrence analysis as a tool to detect nonstationary behavior of time signals or space profiles. We show that the ability to detect small changes provides information about the present status of the physical process responsible to generate the signal and offers mechanisms to predict future states. Here, a higher sensitive recurrence analysis is proposed as a precursor, a tool to predict near future states of a particular system, based on just (experimentally) obtained signals of some available variables of the system. Comparisons with traditional methods of recurrence analysis show that the optimization method developed here is more sensitive to small variations occurring in a signal. The method is applied to numerically generated time series as well as experimental data from physiology.

Quantifying entropy using recurrence matrix microstates

We conceive a new recurrence quantifier for time series based on the concept of information entropy, in which the probabilities are associated with the presence of microstates defined on the recurrence matrix as small binary submatrices. The new methodology to compute the entropy of a time series has advantages compared to the traditional entropies defined in the literature, namely, a good correlation with the maximum Lyapunov exponent of the system and a weak dependence on the vicinity threshold parameter. Furthermore, the new method works adequately even for small segments of data, bringing consistent results for short and long time series. In a case where long time series are available, the new methodology can be employed to obtain high precision results since it does not demand large computational times related to the analysis of the entire time series or recurrence matrices, as is the case of other traditional entropy quantifiers. The method is applied to discrete and continuous systems.

Self organized energetic model for collective activity on animal tissue

We construct a self-organized critical (SOC) model to explain spontaneous collective activity in animal tissue without the necessity of a muscular or a central control nervous system. Our prototype model is an epithelial cuboid tissue formed by a single layer of cells as the internal digestive cavity of primitive animals. The tissue is composed by cells that absorb nutrients and store energy, with probability p, to participate in a collective tissue activity. Each cell can be in two states: at high energy and able to became active or at low metabolic energy and remain at rest. Any cell can spontaneously, with a very low probability, spark a collective activity across its neighbors that share a minimal energy. Cells participating in tissue activity consume all their energy. A power-law relation P(s)∝sγ for the probability of having a collective activity with s cells is observed. By construction this model is analogue to the forest fire SOC model. Our approach produces naturally a critical state for the activity in animal tissue, besides it explains self-sustained activity in a living animal tissue without feedback control.