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Adjunctive therapy for hypertension is in high demand for clinical research. Therefore, several meta-analyses have provided sufficient evidence for meditation as an adjunct therapy, without being anchored on reliable physiological grounds. Meditation modulates the autonomic nervous system. Herein, we propose a hierarchical-dependent effect for the carotid body (CB) in attenuating blood pressure (BP) and ventilatory variability (VV) fine-tuning due to known nerve connections between the CB, prefrontal brain, hypothalamus, and solitary tract nucleus. The aim of this exploratory study was to investigate the role of CB in the possible decrease in BP and changes in VV that could occur in response to meditation. This was a prospective, single-center, parallel-group, randomized, controlled clinical trial with concealed allocation. Eligible adult subjects of both sexes with stage 1 hypertension will be randomized into 1 of 2 groups: transcendental meditation or a control group. Subjects will be invited to 3 visits after randomization and 2 additional visits after completing 8 weeks of meditation or waiting-list control. Thus, subjects will undergo BP measurements in normoxia and hyperoxia, VV measurements using the Poincaré method at rest and during exercise, and CB activity measurement in the laboratory. The primary outcome of this study was the detection of changes in BP and CB activity after 8 weeks. Our secondary outcome was the detection of changes in the VV at rest and during exercise. We predict that interactions between hyperoxic deactivation of CB and meditation; Will reduce BP beyond stand-alone intervention or alternatively; Meditation will significantly attenuate the effects of hyperoxia as a stand-alone intervention. In addition, VV can be changed, partially mediated by a reduction in CB activity. Trial registration number: ReBEC registry (RBR-55n74zm). Stage: pre-results.

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Dengue fever is a tropical disease transmitted mainly by the female Aedes aegypti mosquito that affects millions of people every year. As there is still no safe and effective vaccine, currently the best way to prevent the disease is to control the proliferation of the transmitting mosquito. Since the proliferation and life cycle of the mosquito depend on environmental variables such as temperature and water availability, among others, statistical models are needed to understand the existing relationships between environmental variables and the recorded number of dengue cases and predict the number of cases for some future time interval. This prediction is of paramount importance for the establishment of control policies. In general, dengue-fever datasets contain the number of cases recorded periodically (in days, weeks, months or years). Since many dengue-fever datasets tend to be of the overdispersed, long-tail type, some common models like the Poisson regression model or negative binomial regression model are not adequate to model it. For this reason, in this paper we propose modeling a dengue-fever dataset by using a Poisson-inverse-Gaussian regression model. The main advantage of this model is that it adequately models overdispersed long-tailed data because it has a wider skewness range than the negative binomial distribution. We illustrate the application of this model in a real dataset and compare its performance to that of a negative binomial regression model.

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BACKGROUND: Brain injuries are frequent causes of intubation and mechanical ventilation. The aim of this study was to investigate the accuracy and sensitivity of clinical parameters in predicting successful extubation in patients with acute brain injury. METHODS: Six hundred and forty-four patients assisted at a high-complexity hospital were recruited. Patients were divided as for successful or failed extubation. The VISAGE score, maximum inspiratory and expiratory pressures, peak cough flow, and airway occlusion pressure at 0.1 s were used as predictors. Logistic regression analyses using ROC-curve identified values of accuracy and sensitivity. The Hosmer-Lemeshow test and the stepwise method calibrated the statistical model. RESULTS: VISAGE score (odds ratio of 1.975), maximum inspiratory pressure (odds ratio of 1.024), and peak cough flow (odds ratio of 0.981) are factors consistent in distinguishing success from failure extubation. The ROC curve presented an accuracy of 79.7% and a sensitivity of 95.8%. CONCLUSIONS: VISAGE score, maximum inspiratory pressure and peak cough flow showed good accuracy and sensitivity in predicting successful extubation in patients with acute brain injury. The greater impact of VISAGE score indicates that patients' neurological profile should be considered in association with ventilatory parameters in the decision of extubation.

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The pandemic scenery caused by the new coronavirus, called SARS-CoV-2, increased interest in statistical models capable of projecting the evolution of the number of cases (and associated deaths) due to COVID-19 in countries, states and/or cities. This interest is mainly due to the fact that the projections may help the government agencies in making decisions in relation to procedures of prevention of the disease. Since the growth of the number of cases (and deaths) of COVID-19, in general, has presented a heterogeneous evolution over time, it is important that the modeling procedure is capable of identifying periods with different growth rates and proposing an adequate model for each period. Here, we present a modeling procedure based on the fit of a piecewise growth model for the cumulative number of deaths. We opt to focus on the modeling of the cumulative number of deaths because, other than for the number of cases, these values do not depend on the number of diagnostic tests performed. In the proposed approach, the model is updated in the course of the pandemic, and whenever a "new" period of the pandemic is identified, it creates a new sub-dataset composed of the cumulative number of deaths registered from the change point and a new growth model is chosen for that period. Three growth models were fitted for each period: exponential, logistic and Gompertz models. The best model for the cumulative number of deaths recorded is the one with the smallest mean square error and the smallest Akaike information criterion (AIC) and Bayesian information criterion (BIC) values. This approach is illustrated in a case study, in which we model the number of deaths due to COVID-19 recorded in the State of São Paulo, Brazil. The results have shown that the fit of a piecewise model is very effective for explaining the different periods of the pandemic evolution.

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BACKGROUND/OBJECTIVE: The current approach to measuring ventilatory (in)efficiency (V'E -V'CO2 slope, nadir and intercept) presents critical drawbacks in the evaluation of COPD subjects, owing mainly to mechanical ventilatory constraints. Thus, we aimed to compare the current approach with a new method we have developed for ventilatory efficiency calculation. METHODS: The new procedure was based on measuring the amount of CO2 cleared by the lungs (V'CO2 , L/min) plotted against a predefined range of increase in minute ventilation (V'E ) (ten-fold increase based on semilog scale) during incremental exercise to symptom-limited maximum tolerance. This value was compared to a hypothetical predicted maximum CO2 output at the predicted maximal voluntary ventilation, defining ventilatory efficiency (ηV'E , %). The results were used to compare 30 subjects with COPD (II-IV Global Initiative for Chronic Obstructive Lung Disease, GOLD) and 10 non-COPD smokers, to establish the best discriminative physiological variable for disease severity through logistic multinomial regression. RESULTS: The new approach was more sensitive to progressive deterioration of airway obstruction, resulting in worse ηV'E as lung function worsens throughout the GOLD panel (ηV'E (%), p < .001), when compared with V'E -V'CO2 slope (p = .715) or V'E -V'CO2 nadir (p = .070), besides showing the best model based on the logistic regression approach. CONCLUSION: Although requiring more complex calculations compared to the current procedure, the new approach is highly sensitive to true ventilatory/gas-exchange deterioration, even throughout more severe pulmonary lung function in COPD subjects.

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In this paper, we study the performance of Bayesian computational methods to estimate the parameters of a bivariate survival model based on the Ali-Mikhail-Haq copula with marginal distributions given by Weibull distributions. The estimation procedure was based on Monte Carlo Markov Chain (MCMC) algorithms. We present three version of the Metropolis-Hastings algorithm: Independent Metropolis-Hastings (IMH), Random Walk Metropolis (RWM) and Metropolis-Hastings with a natural-candidate generating density (MH). Since the creation of a good candidate generating density in IMH and RWM may be difficult, we also describe how to update a parameter of interest using the slice sampling (SS) method. A simulation study was carried out to compare the performances of the IMH, RWM and SS. A comparison was made using the sample root mean square error as an indicator of performance. Results obtained from the simulations show that the SS algorithm is an effective alternative to the IMH and RWM methods when simulating values from the posterior distribution, especially for small sample sizes. We also applied these methods to a real data set.

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A common interest in gene expression data analysis is to identify from a large pool of candidate genes the genes that present significant changes in expression levels between a treatment and a control biological condition. Usually, it is done using a statistic value and a cutoff value that are used to separate the genes differentially and nondifferentially expressed. In this paper, we propose a Bayesian approach to identify genes differentially expressed calculating sequentially credibility intervals from predictive densities which are constructed using the sampled mean treatment effect from all genes in study excluding the treatment effect of genes previously identified with statistical evidence for difference. We compare our Bayesian approach with the standard ones based on the use of the t-test and modified t-tests via a simulation study, using small sample sizes which are common in gene expression data analysis. Results obtained report evidence that the proposed approach performs better than standard ones, especially for cases with mean differences and increases in treatment variance in relation to control variance. We also apply the methodologies to a well-known publicly available data set on Escherichia coli bacterium.

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