Volatile constituents and behavioral change induced by Cymbopogon winterianus leaf essential oil in rodents

Cymbopogon winterianus Jowitt (‘Java citronella’) is an important essential oil yielding aromatic grass cultivated in India and Brazil and its volatile essential oils extracted from its leaves are used in perfumery, cosmetics, pharmaceuticals and flavoring industries. However, there is no report on any psychopharmacological study of C. winterianus leaf essential oil (LEO) available to date. In this study, the pharmacological effects of the LEO were investigated in animal models and its phytochemical analyses. GC-MS analysis showed a mixture of monoterpenes, as citronellal (36.19%), geraniol (32.82%) and citronellol (11.37%). LEO exhibited an inhibitory effect on the locomotor activity of mice, an antinociceptive effect by increasing the reaction time in the writhing and capsaicin tests. All doses induced a significant increase in the sleeping time of animals not having modified however, the latency. The LEO did not alter the remaining time of the animals on the rota-rod apparatus. These results suggest a possible central effect.Key words: Cymbopogon winterianus, essential oil, CNS, behavioral effects, analgesic

Bacillary Prostatitis after Intravesical Immunotherapy: A Rare Adverse Effect

Nowadays, the most efficient form of intravesical immunotherapy for superficial transitional cell carcinoma of the urinary bladder is the instillation of bacillus Calmette-Guérin (BCG), proceeding from an attenuated strain of Mycobacterium bovis. In up to 40% of cases, its instillation is associated with significantly elevated prostate-specific antigen (PSA) levels. In these cases, prostate biopsy should be withheld for 3 months and PSA should be monitored. Bacillary prostatitis is a rare occurrence in patients treated with intravesical BCG immunotherapy. Although symptomatic bacillary prostatitis is even rarer, it is the worst type of this condition. The aims of this study are to report a case of bacillary prostatitis as a rare adverse effect of intravesical BCG immunotherapy and to make a theoretical review about how to manage this complication. A 58-year-old man, former smoker, underwent a transurethral resection of the bladder in February 2004 because of a papillary transitional cell carcinoma of the bladder (pT1G2N0M0). After surgery, BCG instillation therapy was given in a total of 15 instillations, the last one in March 2007. In the last 3 months of therapy, until May 2007, a progressive increase in his PSA level was registered, and he underwent a prostate biopsy revealing granulomatous prostatitis of bacillary etiology. The semen culture was positive for M. bovis. After 3 months of a two-drug (isoniazid and rifampin) antituberculous regimen, the semen culture became negative and the PSA level decreased. The early identification of intravesical BCG immunotherapy complications allows their effective treatment. However, when a histological diagnosis of asymptomatic granulomatous prostatitis is made, the execution and type of treatment are controversial

Covid-19 Dynamic Monitoring and Real-Time Spatio-Temporal Forecasting

Background: Periodically, humanity is often faced with new and emerging viruses that can be a significant global threat. It has already been over a century post—the Spanish Flu pandemic, and we are witnessing a new type of coronavirus, the SARS-CoV-2, which is responsible for Covid-19. It emerged from the city of Wuhan (China) in December 2019, and within a few months, the virus propagated itself globally now resulting more than 50 million cases with over 1 million deaths. The high infection rates coupled with dynamic population movement demands for tools, especially within a Brazilian context, that will support health managers to develop policies for controlling and combating the new virus. / Methods: In this work, we propose a tool for real-time spatio-temporal analysis using a machine learning approach. The COVID-SGIS system brings together routinely collected health data on Covid-19 distributed across public health systems in Brazil, as well as taking to under consideration the geographic and time-dependent features of Covid-19 so as to make spatio-temporal predictions. The data are sub-divided by federative unit and municipality. In our case study, we made spatio-temporal predictions of the distribution of cases and deaths in Brazil and in each federative unit. Four regression methods were investigated: linear regression, support vector machines (polynomial kernels and RBF), multilayer perceptrons, and random forests. We use the percentage RMSE and the correlation coefficient as quality metrics. / Results: For qualitative evaluation, we made spatio-temporal predictions for the period from 25 to 27 May 2020. Considering qualitatively and quantitatively the case of the State of Pernambuco and Brazil as a whole, linear regression presented the best prediction results (thematic maps with good data distribution, correlation coefficient >0.99 and RMSE (%) <4% for Pernambuco and around 5% for Brazil) with low training time: [0.00; 0.04 ms], CI 95%. / Conclusion: Spatio-temporal analysis provided a broader assessment of those in the regions where the accumulated confirmed cases of Covid-19 were concentrated. It was possible to differentiate in the thematic maps the regions with the highest concentration of cases from the regions with low concentration and regions in the transition range. This approach is fundamental to support health managers and epidemiologists to elaborate policies and plans to control the Covid-19 pandemics

COVID-SGIS: A Smart Tool for Dynamic Monitoring and Temporal Forecasting of Covid-19

Background: The global burden of the new coronavirus SARS-CoV-2 is increasing at an unprecedented rate. The current spread of Covid-19 in Brazil is problematic causing a huge public health burden to its population and national health-care service. To evaluate strategies for alleviating such problems, it is necessary to forecast the number of cases and deaths in order to aid the stakeholders in the process of making decisions against the disease. We propose a novel system for real-time forecast of the cumulative cases of Covid-19 in Brazil. / Methods: We developed the novel COVID-SGIS application for the real-time surveillance, forecast and spatial visualization of Covid-19 for Brazil. This system captures routinely reported Covid-19 information from 27 federative units from the Brazil.io database. It utilizes all Covid-19 confirmed case data that have been notified through the National Notification System, from March to May 2020. Time series ARIMA models were integrated for the forecast of cumulative number of Covid-19 cases and deaths. These include 6-days forecasts as graphical outputs for each federative unit in Brazil, separately, with its corresponding 95% CI for statistical significance. In addition, a worst and best scenarios are presented. / Results: The following federative units (out of 27) were flagged by our ARIMA models showing statistically significant increasing temporal patterns of Covid-19 cases during the specified day-to-day period: Bahia, Maranhão, Piauí, Rio Grande do Norte, Amapá, Rondônia, where their day-to-day forecasts were within the 95% CI limits. Equally, the same findings were observed for Espírito Santo, Minas Gerais, Paraná, and Santa Catarina. The overall percentage error between the forecasted values and the actual values varied between 2.56 and 6.50%. For the days when the forecasts fell outside the forecast interval, the percentage errors in relation to the worst case scenario were below 5%. / Conclusion: The proposed method for dynamic forecasting may be used to guide social policies and plan direct interventions in a cost-effective, concise, and robust manner. This novel tools can play an important role for guiding the course of action against the Covid-19 pandemic for Brazil and country neighbors in South America

Assessment of routine surveillance data as a tool to investigate measles outbreaks in Mozambique

BACKGROUND: Measles remains a major public health problem in Mozambique despite significant efforts to control the disease. Currently, health authorities base their outbreak control on data from the routine surveillance system while vaccine coverage and efficacy are calculated based on mathematical projections of the target population. The aim of this work was to assess the quality of the measles reporting system during two outbreaks that occurred in Maputo City (1998) and in Manica Province (2002). METHODS: Retrospectively, we collected data from the routine surveillance system, i.e. register books at health facilities and weekly provincial and national epidemiological reports. To test whether the provinces registered an outbreak, the distribution of measles cases was compared to an endemic level established based on cases reported in previous years. RESULTS: There was a significant under-notification of measles cases from the health facilities to the province and national level. Register books, the primary sources of information for the measles surveillance system, were found to be incomplete for two main variables: "age" and "vaccination status". CONCLUSION: The Mozambican surveillance system is based on poor quality records, receives the notification of only a fraction of the total number of measles in the country and may result in failures do detect epidemics. The measles reporting system does not provide the data needed by Expanded Program on Immunisation managers to make evidence-based decisions, nor does it allow in-depth analysis to monitor measles epidemiology in the country. The progress of Mozambique to the next stage of measles elimination will require an improvement of the routine surveillance system and a stronger Health Information System

Qualidade de alface crespa cultivada em sistema orgânico, convencional e hidropônico.

O objetivo deste trabalho foi avaliar a qualidade da alface do grupo crespa, cv. Vera, em sistemas de cultivo orgânico, convencional e hidropônico em Rio Branco-AC. O experimento foi conduzido em julho de 2009. As amostras dos sistemas, convencional e hidropônico (3 marcas comerciais), foram escolhidas aleatoriamente nos supermercados do município de Rio Branco, no mesmo dia de coleta da alface produzida em sistema orgânico. A alface orgânica, produzida na área experimental do Setor de Agricultura Ecológica da Universidade Federal do Acre (UFAC), em Rio Branco, foi cultivada em estufa, sob plantio direto utilizando folhas de bambu como cobertura do solo, e adubada com composto orgânico (17 t ha-1 em base seca). O delineamento utilizado foi o inteiramente casualizado, com cinco tratamentos e quatro repetições compostas por três plantas. As amostras foram lavadas, cortadas e processadas com folha e caule, para obter o suco. Logo após foram determinados os teores de sólidos solúveis e a concentração de nitrato e ácido ascórbico. As três marcas de alface hidropônica apresentaram maior teor de nitrato e menor concentração de sólidos solúveis e ácidos ascórbico, enquanto a alface orgânica apresentou qualidade superior, com baixa concentração de nitrato e maior teor de ácido ascórbico