SORPTION OF S-TRIAZINES IN BRAZILIAN RAINFOREST SOILS

This research was conducted to evaluate the sorption of Ametryn, Atrazine, Simazine, Prometrine and Metamitron to soils from "Mata Atlântica" at Ubatuba region (Atlantic rainforest soils), employing the batch equilibrium approach. The herbicides were weakly retained in soils with low soil organic matter (SOM) content and thus presenting high potential to water contamination. All herbicides have shown high Koc at Typic Humaquepts soil, the higher in SOM content. The sorption isotherms for the herbicides at Typic Humaquepts soil suggested specific interactions between herbicides and SOM probably with partial protonation of herbicides followed by ion-exchange processes and/or hydrogen bonding formation of hydroxyl groups on the SOM surface.Esta pesquisa foi conduzida para avaliar a sorção de ametrina, atrazina, simazina, prometrina e metamitron em solos de Mata Atlântica na região de Ubatuba, empregando-se o método em batelada. Os herbicidas foram fracamente retidos em solos com baixo teor de matéria orgânica (MO) e, portanto, apresentaram elevado potencial de contaminação da água. Todos os herbicidas mostraram alto valor de Koc em solos da classe Gleissolo Melânico Distrófico, que contém o teor mais elevado de MO. As isotermas de sorção dos herbicidas no Gleissolo Melânico Distrófico sugerem interações específicas entre os herbicidas e a MO, provavelmente com protonação parcial dos herbicidas seguida por processos de troca-iônica e/ou formação de pontes de hidrogênio dos grupos hidroxila sobre a superfície da MO

Physical Exercise Restores the Generation of New born Neurons in an Animal Model of Chronic Epilepsy

Neurogenesis impairment is associated with the chronic phase of the epilepsy in humans and also observed in animal models. Recent studies with animal models have shown that physical exercise is capable of improving neurogenesis in adult subjects, alleviating cognitive impairment and depression. Here, we show that there is a reduction in the generation of newborn granule cells in the dentate gyrus of adult rats subjected to a chronic model of epilepsy during the postnatal period of brain development. We also show that the physical exercise was capable to restore the number of newborn granule cells in this animals to the level observed in the control group. Notably, a larger number of newborn granule cells exhibiting morphological characteristics indicative of correct targeting into the hippocampal circuitry and the absence of basal dendrite projections was also observed in the epileptic animals subjected to physical exercise compared to the epileptic animals. The results described here could represent a positive interference of the physical exercise on the neurogenesis process in subjects with chronic epilepsy. The results may also help to reinterpret the benefits of the physical exercise in alleviating symptoms of depression and cognitive dysfunction.Fapemig (Fundacao de Amparo a Pesquisa do Estado de Minas Gerais)CNPq (Conselho Nacional de Desenvolvimento Cientifico e Tecnologico)FAPESP (Fundacao de Amparo a Pesquisa do Estado de Sao Paulo)INNT (Instituto Nacional de Neurociencia Translacional)CAPES (Coordenacao de Aperfeicoamento de Pessoal de Nivel Superior)Univ Fed Sao Joao del Rei, Dept Engn Biossistemas, Lab Neurociencia Expt Computac, Sao Joao Del Rei, BrazilHosp Israelita Albert Einstein, Inst Cerebro, Sao Paulo, BrazilUniv Mogi das Cruzes, Nucleo Pesquisas Tecnol, Mogi Das Cruzes, BrazilUniv Fed Sao Paulo UNIFESP, Dept Fisiol, Escola Paulista Med, Sao Paulo, BrazilUniv Fed Sao Paulo UNIFESP, Disciplina Neurol Expt, Escola Paulista Med, Sao Paulo, BrazilUniv Fed Sao Paulo UNIFESP, Dept Fisiol, Escola Paulista Med, Sao Paulo, BrazilUniv Fed Sao Paulo UNIFESP, Disciplina Neurol Expt, Escola Paulista Med, Sao Paulo, BrazilWeb of Scienc

Automatic diagnosis of the 12-lead ECG using a deep neural network

The role of automatic electrocardiogram (ECG) analysis in clinical practice is limited by the accuracy of existing models. Deep Neural Networks (DNNs) are models composed of stacked transformations that learn tasks by examples. This technology has recently achieved striking success in a variety of task and there are great expectations on how it might improve clinical practice. Here we present a DNN model trained in a dataset with more than 2 million labeled exams analyzed by the Telehealth Network of Minas Gerais and collected under the scope of the CODE (Clinical Outcomes in Digital Electrocardiology) study. The DNN outperform cardiology resident medical doctors in recognizing 6 types of abnormalities in 12-lead ECG recordings, with F1 scores above 80% and specificity over 99%. These results indicate ECG analysis based on DNNs, previously studied in a single-lead setup, generalizes well to 12-lead exams, taking the technology closer to the standard clinical practice

Left ventricular systolic dysfunction predicted by artificial intelligence using the electrocardiogram in Chagas disease patients-The SaMi-Trop cohort

BACKGROUND: Left ventricular systolic dysfunction (LVSD) in Chagas disease (ChD) is relatively common and its treatment using low-cost drugs can improve symptoms and reduce mortality. Recently, an artificial intelligence (AI)-enabled ECG algorithm showed excellent accuracy to detect LVSD in a general population, but its accuracy in ChD has not been tested. OBJECTIVE: To analyze the ability of AI to recognize LVSD in patients with ChD, defined as a left ventricular ejection fraction determined by the Echocardiogram ≤ 40%. METHODOLOGY/PRINCIPAL FINDINGS: This is a cross-sectional study of ECG obtained from a large cohort of patients with ChD named São Paulo-Minas Gerais Tropical Medicine Research Center (SaMi-Trop) Study. The digital ECGs of the participants were submitted to the analysis of the trained machine to detect LVSD. The diagnostic performance of the AI-enabled ECG to detect LVSD was tested using an echocardiogram as the gold standard to detect LVSD, defined as an ejection fraction <40%. The model was enriched with NT-proBNP plasma levels, male sex, and QRS ≥ 120ms. Among the 1,304 participants of this study, 67% were women, median age of 60; there were 93 (7.1%) individuals with LVSD. Most patients had major ECG abnormalities (59.5%). The AI algorithm identified LVSD among ChD patients with an odds ratio of 63.3 (95% CI 32.3-128.9), a sensitivity of 73%, a specificity of 83%, an overall accuracy of 83%, and a negative predictive value of 97%; the AUC was 0.839. The model adjusted for the male sex and QRS ≥ 120ms improved the AUC to 0.859. The model adjusted for the male sex and elevated NT-proBNP had a higher accuracy of 0.89 and an AUC of 0.874. CONCLUSION: The AI analysis of the ECG of Chagas disease patients can be transformed into a powerful tool for the recognition of LVSD