Introduction: The rapid global dissemination of COVID-19 culminated in the mobilization of great technological efforts aimed at its better understanding and control. In this context, Machine Learning gains notoriety, and its application has been widely documented for pathophysiological, diagnostic, therapeutic, prognostic and monitoring of COVID-19 purposes. The present study aimed to build a model for the prediction of the diagnosis of COVID-19 based on blood count results and age of patients and to identify the main characteristics taken into account by the algorithm for the predictive decision.Material and Methods: Anonymous data from 1157 patients made available by the COVID-19 Data Sharing / BR repository were used. The work took place in two distinct stages: description and analysis of the data; and construction of the predictive model. Results: With the exception of hemoglobin measurement, mean corpuscular volume, red cell distribution width, mean platelet volume and neutrophil-lymphocyte ratio, there was a statistically significant association of all other hematological parameters assessed with COVID-19. The predictive model developed from the XGBoost classifier reached an accuracy of 80.0% with a sensitivity of 75.6% and specificity of 82.0%. The variables that had the greatest influence on the predictive decision were basophil, eosinophil and leukocyte measurements. The present study confirms the potential of using blood count results, a widely available and accessible test, in the context of the diagnostic evaluation and pathophysiological investigation of COVID-19.Conclusion: This work highlights the relevance of the systematization and dissemination of data related to COVID-19 for use in new research.
Introduction: Seizure is a transient phenomenon with genesis in excessive abnormal or synchronous neuronal electrical activity in the brain, while epilepsy is defined as a brain dysfunction characterized by persistent predisposition to generate seizures. The identification of epileptogenic electroencephalographic patterns can be performed using machine learning.the present study aimed to develop a transfer learning based classifier able to detect epileptic seizures in images generated from electroencephalographic data graphic representation.Material and Methods: We used the Epileptic Seizure Recognition Data Set,which consists of 500 brain activity records for 23.6 seconds comprising 23 chunks of 178 data points, and transformed the resulting 11500 instances into images by graphically plotting its data points. Those images were then splitted in training and test set and used to build and assess, respectvely, a transfer learning-based deep neural network, which classified the images according the presence or absence of epileptic seizures.Results: The model achieved 100% accuracy, sensitivity and specificity, with a AUC-score of 1.0, demonstrating the great potential of transfer learning for the analysis of graphically represented electroencephalographic data.Conclusion: It is opportune to raise new studies involving transfer learning for the analysis of signal data, with the aim of improving, disseminating and validating its use for daily clinical practice.
Background: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the etiologic agent of coronavirus disease 2019 , triggers a pathophysiological process linked not only to viral mechanisms of infectivity, but also to the pattern of host response. Drug repurposing is a promising strategy for rapid identification of treatments for SARS-CoV-2 infection, and several attractive molecular viral targets can be exploited. Among those, 3CL protease is a potential target of great interest. Objective:The objective of the study was to screen potential 3CL pro inhibitors compounds based on chemical fingerprints among anti-inflammatory, anticoagulant, and respiratory system agents. Methods: The screening was developed based on a drug property prediction framework, in which the evaluated property was the ability to inhibit the activity of the 3CL pro protein, and the predictions were performed using a dense neural network trained and validated on bioassay data. Results: On the validation and test set, the model obtained area under the curve values of 98.2 and 76.3, respectively, demonstrating high specificity for both sets (98.5% and 94.7%). Regarding the 1278 compounds screened, the model indicated four anti-inflammatory agents, two anticoagulants, and one respiratory agent as potential 3CL pro inhibitors. Conclusions: Those findings point to a possible desirable synergistic effect in the management of patients with COVID-19 and provide potential directions for in vitro and in vivo research, which are indispensable for the validation of their results. (REV INVEST CLIN. [AHEAD OF PRINT])
Introdução: A encefalite viral é uma condição com altas taxas de morbimortalidade, e um melhor entendimento de sua epidemiologia pode colaborar para a construção de estratégias de prevenção e controle. Diante disso, este estudo se propôs a traçar um perfil epidemiológico para a encefalite viral no Brasil no ano de 2018 a partir de dados de internações hospitalares no Sistema Único de Saúde (SUS). Métodos: Estudo ecológico de análise espacial. Os dados estudados foram relativos às internações hospitalares por encefalite viral no SUS em 2018, estratificadas por unidade da federação (UF), sexo e faixa etária. A distribuição geográfica foi abordada exploratoriamente, já as variáveis sexo e faixa etária foram abordadas analiticamente. Resultados: Foram registradas 2075 internações, com taxa de 0,99/105 habitantes. As taxas para cada UF foram representadas a partir de um mapa colorimétrico, enquanto as taxas para cada sexo e faixa etária foram representadas em uma tabela comparativa univariada. Discussão: Observou-se ampla variação numérica das taxas de internação dentre as UF, sendo Pernambuco o estado com maior incidência (4,13/105 habitantes) e Paraíba o estado com menor (0,29/105 habitantes). Foi constatada associação significativa com risco de internação hospitalar por encefalite viral para o sexo masculino e para as faixas etárias de 1 a 4 anos (RR: 3,28) e menores de 1 ano (RR: 6,02). Conclusão: UF, gênero e faixa etária foram determinantes importantes da taxa de internação hospitalar por encefalite viral. Todavia, carecem de estudos atuais no Brasil e no mundo para a melhor caracterização da epidemiologia da encefalite viral.
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