Covid-19 rapid test by combining a Random Forest-based web system and blood tests.

The disease caused by the new type of coronavirus, Covid-19, has posed major public health challenges for many countries. With its rapid spread, since the beginning of the outbreak in December 2019, the disease transmitted by SARS-CoV-2 has already caused over 2 million deaths to date. In this work, we propose a web solution, called Heg.IA, to optimize the diagnosis of Covid-19 through the use of artificial intelligence. Our system aims to support decision-making regarding to diagnosis of Covid-19 and to the indication of hospitalization on regular ward, semi-ICU or ICU based on decision a Random Forest architecture with 90 trees. The main idea is that healthcare professionals can insert 41 hematological parameters from common blood tests and arterial gasometry into the system. Then, Heg.IA will provide a diagnostic report. The system reached good results for both Covid-19 diagnosis and to recommend hospitalization. For the first scenario we found average results of accuracy of 92.891%±0.851, kappa index of 0.858 ± 0.017, sensitivity of 0.936 ± 0.011, precision of 0.923 ± 0.011, specificity of 0.921 ± 0.012 and area under ROC of 0.984 ± 0.003. As for the indication of hospitalization, we achieved excellent performance of accuracies above 99% and more than 0.99 for the other metrics in all situations. By using a computationally simple method, based on the classical decision trees, we were able to achieve high diagnosis performance. Heg.IA system may be a way to overcome the testing unavailability in the context of Covid-19.Communicated by Ramaswamy H. Sarma.

Automatic system for analysis of locomotor activity in rodents--a reproducibility study.

Automatic analysis of locomotion in studies of behavior and development is of great importance because it eliminates the subjective influence of evaluators on the study. This study aimed to develop and test the reproducibility of a system for automated analysis of locomotor activity in rats. For this study, 15 male Wistar were evaluated at P8, P14, P17, P21, P30 and P60. A monitoring system was developed that consisted of an open field of 1m in diameter with a black surface, an infrared digital camera and a video capture card. The animals were filmed for 2 min as they moved freely in the field. The images were sent to a computer connected to the camera. Afterwards, the videos were analyzed using software developed using MATLAB® (mathematical software). The software was able to recognize the pixels constituting the image and extract the following parameters: distance traveled, average speed, average potency, time immobile, number of stops, time spent in different areas of the field and time immobile/number of stops. All data were exported for further analysis. The system was able to effectively extract the desired parameters. Thus, it was possible to observe developmental changes in the patterns of movement of the animals. We also discuss similarities and differences between this system and previously described systems.