Early detection and diagnosis are critical factors to control the COVID-19 spreading. A number of deep learning-based methodologies have been recently proposed for COVID-19 screening in CT scans as a tool to automate and help with the diagnosis. These approaches, however, suffer from at least one of the following problems: (i) they treat each CT scan slice independently and (ii) the methods are trained and tested with sets of images from the same dataset. Treating the slices independently means that the same patient may appear in the training and test sets at the same time which may produce misleading results. It also raises the question of whether the scans from the same patient should be evaluated as a group or not. Moreover, using a single dataset raises concerns about the generalization of the methods. Different datasets tend to present images of varying quality which may come from different types of CT machines reflecting the conditions of the countries and cities from where they come from. In order to address these two problems, in this work, we propose an Efficient Deep Learning Technique for the screening of COVID-19 with a voting-based approach. In this approach, the images from a given patient are classified as group in a voting system. The approach is tested in the two biggest datasets of COVID-19 CT analysis with a patient-based split. A cross dataset study is also presented to assess the robustness of the models in a more realistic scenario in which data comes from different distributions. The cross-dataset analysis has shown that the generalization power of deep learning models is far from acceptable for the task since accuracy drops from 87.68% to 56.16% on the best evaluation scenario. These results highlighted that the methods that aim at COVID-19 detection in CT-images have to improve significantly to be considered as a clinical option and larger and more diverse datasets are needed to evaluate the methods in a realistic scenario.

Purpose Confronting the pandemic of COVID-19 is nowadays one of the most prominent challenges of the human species. A key factor in slowing down the virus propagation is the rapid diagnosis and isolation of infected patients. The standard method for COVID-19 identification, the Reverse transcription polymerase chain reaction method, is time-consuming and in short supply due to the pandemic. Thus, researchers have been looking for alternative screening methods, and deep learning applied to chest X-rays of patients has been showing promising results. Despite their success, the computational cost of these methods remains high, which imposes difficulties to their accessibility and availability. Thus, the main goal of this work is to propose an accurate yet efficient method in terms of memory and processing time for the problem of COVID-19 screening in chest X-rays. Methods To achieve the defined objective, we propose a new family of models based on the EfficientNet family of deep artificial neural networks which are known for their high accuracy and low footprints. We also exploit the underlying taxonomy of the problem with a hierarchical classifier. A dataset of 13,569 X-ray images divided into healthy, non-COVID-19 pneumonia, and COVID-19 patients is used to train the proposed approaches and other 5 competing architectures. We also propose a cross-dataset evaluation with a second dataset to evaluate the method generalization power. Results The results show that the proposed approach was able to produce a high-quality model, with an overall accuracy of 93.9%, COVID-19 sensitivity of 96.8%, and positive prediction of 100% while having from 5 to 30 times fewer parameters than the other tested architectures. Larger and more heterogeneous databases are still needed for validation before claiming that deep learning can assist physicians in the task of detecting COVID-19 in X-ray images, since the cross-dataset evaluation shows that even state-of-the-art models suffer from a lack of generalization power. Conclusions We believe the reported figures represent state-of-the-art results, both in terms of efficiency and effectiveness, for the COVIDx database, a database of 13,800 X-ray images, 183 of which are from patients affected by COVID-19. The current proposal is a promising candidate for embedding in medical equipment or even physicians' mobile phones.

The aim of present study was to examine the relationships between serum and salivary values of free testosterone, dehydroepiandrosterone, and cortisol before and after a session of resistance exercise. Twenty-eight healthy men (mean age 40 years, s = 4) participated in the present study. Serum and salivary samples were collected at rest and after a multiple-sets resistance exercise protocol, of approximately 25 minutes duration. Concentrations of free testosterone, dehydroepiandrosterone, and cortisol were measured using radioimmunoassay kits. No significant correlation was observed between serum free testosterone and salivary testosterone (r = 0.22 to 0.26, P > 0.05). Serum cortisol was significantly correlated with salivary cortisol before (r = 0.52, P = 0.005) and after (r = 0.62, P = 0.001) the exercise protocol. Serum and salivary concentrations of dehydroepiandrosterone were significantly correlated before (r = 0.68, P < 0.001) and after (r = 0.7, P < 0.001) exercise. The results of the present study suggest that even under exercise conditions, the salivary values of cortisol and dehydroepiandrosterone can reflect the behaviour of these hormones in blood. However, further studies are necessary to verify if salivary testosterone reflects the behaviour of serum free testosterone during resistance exercise.

The objective of this study was to detect C. difficile A/B toxins and to isolate strains of C. perfringens and C. difficile from diarrheic and non-diarrheic dogs in Brazil. Stool samples were collected from 57 dogs, 35 of which were apparently healthy, and 22 of which were diarrheic. C. difficile A/B toxins were detected by ELISA, and C. perfringens and C. difficile were identified by multiplex PCR. C. difficile A/B toxins were detected in 21 samples (36.8%). Of these, 16 (76.2%) were from diarrheic dogs, and five (23.8%) were from non-diarrheic dogs. Twelve C. difficile strains (21.1%) were isolated, of which ten were A+B+ and two were A−B−. All non-toxigenic strains were isolated from non-diarrheic animals. The binary toxin gene cdtB was found in one strain, which was A+B+ and was derived from a non-diarrheic dog. C. perfringens strains were isolated from 40 samples (70.2%). Of these, 18 (45%) were from the diarrheic group, and 22 (55%) belonged to the non-diarrheic group. All isolates were classified as C. perfringens type A and there was an association between the detection of the cpe gene and the presence of diarrhea. Interestingly, ten strains (25%) were positive for the presence of the cpb2 gene. The high rate of detection of the A/B toxins in non-diarrheic dogs suggests the occurrence of subclinical disease in dogs or carriage of its toxins without disease. More studies are needed to elucidate the epidemiology of C. difficile and C. perfringens in dogs and to better our understanding of C. difficile as a zoonotic agent. This is the first study to report the binary toxin gene in C. difficile strains isolated from dogs in Brazil.

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