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Even with over 80% of the population being vaccinated against COVID-19, the disease continues to claim victims. Therefore, it is crucial to have a secure Computer-Aided Diagnostic system that can assist in identifying COVID-19 and determining the necessary level of care. This is especially important in the Intensive Care Unit to monitor disease progression or regression in the fight against this epidemic. To accomplish this, we merged public datasets from the literature to train lung and lesion segmentation models with five different distributions. We then trained eight CNN models for COVID-19 and Common-Acquired Pneumonia classification. If the examination was classified as COVID-19, we quantified the lesions and assessed the severity of the full CT scan. To validate the system, we used Resnetxt101 Unet++ and Mobilenet Unet for lung and lesion segmentation, respectively, achieving accuracy of 98.05%, F1-score of 98.70%, precision of 98.7%, recall of 98.7%, and specificity of 96.05%. This was accomplished in just 19.70 s per full CT scan, with external validation on the SPGC dataset. Finally, when classifying these detected lesions, we used Densenet201 and achieved accuracy of 90.47%, F1-score of 93.85%, precision of 88.42%, recall of 100.0%, and specificity of 65.07%. The results demonstrate that our pipeline can correctly detect and segment lesions due to COVID-19 and Common-Acquired Pneumonia in CT scans. It can differentiate these two classes from normal exams, indicating that our system is efficient and effective in identifying the disease and assessing the severity of the condition.

Measurement uncertainty is one of the widespread concepts applied in scientific works, particularly to estimate the accuracy of measurement results and to evaluate the conformity of products and processes. In this work, we propose a methodology to analyze the performance of measurement systems existing in the design phases, based on a probabilistic approach, by applying the Monte Carlo method (MCM). With this approach, it is feasible to identify the dominant contributing factors of imprecision in the evaluated system. In the design phase, this information can be used to identify where the most effective attention is required to improve the performance of equipment. This methodology was applied over a simulated electrocardiogram (ECG), for which a measurement uncertainty of the order of 3.54% of the measured value was estimated, with a confidence level of 95%. For this simulation, the ECG computational model was categorized into two modules: the preamplifier and the final stage. The outcomes of the analysis show that the preamplifier module had a greater influence on the measurement results over the final stage module, which indicates that interventions in the first module would promote more significant performance improvements in the system. Finally, it was identified that the main source of ECG measurement uncertainty is related to the measurand, focused towards the objective of better characterization of the metrological behavior of the measurements in the ECG.

Dairy cattle are particularly sensitive to heat stress due to the higher metabolic rate needed for milk production. In recent decades, global warming and the increase in dairy production in warmer countries have stimulated the development of a wide range of environmental control systems for dairy farms. Despite their proven effectiveness, the associated energy and water consumption can compromise the viability of dairy farms in many regions, due to the cost and scarcity of these resources. To make these systems more efficient, they should be activated in time to prevent thermal stress and switched off when that risk no longer exists, which must consider environmental variables as well as the variables of the animals themselves. Nowadays, there is a wide range of sensors and equipment that support farm routine procedures, and it is possible to measure several variables that, with the aid of algorithms based on predictive models, would allow anticipating animals' thermal states. This review summarizes three types of approaches as predictive models: bioclimatic indexes, machine learning, and mechanistic models. It also focuses on the application of the current knowledge as algorithms to be used in the management of diverse types of environmental control systems.

Background: An outbreak of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) Omicron variant was detected in the psychiatric ward of a general hospital in Brasília, Brazil., Methods: We report the investigation, clinical outcomes, viral sequencing, and control measures applied to outbreak containment., Results: The overall attack rate was 95% (23/24) in a period of 13 days. Among the cases, 78% (18/23) were vaccinated and 17% (4/23) required intensive care. The Omicron variant was isolated from the 19 sequenced samples., Conclusions: The findings highlight the potential harm that highly transmissible variants may generate among hospitalized populations, particularly those with comorbidities.

Background: Studies have shown that spiritual/religious beliefs are associated with mental health and health-related quality of life (HRQoL). However, few studies evaluated how spiritual/religious coping (SRC) could affect hemodialysis patients., Objectives: The present study investigated the role of SRC behaviors on HRQoL and depressive symptoms in hemodialysis patients., Design and Participants: This was cross-sectional study with 184 patients. Patients completed the Beck Depression Inventory, Brief SRC Scale, Medical Outcomes Study 36-Item Short-Form Health Survey (SF-36), and a Sociodemographic and Health Characterization Questionnaire., Results: From 218 patients, 184 (84.4%) were included (53.8% male with a median age of 55.9 years). Negative SRC, but not positive SRC, was associated with depressive symptoms. Positive SRC presented significant effects in SF-36 pain and physical and social functioning. On the other hand, negative SRC exhibited significant effects in SF-36 role emotional, energy/fatigue, pain, and physical functioning., Conclusion: SRC influences the mental health and HRQoL in Brazilian hemodialysis patients in two distinct ways. If used positively, it may have positive outcomes. However, if used negatively, it may lead to dysfunctional consequences such as greater depressive symptomatology and affect HRQoL. Health professionals must be aware of these "two sides of the same coin."

We investigated the relation between swimming exercise and fear memory extinction. Rats that performed regular swimming exercise over 6 wk underwent fear conditioning. Twenty-eight days later, they were submitted to extinction tests. Swimming rats had enhanced extinction process throughout the 5 days of the extinction test compared with sedentary rats. This suggests that the swimming exercise accelerated the process of aversive memory extinction, reducing the expression of conditioned fear behavior. These results encourage further studies addressing the anxiolytic effects of exercise, with potential implications for anxiety disorders such as posttraumatic stress disorder. NEW & NOTEWORTHY We have shown that rats that performed regular swimming exercise over 6 wk had enhanced extinction process compared with sedentary animals. The swimming exercise may accelerate the process of aversive memory extinction, reducing the expression of conditioned fear behavior.

This aim of this study was assess whether positive and negative spiritual/religious coping (SRC) strategies are associated with depressive symptoms (DS) of informal caregiver (IC) of hospitalized older adults. A cross-sectional study was conducted among 98 IC of hospitalized older adults in the medical clinic of a Brazilian School Hospital. The functionality, Positive and Negative SRC strategies and DS were evaluated. The IC had high average use of SRC, with Positive SRC being more used than Negative SRC. In the unadjusted regression model, Positive and Negative SRC were associated with DS. However, when adjusted for confounding factors, only Negative SRC remained associated with DS. The IC had used their religious and spiritual beliefs as a way to cope with the stress that comes from caring for hospitalized older adults. Although the positive strategy use of these beliefs was more common, only the negative strategies were associated with a higher DS., (Copyright © 2017 Elsevier Inc. All rights reserved.)

Although bioactive glasses are successfully used as bone substitutes, recent studies have revealed that the high alkali content in these glasses leads to fast in vivo degradation rates that may not match the rate of new bone ingrowth. This prompted us to design and develop novel bioactive glasses that are devoid of alkali but still demonstrate high bioactivity in vitro. This article describes the in vivo performance of an alkali-free bioactive glass with the following composition (Wt %): 13.03 MgO-33.98 CaO-13.37 P 2 O 5 -38.84 SiO 2 -0.77 CaF 2 (labelled as FastOs ® BG). An animal model was used to assess the in vivo performance of FastOs ® BG, using 45S5 Bioglass ® as control. The evaluation was performed through implantation of FastOs ® BG and 45S5 Bioglass ® , during one month, in femoral bone defects in sheep. Subcutaneous implantation of both glasses was also performed in order to assess tissue response through a standardized method. Histological and scanning electron microscopy assessment of retrieved subcutaneous and bone samples demonstrated that FastOs ® BG is biocompatible, osteoconductive, that it can be osteointegrated, and that it is more slowly resorbed than 45S5 Bioglass ® . These features suggest that FastOs ® BG is a potential candidate for bone grafting. © 2015 Wiley Periodicals, Inc. J Biomed Mater Res Part B: Appl Biomater, 105B: 30-38, 2017., (© 2015 Wiley Periodicals, Inc.)

The World Health Organization estimates that 300 million people have asthma, 210 million people have Chronic Obstructive Pulmonary Disease (COPD), and, according to WHO, COPD will become the third major cause of death worldwide in 2030. Computational Vision systems are commonly used in pulmonology to address the task of image segmentation, which is essential for accurate medical diagnoses. Segmentation defines the regions of the lungs in CT images of the thorax that must be further analyzed by the system or by a specialist physician. This work proposes a novel and powerful technique named 3D Adaptive Crisp Active Contour Method (3D ACACM) for the segmentation of CT lung images. The method starts with a sphere within the lung to be segmented that is deformed by forces acting on it towards the lung borders. This process is performed iteratively in order to minimize an energy function associated with the 3D deformable model used. In the experimental assessment, the 3D ACACM is compared against three approaches commonly used in this field: the automatic 3D Region Growing, the level-set algorithm based on coherent propagation and the semi-automatic segmentation by an expert using the 3D OsiriX toolbox. When applied to 40 CT scans of the chest the 3D ACACM had an average F-measure of 99.22%, revealing its superiority and competency to segment lungs in CT images., (Copyright © 2016 Elsevier B.V. All rights reserved.)

Active contours are image segmentation methods that minimize the total energy of the contour to be segmented. Among the active contour methods, the radial methods have lower computational complexity and can be applied in real time. This work aims to present a new radial active contour technique, called pSnakes, using the 1D Hilbert transform as external energy. The pSnakes method is based on the fact that the beams in ultrasound equipment diverge from a single point of the probe, thus enabling the use of polar coordinates in the segmentation. The control points or nodes of the active contour are obtained in pairs and are called twin nodes. The internal energies as well as the external one, Hilbertian energy, are redefined. The results showed that pSnakes can be used in image segmentation of short-axis echocardiogram images and that they were effective in image segmentation of the left ventricle. The echo-cardiologist's golden standard showed that the pSnakes was the best method when compared with other methods. The main contributions of this work are the use of pSnakes and Hilbertian energy, as the external energy, in image segmentation. The Hilbertian energy is calculated by the 1D Hilbert transform. Compared with traditional methods, the pSnakes method is more suitable for ultrasound images because it is not affected by variations in image contrast, such as noise. The experimental results obtained by the left ventricle segmentation of echocardiographic images demonstrated the advantages of the proposed model. The results presented in this paper are justified due to an improved performance of the Hilbert energy in the presence of speckle noise., (Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.)

Background: Sickle cell disease is the most common monogenic hereditary disease in Brazil. Although strokes are one of the main causes of morbidity and mortality in these patients, the use of transcranial Doppler to identify children at risk is not universally used., Objective: To develop Brazilian guidelines for the use of transcranial Doppler in sickle cell disease children and adolescents, so that related health policies can be expanded, and thus contribute to reduce morbidity and mortality., Methods: The guidelines were formulated in a consensus meeting of experts in transcranial Doppler and sickle cell disease. The issues discussed were previously formulated and scientific articles in databases (MEDLINE, SciELO and Cochrane) were carefully analyzed. The consensus for each question was obtained by a vote of experts on the specific theme., Results: Recommendations were made, including indications for the use of transcranial Doppler according to the sickle cell disease genotype and patients age; the necessary conditions to perform the exam and its periodicity depending on exam results; the criteria for the indication of blood transfusions and iron chelation therapy; the indication of hydroxyurea; and the therapeutic approach in cases of conditional transcranial Doppler., Conclusion: The Brazilian guidelines on the use of transcranial doppler in sickle cell disease patients may reduce the risk of strokes, and thus reduce the morbidity and mortality and improve the quality of life of sickle cell disease patients.

Objective: To present a locally developed system of computer vision for use with HRCT images, designated SIStema para a Detecção e a quantificação de Enfisema Pulmonar (SISDEP, System to Detect and Quantify Pulmonary Emphysema), and to compare this system with a freeware system tool., Methods: Thirty-three HRCT images scanned at the apex, hilum and base of the lungs of 11 patients with COPD were analyzed. The SISDEP was compared with the Osiris Medical Imaging Software Program regarding lung parenchyma segmentation, precision of the measurement of the cross-sectional area of the lungs in mm(2), mean lung density (MLD), relative area (RA) of the lung occupied by voxels with attenuation values < -950 hounsfield units (RA -950), 15th percentile point (Perc15) and visualization of hyperinflated areas using a color mask., Results: Although both computational systems were efficient in segmenting the lungs, the SISDEP performed this task automatically and more rapidly. There were significant correlations between the two systems in terms of the results obtained for lung cross-sectional area, MLD, RA -950 and Perc15 (r(2) = 0.99, 0.99, 0.99 and 1.00, respectively). The color mask tool of the SISDEP allowed excellent visualization of hyperinflated areas, discriminating them from normal areas., Conclusions: The SISDEP was efficient in segmenting the lungs and quantifying lung hyperinflation, presenting an excellent correlation with the Osiris system. The SISDEP constitutes a promising computational tool for diagnosing and assessing the progression of emphysema in HRCT images of COPD patients.