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We investigated the magnitude of exercise-induced changes in muscular bioenergetics, redox balance, mitochondrial function, and gene expression within 24 h after the exercise bouts performed with different intensities, durations, and execution modes (continuous or with intervals). Sixty-five male Swiss mice were divided into four groups: one control (n = 5) and three experimental groups (20 animals/group), submitted to a forced swimming bout with an additional load (% of animal weight): low-intensity continuous (LIC), high-intensity continuous (HIC), and high-intensity interval (HII). Five animals from each group were euthanized at 0 h, 6 h, 12 h, and 24 h postexercise. Gastrocnemius muscle was removed to analyze the expression of genes involved in mitochondrial biogenesis (Ppargc1a), fusion (Mfn2), fission (Dnm1L), and mitophagy (Park2), as well as inflammation (Nos2) and antioxidant defense (Nfe2l2, GPx1). Lipid peroxidation (TBARS), total peroxidase, glutathione peroxidase (GPx), and citrate synthase (CS) activity were also measured. Lactacidemia was measured from a blood sample obtained immediately postexercise. Lactacidemia was higher the higher the exercise intensity (LIC < HIC < HII), while the inverse was observed for TBARS levels. The CS activity was higher in the HII group than the other groups. The antioxidant activity was higher 24 h postexercise in all groups compared to the control and greater in the HII group than the LIC and HIC groups. The gene expression profile exhibited a particular profile for each exercise protocol, but with some similarities between the LIC and HII groups. Taken together, these results suggest that the intervals applied to high-intensity exercise seem to minimize the signs of oxidative damage and drive the mitochondrial dynamics to maintain the mitochondrial network, similar to low-intensity continuous exercise.

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The projected burden of dementia by Alzheimer's disease (AD) represents a looming healthcare crisis as the population of most countries grows older. Although there is currently no cure, it is possible to treat symptoms of dementia. Early diagnosis is paramount to the development and success of interventions, and neuroimaging represents one of the most promising areas for early detection of AD. We aimed to deploy advanced deep learning methods to determine whether they can extract useful AD biomarkers from structural magnetic resonance imaging (sMRI) and classify brain images into AD, mild cognitive impairment (MCI), and cognitively normal (CN) groups. We tailored and trained Convolutional Neural Networks (CNNs) on sMRIs of the brain from datasets available in online databases. Our proposed method, ADNet, was evaluated on the CADDementia challenge and outperformed several approaches in the prior art. The method's configuration with machine-learning domain adaptation, ADNet-DA, reached 52.3% accuracy. Contributions of our study include devising a deep learning system that is entirely automatic and comparatively fast, presenting competitive results without using any patient's domain-specific knowledge about the disease. We were able to implement an end-to-end CNN system to classify subjects into AD, MCI, or CN groups, reflecting the identification of distinctive elements in brain images. In this context, our system represents a promising tool in finding biomarkers to help with the diagnosis of AD and, eventually, many other diseases.

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Diabetic Retinopathy (DR) is a complication of diabetes mellitus that affects more than one-quarter of the population with diabetes, and can lead to blindness if not discovered in time. An automated screening enables the identification of patients who need further medical attention. This study aimed to classify retinal images of Aboriginal and Torres Strait Islander peoples utilizing an automated computer-based multi-lesion eye screening program for diabetic retinopathy. The multi-lesion classifier was trained on 1,014 images from the São Paulo Eye Hospital and tested on retinal images containing no DR-related lesion, single lesions, or multiple types of lesions from the Inala Aboriginal and Torres Strait Islander health care centre. The automated multi-lesion classifier has the potential to enhance the efficiency of clinical practice delivering diabetic retinopathy screening. Our program does not necessitate image samples for training from any specific ethnic group or population being assessed and is independent of image pre- or post-processing to identify retinal lesions. In this Aboriginal and Torres Strait Islander population, the program achieved 100% sensitivity and 88.9% specificity in identifying bright lesions, while detection of red lesions achieved a sensitivity of 67% and specificity of 95%. When both bright and red lesions were present, 100% sensitivity with 88.9% specificity was obtained. All results obtained with this automated screening program meet WHO standards for diabetic retinopathy screening.

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Diabetic Retinopathy (DR) is a complication of diabetes that can lead to blindness if not readily discovered. Automated screening algorithms have the potential to improve identification of patients who need further medical attention. However, the identification of lesions must be accurate to be useful for clinical application. The bag-of-visual-words (BoVW) algorithm employs a maximum-margin classifier in a flexible framework that is able to detect the most common DR-related lesions such as microaneurysms, cotton-wool spots and hard exudates. BoVW allows to bypass the need for pre- and post-processing of the retinographic images, as well as the need of specific ad hoc techniques for identification of each type of lesion. An extensive evaluation of the BoVW model, using three large retinograph datasets (DR1, DR2 and Messidor) with different resolution and collected by different healthcare personnel, was performed. The results demonstrate that the BoVW classification approach can identify different lesions within an image without having to utilize different algorithms for each lesion reducing processing time and providing a more flexible diagnostic system. Our BoVW scheme is based on sparse low-level feature detection with a Speeded-Up Robust Features (SURF) local descriptor, and mid-level features based on semi-soft coding with max pooling. The best BoVW representation for retinal image classification was an area under the receiver operating characteristic curve (AUC-ROC) of 97.8% (exudates) and 93.5% (red lesions), applying a cross-dataset validation protocol. To assess the accuracy for detecting cases that require referral within one year, the sparse extraction technique associated with semi-soft coding and max pooling obtained an AUC of 94.2 ± 2.0%, outperforming current methods. Those results indicate that, for retinal image classification tasks in clinical practice, BoVW is equal and, in some instances, surpasses results obtained using dense detection (widely believed to be the best choice in many vision problems) for the low-level descriptors.

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Introdução: O câncer é uma doença crônica não transmissível que provoca, anualmente, 7 milhões de óbitos em todo o mundo. A avaliação nutricional de pacientes oncológicos é de suma importância, dada a grandeza dos problemas nutricionais que essa enfermidade pode ocasionar, interferindo de modo impactante no prognóstico da doença. Objetivo: Avaliar o perfil nutricional de pacientes com câncer assistidos pela Casa de Acolhimento ao Paciente Oncológico do Sudoeste da Bahia, relacionando-o com o tipo de neoplasia. Método: Trata-se de um estudo transversal, realizado com 101 pacientes, no qual o seu estado nutricional foi verificado através de métodos antropométrico, subjetivo, dietético e laboratorial. Resultados: As medidas antropométricas sugerem que, pelo menos, um em cada cinco pacientes apresenta algum grau de desnutrição, enquanto os sintomas relacionados à doença e ou ao tratamento enquadram 42,6 por cento dos pacientes na classe moderadamente desnutrido da Avaliação Subjetiva Global Produzida pelo Paciente. A desnutrição mostrou-se presente, principalmente, nos pacientes com tumores de esôfago, cabeça e pescoço e pulmão e, à avaliação dietética, observou-se que mais da metade dos entrevistados consumia produtos de origem animal, gorduras e açúcares diariamente e vegetais semanalmente antes da descoberta da doença. Foram encontrados, principalmente, níveis séricos reduzidos de hemoglobina, ferro, albumina e linfócitos. Conclusão: Os resultados da pesquisa demonstram que os pacientes estudados apresentaram graus variados de deficiência nutricional e, assim, propõe-se que maior atenção seja destinada ao estado nutricional do paciente com câncer para que os déficits sejam corrigidos precocemente e as complicações ao quadro sejam evitadas.

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