Time-Course of Redox Status, Redox-Related, and Mitochondrial-Dynamics-Related Gene Expression after an Acute Bout of Different Physical Exercise Protocols.

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.

Interaction between cigarette smoke exposure and physical training on inflammatory and oxidative profile in mice muscle.

Regular physical training and cigarette smoke exposure (CSE) have opposite effects on physical performance, antioxidant, and inflammatory profile. However, the interaction between these events is not well studied. We aimed to investigate how regular physical training and CSE interact, and in what is the outcome of this interaction on the physical performance, skeletal muscle antioxidant defense and molecular profile response of pro and anti-inflammatory cytokines. Male C57BL/6 mice were randomly divided into 4 groups (n = 8/group): 1) Sedentary group (SED); 2) 4 weeks of control, followed by 4 weeks of CSE (SED + CSEG); 3) Physically active (PA) along 8 weeks (forced swim training, 5 times a week); 4) Physically active and exposed to the cigarette smoke (PA + CSEG), group submitted to forced swim training for 4 weeks, followed by 4 weeks of concomitant training and CSE. Physical performance was evaluated before and after the experimental period (8 weeks), total peroxidase and glutathione peroxidase (GPx) activities, expression of genes encoding TNF-α, MCP-1, IL1ß, IL-6, IL-10, TGF-ß, HO-1 and the TNF-α/IL-10 ratio were determined from gastrocnemius muscle at the end of experimental period. The CSE attenuated the aerobic capacity adaptation (time to exhaustion in swimming forced test) promoted by physical training and inhibit the improvement in local muscle resistance (inverted screen test). The regular physical training enhanced the antioxidant defense, but the CSE abrogated this benefit. The CSE induced a harmful pro-inflammatory profile in skeletal muscle from sedentary animals whereas the regular physical training induced an opposite adaptation. Likewise, the CSE abolished the protective effect of physical training. Together, these results suggest a negative effect of CSE including, at least in part, the inhibition/attenuation of beneficial adaptations from regular physical training.

Alzheimer's Disease Detection Through Whole-Brain 3D-CNN MRI.

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.

Hyperuricemia is associated with sympathovagal imbalance in older adults.

PURPOSE: This study aimed to compare heart rate variability (HRV) parameters obtained through symbolic analysis (SA), between older adults with and without hyperuricemia. METHODS: This is a cross-sectional study including 202 community-dwelling old adults, which was clinically stratified as with or without hyperuricemia, according to the cutoff point of serum uric acid ≥ 6 mg/dL for women and ≥ 7 mg/dL for men. Successive RR intervals were recorded along 5 min and analyzed with SA method. 0 V%, 1 V% and 2 V% patterns were quantified and compared between groups. Comparisons were carried out through parametric or nonparametric tests, according to the data distribution characteristics, evaluated by Kolmogorov-Smirnov test. The significance level was set as p ≤ 0.05 for all statistical procedures. RESULTS: The prevalence of hyperuricemia was 67.8 %, and the hyperuricemic older adults exhibited significant higher values for V0% and lower values for V2% parameters when compared to normouricemic older adults. CONCLUSION: These results suggesting a sympathovagal imbalance in hyperuricemic older adults, characterized by greater sympathetic predominance (0 V%) and lower vagal modulation (2 V%) at rest conditions.

A data-driven approach to referable diabetic retinopathy detection.

Prior art on automated screening of diabetic retinopathy and direct referral decision shows promising performance; yet most methods build upon complex hand-crafted features whose performance often fails to generalize. OBJECTIVE: We investigate data-driven approaches that extract powerful abstract representations directly from retinal images to provide a reliable referable diabetic retinopathy detector. METHODS: We gradually build the solution based on convolutional neural networks, adding data augmentation, multi-resolution training, robust feature-extraction augmentation, and a patient-basis analysis, testing the effectiveness of each improvement. RESULTS: The proposed method achieved an area under the ROC curve of 98.2% (95% CI: 97.4-98.9%) under a strict cross-dataset protocol designed to test the ability to generalize - training on the Kaggle competition dataset and testing using the Messidor-2 dataset. With a 5 × 2-fold cross-validation protocol, similar results are achieved for Messidor-2 and DR2 datasets, reducing the classification error by over 44% when compared to most published studies in existing literature. CONCLUSION: Additional boost strategies can improve performance substantially, but it is important to evaluate whether the additional (computation- and implementation-) complexity of each improvement is worth its benefits. We also corroborate that novel families of data-driven methods are the state of the art for diabetic retinopathy screening. SIGNIFICANCE: By learning powerful discriminative patterns directly from available training retinal images, it is possible to perform referral diagnostics without detecting individual lesions.

Beyond Lesion-Based Diabetic Retinopathy: A Direct Approach for Referral.

Diabetic retinopathy (DR) is the leading cause of blindness in adults, but can be managed if detected early. Automated DR screening helps by indicating which patients should be referred to the doctor. However, current techniques of automated screening still depend too much on the detection of individual lesions. In this study, we bypass lesion detection, and directly train a classifier for DR referral. Additional novelties are the use of state-of-the-art mid-level features for the retinal images: BossaNova and Fisher Vector. Those features extend the classical Bags of Visual Words and greatly improve the accuracy of complex classification tasks. The proposed technique for direct referral is promising, achieving an area under the curve of 96.4%, thus, reducing the classification error by almost 40% over the current state of the art, held by lesion-based techniques.

Automated multi-lesion detection for referable diabetic retinopathy in indigenous health care.

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.

Advancing bag-of-visual-words representations for lesion classification in retinal images.

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.

Automatic Diabetic Retinopathy detection using BossaNova representation.

The biomedical community has shown a continued interest in automated detection of Diabetic Retinopathy (DR), with new imaging techniques, evolving diagnostic criteria, and advancing computing methods. Existing state of the art for detecting DR-related lesions tends to emphasize different, specific approaches for each type of lesion. However, recent research has aimed at general frameworks adaptable for large classes of lesions. In this paper, we follow this latter trend by exploring a very flexible framework, based upon two-tiered feature extraction (low-level and mid-level) from images and Support Vector Machines. The main contribution of this work is the evaluation of BossaNova, a recent and powerful mid-level image characterization technique, which we contrast with previous art based upon classical Bag of Visual Words (BoVW). The new technique using BossaNova achieves a detection performance (measured by area under the curve - AUC) of 96.4% for hard exudates, and 93.5% for red lesions using a cross-dataset training/testing protocol.

Assessing the Need for Referral in Automatic Diabetic Retinopathy Detection.

Emerging technologies in health care aim at reducing unnecessary visits to medical specialists, minimizing overall cost of treatment and optimizing the number of patients seen by each doctor. This paper explores image recognition for the screening of diabetic retinopathy, a complication of diabetes that can lead to blindness if not discovered in its initial stages. Many previous reports on DR imaging focus on the segmentation of the retinal image, on quality assessment, and on the analysis of presence of DR-related lesions. Although this study has advanced the detection of individual DR lesions from retinal images, the simple presence of any lesion is not enough to decide on the need for referral of a patient. Deciding if a patient should be referred to a doctor is an essential requirement for the deployment of an automated screening tool for rural and remote communities. We introduce an algorithm to make that decision based on the fusion of results by metaclassification. The input of the metaclassifier is the output of several lesion detectors, creating a powerful high-level feature representation for the retinal images. We explore alternatives for the bag-of-visual-words (BoVW)-based lesion detectors, which critically depends on the choices of coding and pooling the low-level local descriptors. The final classification approach achieved an area under the curve of 93.4% using SOFT-MAX BoVW (soft-assignment coding/max pooling), without the need of normalizing the high-level feature vector of scores.

Perfil nutricional de pacientes com câncer assistidos pela casa de acolhimento ao paciente oncológico do sudoeste da Bahia / Nutrictional profile of cancer patients assisted by the house os support for cancer patients from the southwest of Bahia

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.