Effects of Exercise Training in Patients with Chronic Heart Failure and Sleep Apnea

Study Objectives: To test the effects of exercise training on sleep and neurovascular control in patients with systolic heart failure with and without sleep disordered breathing. Design: Prospective interventional study. Setting: Cardiac rehabilitation and exercise physiology unit and sleep laboratory. Patients: Twenty-five patients with heart failure, aged 42 to 70 years, and New York Heart Association Functional Class I-III were divided into 1 of 3 groups: obstructive sleep apnea (n = 8), central sleep apnea (n 9) and no sleep apnea (n = 7). Interventions: Four months of no-training (control) followed by 4 months of an exercise training program (three 60-minute, supervised, exercise sessions per week). Measures and Results: Sleep (polysomnography), microneurography, forearm blood flow (plethysmography), peak VO(2). and quality of life were evaluated at baseline and at the end of the control and trained periods. No significant changes occurred in the control period. Exercise training reduced muscle sympathetic nerve activity (P < 0.001) and increased forearm blood flow (P < 0.01), peak VO(2) (P < 0.01), and quality of life (P < 0.01) in all groups, independent of the presence of sleep apnea. Exercise training improved the apnea-hypopnea index, minimum O(2) saturation, and amount stage 3-4 sleep (P < 0.05) in patients with obstructive sleep apnea but had no significant effects in patients with central sleep apnea. Conclusions. The beneficial effects of exercise training on neurovascular function, functional capacity, and quality of life in patients with systolic dysfunction and heart failure occurs independently of sleep disordered breathing. Exercise training lessens the severity of obstructive sleep apnea but does not affect central sleep apnea in patients with heart failure and sleep disordered breathing.

Outpatient Radioiodine Therapy for Thyroid Cancer A Safe Nuclear Medicine Procedure

Purposes: To evaluate the dosimetric effect of outpatient radioiodine therapy for thyroid cancer in members of a patient`s family and their living environment, when using iodine-131 doses reaching 7.4 GBq. The following parameters were thus defined: (a) whole-body radiation doses to caregivers, (b) the production of contaminated solid waste, and (c) radiation potential and surface contamination within patients` living quarters. Methods: In total, 100 patients were treated on an outpatient basis, taking into consideration their acceptable living conditions, interests, and willingness to comply with medical and radiation safety guidelines. Both the caregivers and the radiation dose potentiality inside patients` residences were monitored by using thermoluminescent dosimeters. Surface contamination and contaminated solid wastes were identified and measured with a Geiger-Muller detector. Results: A total of 90 monitored individuals received a mean dose of 0.27 (+/- 0.28) mSv, and the maximum dose registered was 1.6 mSv. The mean value for the potential dose within all living quarters was 0.31(+/- 0.34) mSv, and the mean value per monitored surface was 5.58 Bq/cm(2) for all the 1659 points measured. The overall production of contaminated solid wastes was at a low level, being about 3 times less than the exemption level indicated by the International Atomic Energy Agency. Conclusions: This study indicates that the treatment of thyroid cancer by applying radioiodine activities up to 7.4 GBq, on an outpatient basis, is a safe procedure, especially when supervised by qualified professionals. This alternative therapy should be a topic for careful discussion considering the high potential for reducing costs in healthcare and improving patient acceptance.

Effect of aquatic respiratory exercise-based program in patients with fibromyalgia

Objective: This study assessed the effects of an aquatic respiratory exercise-based program in patients with fibromyalgia (FMS). Methods: Forty women, aged between 20 and 60 years, were randomly assigned into two groups of 20 patients: the aquatic respiratory exercise-based program (ARG) and the control group (CTL). The ARG group performed the exercise program for 1 h, four times a week, for 4 weeks which included: (i) warming-Lip; (ii) respiratory exercises, consisting of five different breathing patterns, along With upper, lower limbs and trunk movements (45 min); and (iii) relaxation exercises. Both groups were included in supervised recreational activities of 1 h, once a week, for 4 weeks. Questionnaires were applied before and after intervention to assess quality of life and functional capacity (SF-36, Fibromyalgia Impact Questionnaire [FIQ]), anxiety (Hamilton Anxiety Scale [HAS]), and quality of sleep (Pittsburg Sleep Quality Index [PSQI]). Number of tender points and pain (Visual Analogue Scale [VAS]) were also evaluated. Results: At baseline there was no difference between the two groups, including number of tender points and questionnaire responses. After intervention, the ARG group, compared with the CTL group, showed improvement in SF-36 scores (physical functioning P = 0.001, bodily pain 1) = 0.001, vitality P = 0.009, social functioning P = 0.001, emotional role P = 0.001), in FIQ (total score P = 0.049, work missed P = 0.036, fatigue P = 0.013, morning tiredness P = 0.007) plus in VAS-pain (P = 0.029), VAS-dyspnea (P = 0.04), anxiety (HAS P = 0.005) and quality of sleep (PSQI P = 0.004). Conclusions: The short-term aquatic respiratory exercise-based program improved pain, quality of life, functional capacity, anxiety and quality of sleep in patients with FMS and may be a relevant addition to the treatment of these patients.

Interferon and Alternative Activation of Monocyte/Macrophages in Systemic Sclerosis-Associated Pulmonary Arterial Hypertension

Objective. To explore the relationship between biomarkers of pulmonary arterial hypertension (PAH), interferon (IFN)-regulated gene expression, and the alternative activation pathway in systemic sclerosis (SSc). Methods. Peripheral blood mononuclear cells (PBMCs) were purified from healthy controls, patients with idiopathic PAH, and SSc patients (classified as having diffuse cutaneous SSc, limited cutaneous SSc [lcSSc] without PAH, and lcSSc with PAH). IFN-regulated and ""PAH biomarker"" genes were compared after supervised hierarchical clustering. Messenger RNA levels of selected IFN-regulated genes (Siglec1 and MX1), biomarker genes (IL13RA1, CCR1, and JAK2), and the alternative activation marker gene (MRC1) were analyzed on PBMCs and on CD14- and CD14+ cell populations. Interleukin-13 (IL-13) and IL-4 concentrations were measured in plasma by immunoassay. CD14, MRC1, and IL13RA1 surface expression was analyzed by flow cytometry. Results. Increased PBMC expression of both IFN-regulated and biomarker genes distinguished SSc patients from healthy controls. Expression of genes in the biomarker cluster, but not in the IFN-regulated cluster, distinguished lcSSc with PAH from lcSSc without PAH. The genes CCR1 (P < 0.001) and JAK2 (P < 0.001) were expressed more highly in lcSSc patients with PAH compared with controls and mainly by CD14+ cells. MRC1 expression was increased exclusively in lcSSc patients with PAH (P < 0.001) and correlated strongly with pulmonary artery pressure (r = 0.52, P = 0.03) and higher mortality (P = 0.02). MRC1 expression was higher in CD14+ cells and was greatly increased by stimulation with IL-13. IL-13 concentrations in plasma were most highly increased in lcSSc patients with PAH (P < 0.001). Conclusion. IFN-regulated and biomarker genes represent distinct, although related, clusters in lcSSc patients with PAH. MRC1, a marker for the effect of IL-13 on alternative monocyte/macrophage activation, is associated with this severe complication and is related to mortality.

Effects of progressive resistance strength training on knee biomechanics during single leg step-up in persons with mild knee osteoarthritis

Background: The goal of this study was to determine if increasing strength in primary knee extensors and flexors would directly affect net knee joint moments during a common functional task in persons with knee osteoarthritis. Methods: An exploratory single sample clinical trial with pre-post treatment measures was used to study volunteers with clinical diagnosis of mild knee osteoarthritis (OA) in one knee. Subjects participated in an individually supervised training program 3 times a week for eight weeks consisting of progressive resistive exercises for knee extensors and knee flexors. Pre and post training outcome assessments included: 1. Net internal knee joint moments, 2. Electromyography of primary knee extensors and flexors, and 3. Self-report measures of knee pain and function. The distribution of lower extremity joint moments as a percent of the total support moment was also investigated. Findings: Pain, symptoms, activities of daily life, quality of life, stiffness, and function scores showed significant improvement following strength training. Knee internal valgus and hip internal rotation moments showed increasing but non-statistically significant changes post-training. There were no significant differences in muscle co-contraction activation of the Quadriceps and Hamstrings. Interpretation: While exercise continues to be an important element of OA management, the results of this study suggest improvements in function, pain, and other symptoms, as a result of strength training may not be causally related to specific biomechanical changes in net joint moments. (C) 2011 Elsevier Ltd. All rights reserved.

Nutritional information of meals supplied by companies, participating in the workers` meal program in Sao Paulo, Brazil

Objective. To compare the nutritional value of meals provided by companies participating in the Workers` Meal Program in the city of Sao Paulo, Brazil, to the nutritional recommendations and guidelines established by the Ministry of Health for the Brazilian population. Methods. The 72 companies studied were grouped according to economic sector (industrial, services, or commerce), size (micro, small, medium, or large), meal preparation modality (prepared on-site by the company itself, on-site by a hired caterer, or off-site by a hired caterer), and supervision by a dietitian (yes or no). The per capita amount of food was determined based on the lunch, dinner, and supper menus for three days. The nutritional value of the meals was defined by the amount of calories, carbohydrates, protein, total fat, polyunsaturated fat, saturated fat, trans fat, sugars, cholesterol, and fruits and vegetables. Results. Most of the menus were deficient in the number of fruits and vegetables (63.9%) and amount of polyunsaturated fat (83.3%), but high in total fat (47.2%) and cholesterol (62.5%). Group 2, composed of mostly medium and large companies, supervised by a dietician, belonging to the industrial and/or service sectors, and using a hired caterer, on averaged served meals with higher calorie content (P < 0.001), higher percentage of polyunsaturated fat (P < 0.001), more cholesterol (P = 0.015), and more fruits and vegetables (P < 0.001) than Group 1, which was composed of micro and small companies from the commercial sector, that prepare the meals themselves on-site, and are not supervised by a dietitian. Regarding the nutrition guidelines set for the Brazilian population, Group 2 meals were better in terms of fruit and vegetable servings (P < 0.001). Group I meals were better in terms of cholesterol content (P = 0.05). Conclusions. More specific action is required targeting company officers and managers in charge of food and nutrition services, especially in companies without dietitian supervision.

Bayesian network classifiers: Beyond classification accuracy

This work proposes and discusses an approach for inducing Bayesian classifiers aimed at balancing the tradeoff between the precise probability estimates produced by time consuming unrestricted Bayesian networks and the computational efficiency of Naive Bayes (NB) classifiers. The proposed approach is based on the fundamental principles of the Heuristic Search Bayesian network learning. The Markov Blanket concept, as well as a proposed ""approximate Markov Blanket"" are used to reduce the number of nodes that form the Bayesian network to be induced from data. Consequently, the usually high computational cost of the heuristic search learning algorithms can be lessened, while Bayesian network structures better than NB can be achieved. The resulting algorithms, called DMBC (Dynamic Markov Blanket Classifier) and A-DMBC (Approximate DMBC), are empirically assessed in twelve domains that illustrate scenarios of particular interest. The obtained results are compared with NB and Tree Augmented Network (TAN) classifiers, and confinn that both proposed algorithms can provide good classification accuracies and better probability estimates than NB and TAN, while being more computationally efficient than the widely used K2 Algorithm.

Building binary-tree-based multiclass classifiers using separability measures

Various popular machine learning techniques, like support vector machines, are originally conceived for the solution of two-class (binary) classification problems. However, a large number of real problems present more than two classes. A common approach to generalize binary learning techniques to solve problems with more than two classes, also known as multiclass classification problems, consists of hierarchically decomposing the multiclass problem into multiple binary sub-problems, whose outputs are combined to define the predicted class. This strategy results in a tree of binary classifiers, where each internal node corresponds to a binary classifier distinguishing two groups of classes and the leaf nodes correspond to the problem classes. This paper investigates how measures of the separability between classes can be employed in the construction of binary-tree-based multiclass classifiers, adapting the decompositions performed to each particular multiclass problem. (C) 2010 Elsevier B.V. All rights reserved.

A review on the combination of binary classifiers in multiclass problems

Several real problems involve the classification of data into categories or classes. Given a data set containing data whose classes are known, Machine Learning algorithms can be employed for the induction of a classifier able to predict the class of new data from the same domain, performing the desired discrimination. Some learning techniques are originally conceived for the solution of problems with only two classes, also named binary classification problems. However, many problems require the discrimination of examples into more than two categories or classes. This paper presents a survey on the main strategies for the generalization of binary classifiers to problems with more than two classes, known as multiclass classification problems. The focus is on strategies that decompose the original multiclass problem into multiple binary subtasks, whose outputs are combined to obtain the final prediction.

Comparing machine learning classifiers in potential distribution modelling

Species` potential distribution modelling consists of building a representation of the fundamental ecological requirements of a species from biotic and abiotic conditions where the species is known to occur. Such models can be valuable tools to understand the biogeography of species and to support the prediction of its presence/absence considering a particular environment scenario. This paper investigates the use of different supervised machine learning techniques to model the potential distribution of 35 plant species from Latin America. Each technique was able to extract a different representation of the relations between the environmental conditions and the distribution profile of the species. The experimental results highlight the good performance of random trees classifiers, indicating this particular technique as a promising candidate for modelling species` potential distribution. (C) 2010 Elsevier Ltd. All rights reserved.

Semantic mapping using mobile robots

Robotic mapping is the process of automatically constructing an environment representation using mobile robots. We address the problem of semantic mapping, which consists of using mobile robots to create maps that represent not only metric occupancy but also other properties of the environment. Specifically, we develop techniques to build maps that represent activity and navigability of the environment. Our approach to semantic mapping is to combine machine learning techniques with standard mapping algorithms. Supervised learning methods are used to automatically associate properties of space to the desired classification patterns. We present two methods, the first based on hidden Markov models and the second on support vector machines. Both approaches have been tested and experimentally validated in two problem domains: terrain mapping and activity-based mapping.

Multilevel Training of Binary Morphological Operators

The design of binary morphological operators that are translation-invariant and locally defined by a finite neighborhood window corresponds to the problem of designing Boolean functions. As in any supervised classification problem, morphological operators designed from a training sample also suffer from overfitting. Large neighborhood tends to lead to performance degradation of the designed operator. This work proposes a multilevel design approach to deal with the issue of designing large neighborhood-based operators. The main idea is inspired by stacked generalization (a multilevel classifier design approach) and consists of, at each training level, combining the outcomes of the previous level operators. The final operator is a multilevel operator that ultimately depends on a larger neighborhood than of the individual operators that have been combined. Experimental results show that two-level operators obtained by combining operators designed on subwindows of a large window consistently outperform the single-level operators designed on the full window. They also show that iterating two-level operators is an effective multilevel approach to obtain better results.