Search Results (266)

Background/Objectives: Utilizing a significative sample, this study aimed to analyze body composition components in different age groups and to develop age-specific normative tables for individuals in southern Brazil. Methods: This observational, descriptive, and cross-sectional study evaluated 8556 individuals of both sexes (54% females) aged 18–49. The hypotheses of the present study are related to declining fat-free mass (FFM), lean mass (LM), and skeletal muscle mass (SMM) and increasing fat mass (FM) and body fat percentage (BFP) during the aging process. Data were collected through bioelectrical impedance analysis (BIA) and stratified by age (18–29, 30–39, and 40–49 years), sex, and body mass index (BMI) classifications (normal weight, overweight, grade I, and grade II obesity). Following the comparison, body composition components were presented in the 3rd, 10th, 25th, 50th, 75th, 90th, and 97th percentiles. Results: This study’s main findings indicated that FM and BFP increased with age in both sexes. Among overweight and obese individuals, elevated BFP was particularly high in obese females aged 40–49 years and in normal and overweight males. FFM, LM, and SMM were generally lower in the 40–49-year-old group, although obese females over 40 presented higher FFM and LM values. In contrast, males presented lower FFM and LM values but higher values among individuals with higher BMI. SMM was lower in overweight individuals over 40, likely reflecting muscle mass loss associated with aging. Conclusions: Based on these results, lifestyle interventions that combine nutrition and physical exercise may be recommended to mitigate these effects of aging. Full article

Background: Physical therapy is crucial in the rehabilitation of children with cerebral palsy (CP), aiming to enhance motor function, postural control, and functional independence. Objective: The study explored the current physical therapy interventions for children with CP in Saudi Arabia, including waiting time, the most used interventions, the focus of therapy, and parents’ desired goals. Methods: A cross-sectional study was conducted involving 215 children with CP (aged 6 months to 18.2 years). Face-to-face surveys were conducted to collect data on CP classification (based on the Gross Motor Function Classification System), age at first referral, types of interventions used, intervention goals, and parents’ desired goals for their children. Results: Children with severe CP (non-ambulators) received physical therapy services significantly earlier than those with milder involvement (ambulators). The most commonly used interventions were therapeutic exercises and home exercises, followed by standing frames. Hydrotherapy was the least utilized intervention. The focus of therapy was mainly on joints and muscles, as well as mobility and transfers. Conclusions: The study underscores the need to identify and refer children with CP for physical therapy. The findings suggest further investigation into barriers to utilizing certain interventions like hydrotherapy and emphasize the need for more inclusive goal-setting processes in the rehabilitation of children with CP based on both physical therapy and parent perspectives. Full article

This article proposes a conversation on the limits and possibilities of collectivizing the way in which we generate and inscribe knowledge within the terms of a political economy of knowledge production and circulation regulated by hierarchies of academic and non-academic classifications, as well as those that demarcate centres and peripheries domestically and internationally through racial–gendered distributions of authority. To this end, it explores a series of collective projects elaborated within the GlobalGRACE network in Brazil (Global Gender and Cultures of Equality), which experiment with residency methodologies designed to create the necessary infrastructure for a redistribution of power, knowledge, and authority in investigations on racial–gendered violence in the peripheries of Rio de Janeiro. As collaborators in this research–action project initiated in 2018 with the Observatory of Favelas of Rio de Janeiro, here, we mobilize two of these collective projects as case studies—the dance residency of Cia Passinho Carioca and the Free School of Arts ELÃ residency—so as to reflect on our ways of knowing and experiencing racial–gendered inequalities in context. In this way, it becomes possible to propose not only questions around the production, erasure, and appropriation of knowledge but also possibilities for the broad-based circulation of dissident knowledge practices and the subsequent displacement of established authorities in the field, notably by means of a disobjectification of subjects of knowledge and exercises in authoring in the first-person plural. This entry point into the conversation on who has the power to know and control the meanings of intersectional inequalities enables a focus on practice, pedagogy, and methods to unpack the ethical and epistemological questions at hand. By centring the problem of authorship, we argue that feminist and decolonial approaches to knowing, teaching, and learning need to effectuate redistributions of power and the construction of politico-epistemic infrastructure if we have any chance of cultivating the conditions needed for liberatory knowledge practices. Full article

Cardiovascular diseases (CVDs) are among the leading causes of mortality worldwide, particularly in Mexico, where rural regions face challenges due to limited access to medical equipment. This preliminary study proposes a low-cost cardiovascular disease classifier, Buazduino-001, which integrates machine learning (ML) techniques with Arduino-based technology to provide accessible and non-invasive risk assessment. Three classical ML models—logistic regression, random forest, and support vector machine—were implemented and evaluated using a dataset of 303 patients from the UCI Machine Learning Repository. This study introduces a six-stage methodology, including a novel step that prioritizes non-invasive attributes to optimize diagnostic time and cost. The random forest model demonstrated the best performance, achieving 87% classification accuracy, with a reduced feature set of five attributes (sex, age, chest pain, heart rate, and exercise-induced angina). In this preliminary study, the system was validated experimentally with 30 patients, confirming an 85% accuracy and an 80% reduction in diagnostic time compared to traditional medical assessments. The results highlight the practicality of combining ML with low-cost electronics to address healthcare gaps in resource-limited settings. While this study is preliminary, the Buazduino-001 system demonstrates potential for early CVD risk detection and could serve as a screening tool in rural clinics, complementing conventional diagnostic methods. Full article

Internet Gaming Disorder (IGD) has been recognized by the World Health Organization (WHO) in the International Classification of Diseases (ICD-11) and as an emerging condition in the DSM-5. IGD is increasingly prevalent, with various negative effects on individuals’ development and adaptation. To address this issue, different therapeutic interventions, like CBT, virtual reality, mindfulness, or family therapy, have been explored. This systematic review aimed to answer the following research question: What is the effectiveness of therapeutic interventions in reducing IGD symptoms in adolescents and young adults diagnosed with this disorder? Following PRISMA guidelines, 22 studies published between 2014 and 2025 were included. Results show that cognitive behavioral therapy (CBT) is the most effective intervention, significantly reducing IGD severity, anxiety, and depression. Combining CBT with physical exercise or mindfulness further enhanced outcomes. Other promising approaches include virtual reality (VR), transcranial direct current stimulation (tDCS), and family-based interventions. Additionally, treatments involving mindfulness and animal-assisted therapy showed potential in improving emotional regulation and interpersonal relationships. However, further research is needed to evaluate long-term efficacy and explore emerging therapies. Full article

Background: Hallux valgus (HV) is one of the most common foot deformities and negatively impacts plantar support. The abductor hallucis (AH) is the most important muscle in the etiopathogenesis of hallux valgus, but the effectiveness of its rehabilitation clashes with the difficulty of identifying the most suitable exercises to activate it. Therefore, the aim of this study was to compare four different therapeutic exercises in the activation of AH in these patients. Methods: In this observational case–control study, 48 patients suffering from hallux valgus of moderate/severe grade, according to traditional radiographic classification and the Manchester scale, were divided into two groups: the case group underwent a monthly rehabilitation protocol for their foot deformity, whereas the control group was only evaluated without any intervention. The exercises were as follows: Toe Spread Out (TSO), Short Foot (SF), Forefoot Adduction (FA), and Flexion of the Metatarsophalanges (FM). Both groups were analyzed at baseline and 1 month later (at the end of rehabilitation for the case group) while performing the four mentioned exercises using a surface electromyograph (sEMG) to record the muscle activity of AH in terms of Root Mean Square (RMS) and Maximum Voluntary Contraction (MVC). Results: FA was the only exercise to determine a statistically significant improvement in AH at the end of the rehabilitation cycle, both in terms of RMS (p = 0.015) and in terms of MVC (p < 0.0001), whereas the other exercises did not produce any change in muscle activity in the comparison between times and groups or in the related interaction. Conclusions: FA seems to be the best exercise to activate and train AH, so rehabilitation programs for patients suffering from hallux valgus should consider this exercise as the starting point for improving plantar support, always considering the specific characteristics of HV. Further studies are needed to deepen the effectiveness of this exercise, with the aim of implementing rehabilitation strategies and rethinking traditional HV therapies, which are currently predominantly surgical. Full article

Measuring breathing changes during exercise is crucial for healthcare applications. This study used wearable capacitive sensors to capture abdominal motion and extract breathing patterns. Data preprocessing methods included filtering and normalization, followed by feature extraction for classification. Despite the growing interest in respiratory monitoring, research on a deep learning-based analysis of breathing data remains limited. To address this research gap, we optimized CNN and ResNet through systematic hyperparameter tuning, enhancing classification accuracy and robustness. The optimized ResNet outperformed the CNN in accuracy (0.96 vs. 0.87) and precision for Class 4 (0.8 vs. 0.6), demonstrating its capability to capture complex breathing patterns. These findings highlight the importance of hyperparameter optimization in respiratory monitoring and suggest ResNet as a promising tool for real-time assessment in medical applications. Full article

This paper introduces a fatigue detection model specifically designed for immersive virtual reality (VR) environments, aimed at facilitating upper limb rehabilitation for individuals with spinal cord injuries (SCIs). The model’s primary application centers on the Box-and-Block Test, providing healthcare professionals with a reliable tool to monitor patient progress and adapt rehabilitation routines. At its core, the model employs data fusion techniques via ordered weighted averaging (OWA) operators to aggregate multiple metrics captured by the VR rehabilitation system. Additionally, fuzzy logic is employed to personalize fatigue assessments. Therapists are provided with a detailed classification of fatigue levels alongside a video-based visual representation that highlights critical moments of fatigue during the exercises. The experimental methodology involved testing the fatigue detection model with both healthy participants and patients, using immersive VR-based rehabilitation scenarios and validating its accuracy through self-reported fatigue levels and therapist observations. Furthermore, the model’s scalable design promotes its integration into remote rehabilitation systems, highlighting its adaptability to diverse clinical scenarios and its potential to enhance accessibility to rehabilitation services. Full article

Retrospective studies have suggested that performing perioperative abdominal wall exercises may decrease the incidence of parastomal hernias. Objectives: This study seeks to assess the usefulness of supervised preoperative and postoperative abdominal wall exercises in the prevention of parastomal hernia. Methods: An observational study of patients who underwent a stoma, temporary or permanent, between January 2019 and December 2020, was performed. Minimum follow-up was 12 months. During the first 12 months of recruitment, patients were enrolled on a consecutive basis and assigned to the control group, and the remaining patients were assigned to the intervention group. A propensity score matching was performed to obtain totally comparable groups. A set of exercises was designed by the Rehabilitation Department, and their performance was supervised by physiotherapists and stoma therapists. The diagnosis of parastomal hernia was made by physical examination and computed axial tomography. Descriptive statistics of the study group were performed. Subsequently, prediction models for the occurrence of parastomal hernia were created based on binary logistic regression and classification trees. Results: After propensity matching and inclusion criteria, 64 patients were included (colostomy: n = 39, ileostomy: n = 25). Independent prognostic variables for parastomal hernias in colostomy were age (p = 0.044) and perioperative exercises (p = 0.003). The binary logistic regression model based on these variables gave an AUC of 97.6. The classification tree model included only perioperative exercises with an AUC of 92.5%. In the case of ileostomy, perioperative exercises were the only independent prognostic variable identified. The classification-tree-based model reported an AUC of 84%. Conclusions: The performance of supervised abdominal wall training and strengthening exercises may be useful in the prevention of parastomal hernias. Full article

Wheat (Triticum aestivum L.) omega-5 gliadin, a major allergen responsible for wheat-dependent exercise-induced anaphylaxis in humans, is encoded by genes located at the Gli-B1 locus on chromosome 1B, which exhibits genetic polymorphism. Gli-B1 alleles have generally been identified based on the electrophoretic mobilities of the encoded gamma-, omega-1,2, and omega-5 gliadins in acid polyacrylamide gel electrophoresis. However, the similar mobilities of omega-5 gliadin variants make it difficult to distinguish them among different wheat varieties. In this study, we optimized reverse-phase–ultra-performance liquid chromatography (RP-UPLC) conditions to separate omega-5 gliadins in the reference wheat cultivar Chinese Spring and its nullisomic–tetrasomic lines for chromosome 1B. Five chromatographic peaks corresponded to omega-5 gliadin, and the average relative standard deviation to each peak retention time ranged from 0.31% to 0.93%, indicating that the method is accurate and reproducible for fractionating omega-5 gliadins in gliadin extracts from wheat flour. Using the optimized RP-UPLC method, we analyzed omega-5 gliadins in 24 wheat varieties with the Gli-B1f allele. The result showed that the wheat varieties were sorted into eight groups according to the composition of omega-5 gliadin, indicating that the classification of Gli-B1 alleles based on A-PAGE could not explain the composition of omega-5 gliadin in wheat. We reclassified 73 wheat varieties containing 16 unique Gli-B1 alleles into 31 groups based on the chromatographic patterns of their omega-5 gliadins. Our results provide information on the specific Gli-B1 alleles of wheat varieties belonging to each group and demonstrate the potential for RP-UPLC to facilitate genetic studies of wheat varieties. Full article

Excessive exercise is a primary cause of sports injuries and sudden death. Therefore, it is vital to develop an effective monitoring technology for exercise intensity. Based on the noninvasiveness and real-time nature of an electrocardiogram (ECG), exercise ECG classification based on ECG features could be used for detecting exercise intensity. However, current R-peak detection algorithms still have limitations, especially in high-intensity exercise scenarios and in the presence of noise interference. Additionally, the features utilized for exercise ECG classification are not comprehensive. To address these issues, the following tasks have been accomplished: (1) a hybrid time–frequency-domain model, BILSTM-CNN, is proposed for R-peak detection by utilizing BILSTM, multi-scale convolution, and an attention mechanism; (2) to enhance the robustness of the detector, a preprocessing data generator and a post-processing adaptive filter technique are proposed; (3) to improve the reliability of exercise intensity detection, the accurate heart rate variability (HRV) features derived from the proposed BILSTM-CNN and comprehensive features are constructed, which include various descriptive features (wavelets, local binary patterns (LBP), and higher-order statistics (HOS)) tested by the feasibility experiments and optimized deep learning features extracted from the continuous wavelet transform (CWT) of exercise ECG signals. The proposed system is evaluated by real ECG datasets, and it shows remarkable effectiveness in classifying five types of motion states, with an accuracy of 99.1%, a recall of 99.1%, and an F1 score of 99.1%. Full article

Background/Objectives: This study aimed to explore machine learning approaches for predicting physical exertion using physiological signals collected from wearable devices. Methods: Both traditional machine learning and deep learning methods for classification and regression were assessed. The research involved 27 healthy participants engaged in controlled cycling exercises. Physiological data, including ECG, heart rate, oxygen saturation, and pedal speed (RPM), were collected during these sessions, which were divided into eight two-minute segments. Heart rate variability (HRV) was also calculated to serve as a predictive indicator. We employed two feature selection algorithms to identify the most relevant features for model training: Minimum Redundancy Maximum Relevance (MRMR) for both classification and regression, and Univariate Feature Ranking for Classification. A total of 34 traditional models were developed using MATLAB’s Classification Learner App, utilizing 20% of the data for testing. In addition, Long Short-Term Memory (LSTM) networks were trained on the top features selected by the MRMR and Univariate Feature Ranking algorithms to enhance model performance. Finally, the MRMR-selected features were used for regression to train the LSTM model for predicting continuous outcomes. Results: The LSTM model for regression demonstrated robust predictive capabilities, achieving a mean squared error (MSE) of 0.8493 and an R-squared value of 0.7757. The classification models also showed promising results, with the highest testing accuracy reaching 89.2% and an F1 score of 91.7%. Conclusions: These results underscore the effectiveness of combining feature selection algorithms with advanced machine learning (ML) and deep learning techniques for predicting physical exertion levels using wearable sensor data. Full article

In an attempt to address the growing shortage of cybersecurity specialists in the country, the School of Electrical Engineering, University of Belgrade, started the course entitled Advanced Network and System Security (ANS) in the 2019/2020 school year. The ANS course covers the topics of computer system and network security, intrusion detection and prevention, and ethical hacking methodologies. This paper presents the course organization and associated laboratory environment and exercises and aims to prove that providing such a multidisciplinary laboratory leads to successful learning outcomes and directly improves gained knowledge in cybersecurity. The ANS course differs from all other related courses by covering various cybersecurity topics ranging from hardware through to network to web security. The analysis showed that 13 out of 19 Cyber Security Body of Knowledge classification Knowledge Areas are covered in the ANS course, unlike other related courses which cover up to 8 Knowledge Areas. Ultimately, the students’ practical skills improvement evaluation was performed through quantitative and qualitative analysis in order to prove that improving practical skills in the ANS laboratory resulted in the overall improvement of the gained knowledge. Full article

Background: Indirect calorimetry is the gold standard field-testing technique for measuring energy expenditure and exercise intensity based on the volume of oxygen consumed (VO₂, mL O₂/min). Although heart rate is often used as a proxy for VO₂, heart rate-based estimates of VO₂ may be inaccurate after stroke due to changes in the heart rate–VO₂ relationship. Our objective was to evaluate in people post stroke the accuracy of using heart rate to estimate relative walking VO₂ (wVO₂) and classify exercise intensity. Moreover, we sought to determine if estimation accuracy could be improved by including clinical variables related to patients’ function and health in the estimation. Methods: Sixteen individuals post stroke completed treadmill walking exercises with concurrent indirect calorimetry and heart rate monitoring. Using 70% of the data, forward selection regression with repeated k-fold cross-validation was used to build wVO₂ estimation equations that use heart rate alone and together with clinical variables available at the point-of-care (i.e., BMI, age, sex, and comfortable walking speed). The remaining 30% of the data were used to evaluate accuracy by comparing (1) the estimated and actual wVO₂ measurements and (2) the exercise intensity classifications based on metabolic equivalents (METs) calculated using the estimated and actual wVO₂ measurements. Results: Heart rate-based wVO₂ estimates were inaccurate (MAE = 3.11 mL O₂/kg/min) and unreliable (ICC = 0.68). Incorporating BMI, age, and sex in the estimation resulted in improvements in accuracy (MAE Δ: −36.01%, MAE = 1.99 mL O₂/kg/min) and reliability (ICC Δ: +20, ICC = 0.88). Improved exercise intensity classifications were also observed, with higher accuracy (Δ: +29.85%, from 0.67 to 0.87), kappa (Δ: +108.33%, from 0.36 to 0.75), sensitivity (Δ: +30.43%, from 0.46 to 0.60), and specificity (Δ: +17.95%, from 0.78 to 0.92). Conclusions: In people post stroke, heart rate-based wVO₂ estimations are inaccurate but can be substantially improved by incorporating clinical variables readily available at the point of care. Full article

Recombinant therapeutic and vaccine proteins have revolutionized healthcare, but there remain challenges, as many are awaiting development due to their slow development speed and high development cost. Cell-free in vivo ribosomes offer one choice, but they come with similar constraints. The validation of in vivo messenger RNA (mRNA) technology has been accomplished for COVID-19 vaccines. The bioreactors inside the body, the ribosomes, deliver these proteins at a small cost, since these are chemical products and do not require extensive analytical and regulatory exercises. In this study, we test and validate the final product. A smaller fraction of the recombinant protein cost is needed, removing both constraints. Although thousands of in vivo mRNA products are under development, their regulatory classification remains unresolved: do they qualify as chemical drugs, biological drug, or gene therapy items? These questions will soon be resolved. Additionally, how would the copies of approved in vivo mRNA protein products be brought in, and how would they be treated: as new drugs, generic drugs, or new biological drugs? Researchers are currently working to answer these questions. Regardless, these products’ cost of goods (COGs) remains much smaller than that of ex vivo mRNA or recombinant products. This is necessary to meet the needs of the approximately 6.5 billion people around the world who do not have access to biological drugs; these products will indeed serve the dire needs of humanity. Given the minor cost of establishing the manufacturing of these products, it will also prove financially attractive to investors. Full article