Introduction: The new corona virus (2019-nCoV OR HCOV-19 or CoV2), has emerged in China as the main cause of viral pneumonia (COVID-19, Coronavirus Disease-19). Objective: To provide evidence-based Physiotherapy and functionality in patients with adult and pediatric COVID-19. Methods: This is an integrative literature review using the MedLine / PubMed databases, library of Latin American and Caribbean Literature in Health Sciences (LILACS) and Physiotherapy Evidence Database (PEDRo). Results: Part of the patients with covid 19 show signs of respiratory deficiency with hypoxemia, with low severity in children. Impaired functionality is also expected. Conclusion: COVID-19 causes low pulmonary compliance and important changes in lung function with hypoxemia and cardiovascular repercussions. These changes lead to the need for Physiotherapy and the management of oxygen therapy and ventilatory support (invasive and non-invasive) for these patients.
The aim of this study was to evaluate the association among anxiety, symptoms of temporomandibular disorders, and awake bruxism in children. A cross-sectional study was carried out with 274 children aged between 7 and 12 years. Data were collected through structured questionnaires, applied by interview and clinical assessment. Statistical analysis involved descriptive and inferential analysis using the Chi-square test and Fisher’s exact test. A binary logistic regression model was applied to identify independent predictors of anxiety. Age ranged from 7 to 12 years and was equally distributed between the sexes. 23.3
Parkinson’s disease (PD) is a progressive neurodegenerative disorder with no proven disease-modifying therapies to date. Because changes in cerebral glucose metabolism and insulin resistance have been linked to PD pathophysiology, glucagon-like peptide-1 receptor agonists (GLP-1RAs), widely used for diabetes, have been investigated as potential neuroprotective treatments. This study systematically assessed the efficacy and safety of GLP-1RAs in PD through a systematic review and meta-analysis of randomized controlled trials identified in PubMed, Embase, and the Cochrane Library. The primary outcomes were motor function improvements measured by the MDS-UPDRS Part III in both on- and off-medication states at study endpoints and at intermediate timepoints of interest. Secondary outcomes included MDS-UPDRS Parts I, II, and IV, quality of life assessed by the PDQ-39, levodopa equivalent daily dose (LEDD), and the occurrence of adverse events. The meta-analysis found no statistically significant difference in favor of GLP-1RAs over placebo for motors and non-motors outcomes, except for PDQ-39 (MD: − 0.75; 95
We consider a rational scalar field model in (1+1)-dimensions where the long-range character of the kinks is controllable. We show via numerical simulations that kinks with long-range tails on both sides can exhibit resonance windows. The resonant energy exchange mechanism occurs via the excitation of quasinormal modes, which we obtain via a spectral analysis. Additionally, we locate a resonance window in a family of ϕ ^10 models with long-range tails on both sides. Moreover, we propose a new algorithm for initializing long-range kink collisions, based on convection–diffusion dynamics.
Recognizing complex behavioral states such as Ambivalence and Hesitancy (A/H) in naturalistic video settings remains a significant challenge in affective computing. Unlike basic facial expressions, A/H manifests as subtle, multimodal conflicts that require deep contextual and temporal understanding. In this paper, we propose a highly regularized, multimodal fusion pipeline to predict A/H at the video level. We extract robust unimodal features from visual, acoustic, and linguistic data, introducing a specialized statistical text modality explicitly designed to capture temporal speech variations and behavioral cues. To identify the most effective representations, we evaluate 15 distinct modality combinations across a committee of machine learning classifiers (MLP, Random Forest, and GBDT), selecting the most well-calibrated models based on validation Binary Cross-Entropy (BCE) loss. Furthermore, to optimally fuse these heterogeneous models without overfitting to the training distribution, we implement a Particle Swarm Optimization (PSO) hard-voting ensemble. The PSO fitness function dynamically incorporates a train-validation gap penalty (lambda) to actively suppress redundant or overfitted classifiers. Our comprehensive evaluation demonstrates that while linguistic features serve as the strongest independent predictor of A/H, our heavily regularized PSO ensemble (lambda = 0.2) effectively harnesses multimodal synergies, achieving a peak Macro F1-score of 0.7465 on the unseen test set. These results emphasize that treating ambivalence and hesitancy as a multimodal conflict, evaluated through an intelligently weighted committee, provides a robust framework for in-the-wild behavioral analysis.
