Virtual reality (VR) rehabilitation demonstrates high patient satisfaction in hospital settings, yet implementation in home-based telerehabilitation remains unexplored in Czechia. Understanding patient attitudes toward VR telerehabilitation is crucial for developing feasible post-discharge rehabilitation programs. To assess patient attitudes toward actual VR telerehabilitation implementation following positive hospital-based VR rehabilitation experience in subacute stroke patients. A telephone-based follow-up study was conducted among patients who previously participated in VR rehabilitation during subacute stroke hospitalization at University Hospital Ostrava. Participants completed a structured interview assessing overall VR rehabilitation satisfaction, interest in home-based VR telerehabilitation with actual equipment provision, confidence in remote physiotherapist supervision, family support availability, perceived barriers, and open-ended feedback. Non-response bias was assessed through baseline characteristics comparison and sensitivity analyses. Ten patients from the original cohort of 19 participants completed telephone interviews (response rate 52.6
Bipolar affective disorder (BD) is a severe mental illness characterized by recurrent episodes of mania, hypomania, and depression, accompanied by progressive neurobiological changes that go beyond the classical concepts of neurotransmitter dysregulation. Increasing evidence points to the key role of the interaction between inflammatory processes, mitochondrial dysfunction, and disturbances within neural networks in the pathogenesis, course, and treatment response of BD. Neuroinflammatory processes, including elevated levels of pro-inflammatory cytokines, chemokines, and microglial activation, are consistently reported in patients with BD and linked to cognitive impairment, accelerated neuroprogression, and treatment resistance. At the same time, mitochondrial abnormalities-such as impaired oxidative phosphorylation, excessive production of reactive oxygen species, and disturbances in calcium homeostasis-contribute to oxidative stress, synaptic dysfunction, and increased neuronal vulnerability, forming the biological substrate of mood instability. Findings from neuroimaging studies provide consistent evidence of structural and functional alterations within the cortico-limbic networks regulating emotions, including the prefrontal cortex, anterior cingulate cortex, amygdala, and hippocampus. Importantly, a growing number of studies demonstrate correlations between neuroimaging changes and inflammatory and metabolic biomarkers, making it possible to link molecular pathology with dysfunctions at the level of neural networks. The use of multimodal methods-encompassing structural and functional magnetic resonance imaging, spectroscopy, and molecular analyses-allows for a more precise explanation of these complex interactions and the identification of biomarkers of clinical states, progression, and treatment response. This review synthesizes current knowledge on the molecular and neuroimaging correlates of BD, emphasizing the interdependence of inflammatory processes, mitochondrial function, and neural networks. The integration of molecular biomarkers with imaging-based phenotyping opens new perspectives for precision medicine in BD.
Neoadjuvant chemo-immunotherapy has transformed the treatment landscape for resectable non-small-cell lung cancer (NSCLC). However, real-world surgical data from nationwide European cohorts remain limited. This study evaluated surgical outcomes and practice patterns during the first year of nationwide implementation of neoadjuvant chemo-immunotherapy with nivolumab in the Czech Republic. This prospective, multicenter, observational cohort study included all consecutive patients with resectable programmed cell death ligand-1(PD-L1) ≥ 1
BACKGROUND:Patent foramen ovale (PFO) is the most common inherited cardiac abnormality, with intermittent right-to-left (R-L) shunt of variable size. AIMS:To study the quantification of R-L shunt (shunt) in PFO patients using an original thermodilution method in relationship to the morphology of the interatrial septum, measured by echocardiography and cardiopulmonary exercise tests before and after the occlusion of a PFO. METHODS:A multicenter study enrolled a population of 151 patients, eligible for PFO closure in secondary prevention, who underwent transthoracic echocardiography, contrast transesophageal echocardiography, and cardiopulmonary exercise tests before and 6 months after the closure. The size of the shunt was measured by catheterization using the thermodilution Inntherm® system. RESULTS:Shunt was detected in 133 (90%) patients. Size of shunt >20% was present in 41 (28%) patients, 10%-20% in 33 (22%), and <10% in 49 (40%) patients. Shunt size >10% was associated with greater atrial septal separation during the Valsalva (P = 0.03) and respiration (P = 0.04), amplitude of septal displacement (P = 0.009), amount of contrast media in the left atrium (P = 0.03), and hypermobility of septum (P = 0.03). After PFO closure, work (W) capacity increased in patients with shunt ≥20% (Δ +6.8; standard deviation [SD] 16.9 [W]) compared with those with <10% (Δ -2.1; SD 24.5 [W]; P = 0.02). Migraine occurrence decreased in 8 patients (62%) (P = 0.008). A decrease in saturation on exercise occurred in 12 cases (9%), after the PFO closure saturation normalized from 90.5% to 95.0% (P = 0.009), with no effect on exercise. CONCLUSIONS:We proved safe use of thermodilution for R-L shunt measurement. The main cause of inducible shunt severity is atrial septal mobility. A severe PFO shunt impacts work capacity.
Detecting fetal distress early and accurately is critical to the reduction of adverse neonatal outcomes. However, this proves to be a challenging task. The use of artificial intelligence (AI) provides a promising solution by facilitating the analysis of cardiotocography (CTG) signals in a more timely and precise manner. Despite this, AI in fetal monitoring faces several critical obstacles. In this study, we evaluated six experimental scenarios on a publicly available UCI CTG dataset consisting of 2,160 cases, each containing 21 features and examined how selected machine learning-based models, various data partitioning strategies, and input feature selection influence classification performance and predictor importance. We compare multiple classification models-Classification and Regression Trees (CART), C5, Random Tree, Chi-squared Automatic Interaction Detection (CHAID), K-Nearest Neighbor (KNN), Multilayer Perceptron (MLP), Radial Basis Function Network (RBF), and Support Vector Machines (SVM). According to our results, we were able to predict normal, suspect, and pathological cases with an overall accuracy ranging from 86.14% up to 96.92%. Our experiments shed light on how different dataset partitioning strategies affect the results and interpretability of significant predictors. In addition, we tested a scenario based on clinical parameters routinely acquired by our best practices in order to determine how these parameters may affect our model's performance. Our findings provide practical recommendations for optimizing both the accuracy and interpretability of machine learning-based models. Furthermore, they emphasize the urgent need for larger, more comprehensive datasets as well as standardized definitions of outcomes. These insights aim to encourage clinical centers to create their own high-quality databases and collaborate across institutions, ultimately advancing the adoption of AI-driven decision-support tools in routine obstetric practice.
