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Aim: To assess the perceived health-related quality of life (HRQoL) in hospitalized patients with rheumatic diseases and to determine whether selected characteristics can affect their perceived quality of life. Design: A cross-sectional study. Methods: Data were collected between September 2022 and March 2023 using the standardized instrument to measure HRQoL, namely the RAND 36-Item Health Survey (SF-36). The sample consisted of 170 patients with rheumatic diseases hospitalized in a teaching hospital in the Czech Republic. Results: The average perceived HRQoL was 34.94 (SD = 17.06), indicating poor quality of life. The most highly rated subscale was 'emotional well-being' (56.02 ± 15.71), and the lowest rated subscale was 'role limitations due to physical health' (11.76 ± 28.54). The HRQoL subscales were significantly correlated with Mini-Mental State Examination (MMSE) score, self-care activities, pain severity, age of the patients, and length of hospitalization. Differences in HRQoL were identified based on education, social status, use of compensatory aids, attendance at social events, and history of falls in the past year (p < 0.05). Conclusion: The study contributes to the existing body of knowledge on the subject, but also underscores the importance of considering holistic factors when evaluating HRQoL in individuals with rheumatic diseases. [ABSTRACT FROM AUTHOR]

Introduction: Dual diagnosis is used in addiction medicine to refer to the co-occurrence of an addiction-related disorder and another psychiatric disorder in the same individual. Adolescence is a key period for the development of both mental disorders and addictions. Objective: The aim of this study is to describe the relationships between psychiatric and addiction-related disorders in patients of the Outpatient Addiction Treatment Clinic for Children and Adolescents at the 1st Faculty of Medicine, Charles University in Prague in 2015–2022. Methods: Data were retrospectively analyzed from the hospital's medical system, which collects basic diagnostic data on patients. Descriptive statistics and cluster analysis were performed to identify relationships between psychiatric and addiction-related disorders. Results: Of the 450 patients, 153 patients (34%) met the criteria for dual diagnosis. The most common addiction-related disorders were mental and behavioural disorders due to the use of cannabinoids (35%) and internet gaming disorder (35%). The most common psychiatric diagnoses were behavioural and emotional disorders with usual onset in childhood and adolescence (64%), with a lower prevalence in girls than in boys. Conclusions: These findings may be important for the diagnosis and treatment of risky behaviours and addictions in children and adolescents. [ABSTRACT FROM AUTHOR]

This study presents part of the research from a 2019 project in Czechia's residential educational facilities for children with emotional and behavioral disorders. The high prevalence of drug use among these adolescents has a cumulative set of harms to health. The main research objective was to examine the prevalence of substance use among adolescent girls and boys living in residential educational facilities. The auxiliary research objective was to compare its results with the ESPAD (2015 and 2019) survey of the general Czech adolescent mainstream population. The sample consisted of 204 respondents aged 15-19 years. The primary data collection instrument was the ESPAD questionnaire. The analysis of the collected data was carried out using SPSS software. The findings arrived from statistical procedures such as descriptive data analysis and various statistical methods. Data from ESPAD research (2015, 2019) was selected for analytical comparisons. The findings indicate a high prevalence of drug use among the study group of adolescents in residential education facilities and a high majority of selected substance use in comparison with the results found in the ESPAD (2015, 2019) study in the general school population of Czechia. The findings support a systemic focus on the prevention of health risks, harm reduction, as well as the treatment of substance use disorder among adolescents. Data on the incidence and prevalence of substance use should be regularly collected and evaluated in these facilities. [ABSTRACT FROM AUTHOR]

Goals: This study aims to identify sociodemographic predictors of breastfeeding and evaluate the effect of nursing interventions on nutritional strategies in premature infants (0-6 months). Methods: A prospective monocentric longitudinal study focused on the nutritional management of 201 preterm infants. Monitoring was performed for two years at specific time intervals. Results: The mother's nationality influences the child's nutrition in the first weeks but this influence decreases with the child's age. The mother's education has a significant effect on nutritional choices. Higher education correlates with using different nutritional combinations. Strong predictors of breastfeeding include the first latch, skin-to-skin contact, and orofacial stimulation. Conclusion: The study identified vital factors influencing the nutrition of preterm infants. These findings enable better targeting of measures and support better health and development of such children. The results provide a scientific basis for the development of nutritional strategies and interventions. [ABSTRACT FROM AUTHOR]

Parkinson's disease (PD) and essential tremor (ET) are prevalent movement disorders that mainly affect elderly people, presenting diagnostic challenges due to shared clinical features. While both disorders exhibit distinct speech patterns—hypokinetic dysarthria in PD and hyperkinetic dysarthria in ET—the efficacy of speech assessment for differentiation remains unexplored. Developing technology for automatic discrimination could enable early diagnosis and continuous monitoring. However, the lack of data for investigating speech behavior in these patients has inhibited the development of a framework for diagnostic support. In addition, phonetic variability across languages poses practical challenges in establishing a universal speech assessment system. Therefore, it is necessary to develop models robust to the phonetic variability present in different languages worldwide. We propose a method based on Gaussian mixture models to assess domain adaptation from models trained in German and Spanish to classify PD and ET patients in Czech. We modeled three different speech dimensions: articulation, phonation, and prosody and evaluated the models' performance in both bi-class and tri-class classification scenarios (with the addition of healthy controls). Our results show that a fusion of the three speech dimensions achieved optimal results in binary classification, with accuracies up to 81.4 and 86.2% for monologue and /pa-ta-ka/ tasks, respectively. In tri-class scenarios, incorporating healthy speech signals resulted in accuracies of 63.3 and 71.6% for monologue and /pa-ta-ka/ tasks, respectively. Our findings suggest that automated speech analysis, combined with machine learning is robust, accurate, and can be adapted to different languages to distinguish between PD and ET patients. [ABSTRACT FROM AUTHOR]