Introduction: Objective of this study is to describe impact of gender, age, social status, and geographic location on mobility changes during the COVID-19 pandemic within the Czechia, Hradec Kralove region, and Ostrava region.Methods: A cross-sectional study was carried out in two regions in the Czechia: the Hradec Kralove region and the Ostrava region.Results: The age group of seniors 85 and older was more vulnerable to these alterations than other age groups. Age had a statistically significant impact on both the frequency of trips and the mode of transportation used. Seniors' shifts in mobility were more frequently impacted by urbanization, whereas the region's impact was seen in as many as five components. Transport, Route, and Time all showed the impact of urbanization. However, the region had the largest impact.Conclusion: There has been little evidence of the influence of age, gender, or social class on perceptions of changes during COVID-19. Research found conflicting evidence about older adults' physical activity throughout the epidemic.
- MeSH
- COVID-19 * MeSH
- doprava MeSH
- lidé MeSH
- omezení pohyblivosti MeSH
- průzkumy a dotazníky MeSH
- senioři nad 80 let MeSH
- senioři MeSH
- sociální izolace * MeSH
- socioekonomické faktory MeSH
- Check Tag
- lidé MeSH
- senioři nad 80 let MeSH
- senioři MeSH
- Geografické názvy
- Česká republika MeSH
BACKGROUND: Maintaining mobility is fundamental to active aging, allowing older adults to lead dynamic and independent lives. The perception of mobility among older adults significantly impacts their overall well-being and quality of life. Given the aging population, mobility has become an increasingly pressing issue. AIM: This study focused on the perception of urban neighborhoods, including considerations of urban tissue (crossings and sidewalk maintenance), urban scenes (benches and traffic), and safety (fears and street lighting quality). We investigated the differences in the perception of the surroundings of residences by urban and rural seniors concerning their demographic and social characteristics and environmental determinants. METHODS: A quantitative study design utilizing a questionnaire survey was employed. Data were collected mainly through face-to-face interviews in the field (PAPI) and via an online questionnaire (CAWI). The final sample comprised 525 participants. Hypotheses regarding the influence of gender, age, social status, level of physical activity, degree of urbanization, and region on environmental perception were tested using ordinal regression. RESULTS: The hypothesis regarding the dependence of the perception of the surroundings on the level of urbanization was confirmed; that regarding the dependence of the perception of the residence surroundings on seniors' age was not confirmed. The other hypotheses were partially confirmed. For the seven investigated environmental attributes, gender was significant in two cases, social status and physical activity in three cases, and region in four cases. CONCLUSION: While most studies have focused on urban settings, this study highlights the situation in rural municipalities. Substantially worse pedestrian conditions in availability of pedestrian crossings, benches, and lighting were recognized in rural municipalities versus cities. Understanding the complexity of mobility and the spatial locations relevant for older persons concerning potential barriers and facilitators for mobility aids in planning and adapting neighborhood environments to promote active and healthy aging in place.
- Publikační typ
- časopisecké články MeSH
BACKGROUND: Attention is focused on the health and physical fitness of older adults due to their increasing age. Maintaining physical abilities, including safe walking and movement, significantly contributes to the perception of health in old age. One of the early signs of declining fitness in older adults is limited mobility. Approximately one third of 70-year-olds and most 80-year-olds report restrictions on mobility in their apartments and immediate surroundings. Restriction or loss of mobility is a complex multifactorial process, which makes older adults prone to falls, injuries, and hospitalizations and worsens their quality of life while increasing overall mortality. OBJECTIVE: The objective of the study is to identify the factors that have had a significant impact on mobility in recent years and currently, and to identify gaps in our understanding of these factors. The study aims to highlight areas where further research is needed and where new and effective solutions are required. METHODS: The PRISMA methodology was used to conduct a scoping review in the Scopus and Web of Science databases. Papers published from 2007 to 2021 were searched in November 2021. Of these, 52 papers were selected from the initial 788 outputs for the final analysis. RESULTS: The final selected papers were analyzed, and the key determinants were found to be environmental, physical, cognitive, and psychosocial, which confirms the findings of previous studies. One new determinant is technological. New and effective solutions lie in understanding the interactions between different determinants of mobility, addressing environmental factors, and exploring opportunities in the context of emerging technologies, such as the integration of smart home technologies, design of accessible and age-friendly public spaces, development of policies and regulations, and exploration of innovative financing models to support the integration of assistive technologies into the lives of seniors. CONCLUSION: For an effective and comprehensive solution to support senior mobility, the determinants cannot be solved separately. Physical, cognitive, psychosocial, and technological determinants can often be perceived as the cause/motivation for mobility. Further research on these determinants can help to arrive at solutions for environmental determinants, which, in turn, will help improve mobility. Future studies should investigate financial aspects, especially since many technological solutions are expensive and not commonly available, which limits their use.
- MeSH
- chůze * MeSH
- cvičení MeSH
- databáze faktografické MeSH
- kvalita života * MeSH
- lidé MeSH
- senioři MeSH
- tělesná výkonnost MeSH
- Check Tag
- lidé MeSH
- senioři MeSH
- Publikační typ
- časopisecké články MeSH
- práce podpořená grantem MeSH
- přehledy MeSH
BACKGROND: One of the important areas of heart research is to analyze heart rate variability during (HRV) walking. OBJECTIVE: In this research, we investigated the correction between heart activation and the variations of walking paths. METHOD: We employed Shannon entropy to analyze how the information content of walking paths affects the information content of HRV. Eight healthy students walked on three designed walking paths with different information contents while we recorded their ECG signals. We computed and analyzed the Shannon entropy of the R-R interval time series (as an indicator of HRV) versus the Shannon entropy of different walking paths and accordingly evaluated their relation. RESULTS: According to the obtained results, walking on the path that contains more information leads to less information in the R-R time series. CONCLUSION: The analysis method employed in this research can be extended to analyze the relation between other physiological signals (such as brain or muscle reactions) and the walking path.
- MeSH
- časové faktory MeSH
- chůze * MeSH
- elektrokardiografie * MeSH
- entropie MeSH
- lidé MeSH
- srdeční frekvence fyziologie MeSH
- Check Tag
- lidé MeSH
- Publikační typ
- časopisecké články MeSH
BACKGROUND: Analysis of the reactions of different organs to external stimuli is an important area of research in physiological science. OBJECTIVE: In this paper, we investigated the correlation between the brain and facial muscle activities by information-based analysis of electroencephalogram (EEG) signals and electromyogram (EMG) signals using Shannon entropy. METHOD: The EEG and EMG signals of thirteen subjects were recorded during rest and auditory stimulations using relaxing, pop, and rock music. Accordingly, we calculated the Shannon entropy of these signals. RESULTS: The results showed that rock music has a greater effect on the information of EEG and EMG signals than pop music, which itself has a greater effect than relaxing music. Furthermore, a strong correlation (r= 0.9980) was found between the variations of the information of EEG and EMG signals. CONCLUSION: The activities of the facial muscle and brain are correlated in different conditions. This technique can be utilized to investigate the correlation between the activities of different organs versus brain activity in different situations.
- MeSH
- akustická stimulace MeSH
- elektroencefalografie * metody MeSH
- elektromyografie metody MeSH
- lidé MeSH
- mozek fyziologie MeSH
- obličejové svaly * fyziologie MeSH
- Check Tag
- lidé MeSH
- Publikační typ
- časopisecké články MeSH
This work analyses the results of research regarding the predisposition of genetic hematological risks associated with secondary polyglobulia. The subjects of the study were selected based on shared laboratory markers and basic clinical symptoms. JAK2 (Janus Kinase 2) mutation negativity represented the common genetic marker of the subjects in the sample of interest. A negative JAK2 mutation hypothetically excluded the presence of an autonomous myeloproliferative disease at the time of detection. The parameters studied in this work focused mainly on thrombotic, immunological, metabolic, and cardiovascular risks. The final goal of the work was to discover the most significant key markers for the diagnosis of high-risk patients and to exclude the less important or only complementary markers, which often represent a superfluous economic burden for healthcare institutions. These research results are applicable as a clinical guideline for the effective diagnosis of selected parameters that demonstrated high sensitivity and specificity. According to the results obtained in the present research, groups with a high incidence of mutations were evaluated as being at higher risk for polycythemia vera disease. It was not possible to clearly determine which of the patients examined had a higher risk of developing the disease as different combinations of mutations could manifest different symptoms of the disease. In general, the entire study group was at risk for manifestations of polycythemia vera disease without a clear diagnosis. The group with less than 20% incidence appeared to be clinically insignificant for polycythemia vera testing and thus there is a potential for saving money in mutation testing. On the other hand, the JAK V617F (somatic mutation of JAK2) parameter from this group should be investigated as it is a clear exclusion or confirmation of polycythemia vera as the primary disease.
- Publikační typ
- časopisecké články MeSH
Depression is a major depressive disorder characterized by persistent sadness and a sense of worthlessness, as well as a loss of interest in pleasurable activities, which leads to a variety of physical and emotional problems. It is a worldwide illness that affects millions of people and should be detected at an early stage to prevent negative effects on an individual's life. Electroencephalogram (EEG) is a non-invasive technique for detecting depression that analyses brain signals to determine the current mental state of depressed subjects. In this study, we propose a method for automatic feature extraction to detect depression by first constructing a graph from the dataset where the nodes represent the subjects in the dataset and where the edge weights obtained using the Euclidean distance reflect the relationship between them. The Node2vec algorithmic framework is then used to compute feature representations for nodes in a graph in the form of node embeddings ensuring that similar nodes in the graph remain near in the embedding. These node embeddings act as useful features which can be directly used by classification algorithms to determine whether a subject is depressed thus reducing the effort required for manual handcrafted feature extraction. To combine the features collected from the multiple channels of the EEG data, the method proposes three types of fusion methods: graph-level fusion, feature-level fusion, and decision-level fusion. The proposed method is tested on three publicly available datasets with 3, 20, and 128 channels, respectively, and compared to five state-of-the-art methods. The results show that the proposed method detects depression effectively with a peak accuracy of 0.933 in decision-level fusion, which is the highest among the state-of-the-art methods.
- MeSH
- algoritmy MeSH
- deprese diagnóza MeSH
- depresivní porucha unipolární * diagnóza MeSH
- elektroencefalografie MeSH
- lidé MeSH
- rozhraní mozek-počítač * MeSH
- Check Tag
- lidé MeSH
- Publikační typ
- časopisecké články MeSH
- práce podpořená grantem MeSH
Cyber-attack detection via on-gadget embedded models and cloud systems are widely used for the Internet of Medical Things (IoMT). The former has a limited computation ability, whereas the latter has a long detection time. Fog-based attack detection is alternatively used to overcome these problems. However, the current fog-based systems cannot handle the ever-increasing IoMT's big data. Moreover, they are not lightweight and are designed for network attack detection only. In this work, a hybrid (for host and network) lightweight system is proposed for early attack detection in the IoMT fog. In an adaptive online setting, six different incremental classifiers were implemented, namely a novel Weighted Hoeffding Tree Ensemble (WHTE), Incremental K-Nearest Neighbors (IKNN), Incremental Naïve Bayes (INB), Hoeffding Tree Majority Class (HTMC), Hoeffding Tree Naïve Bayes (HTNB), and Hoeffding Tree Naïve Bayes Adaptive (HTNBA). The system was benchmarked with seven heterogeneous sensors and a NetFlow data infected with nine types of recent attack. The results showed that the proposed system worked well on the lightweight fog devices with ~100% accuracy, a low detection time, and a low memory usage of less than 6 MiB. The single-criteria comparative analysis showed that the WHTE ensemble was more accurate and was less sensitive to the concept drift.
- MeSH
- Bayesova věta MeSH
- big data MeSH
- časná diagnóza MeSH
- internet věcí * MeSH
- Publikační typ
- časopisecké články MeSH
Selective, sensitive and affordable techniques to detect disease and underlying health issues have been developed recently. Biosensors as nanoanalytical tools have taken a front seat in this context. Nanotechnology-enabled progress in the health sector has aided in disease and pandemic management at a very early stage efficiently. This report reflects the state-of-the-art of nanobiosensor-based virus detection technology in terms of their detection methods, targets, limits of detection, range, sensitivity, assay time, etc. The article effectively summarizes the challenges with traditional technologies and newly emerging biosensors, including the nanotechnology-based detection kit for COVID-19; optically enhanced technology; and electrochemical, smart and wearable enabled nanobiosensors. The less explored but crucial piezoelectric nanobiosensor and the reverse transcription-loop mediated isothermal amplification (RT-LAMP)-based biosensor are also discussed here. The article could be of significance to researchers and doctors dedicated to developing potent, versatile biosensors for the rapid identification of COVID-19. This kind of report is needed for selecting suitable treatments and to avert epidemics.
Nanotechnology is gaining significant attention, with numerous biomedical applications. Silver in wound dressings, copper oxide and silver in antibacterial preparations, and zinc oxide nanoparticles as a food and cosmetic ingredient are common examples. However, adverse effects of nanoparticles in humans and the environment from extended exposure at varied concentrations have yet to be established. One of the drawbacks of employing nanoparticles is their tendency to cause oxidative stress, a significant public health concern with life-threatening consequences. Cardiovascular, renal, and respiratory problems and diabetes are among the oxidative stress-related disorders. In this context, phytoantioxidant functionalized nanoparticles could be a novel and effective alternative. In addition to performing their intended function, they can protect against oxidative damage. This review was designed by searching through various websites, books, and articles found in PubMed, Science Direct, and Google Scholar. To begin with, oxidative stress, its related diseases, and the mechanistic basis of oxidative damage caused by nanoparticles are discussed. One of the main mechanisms of action of nanoparticles was unearthed to be oxidative stress, which limits their use in humans. Secondly, the role of phytoantioxidant functionalized nanoparticles in oxidative damage prevention is critically discussed. The parameters for the characterization of nanoparticles were also discussed. The majority of silver, gold, iron, zinc oxide, and copper nanoparticles produced utilizing various plant extracts were active free radical scavengers. This potential is linked to several surface fabricated phytoconstituents, such as flavonoids and phenols. These phytoantioxidant functionalized nanoparticles could be a better alternative to nanoparticles prepared by other existing approaches.
- MeSH
- antioxidancia chemie farmakologie MeSH
- fytonutrienty chemie farmakologie MeSH
- kovové nanočástice aplikace a dávkování chemie toxicita MeSH
- lidé MeSH
- oxidační stres účinky léků MeSH
- rostlinné extrakty farmakologie MeSH
- scavengery volných radikálů farmakologie MeSH
- zvířata MeSH
- Check Tag
- lidé MeSH
- zvířata MeSH
- Publikační typ
- časopisecké články MeSH
- přehledy MeSH
