Nurses' Perception of Tension, Stress, and Pressure before and during the COVID-19 Pandemic: A Multicenter Serbian Study.

The mental health of healthcare workers, especially the nursing staff in intensive care units, is crucial for the optimal functioning of healthcare systems during medical emergencies. This study implements a cross-sectional design to investigate the associations between nurses' personal characteristics, workplace challenges, and job satisfaction with the increased perception of tension, stress, and pressure at the workplace (TSPW) before and during the COVID-19 pandemic. In 2021, we surveyed 4210 nurses from 19 intensive healthcare facilities in the capital of Serbia, Belgrade, and, at that time, collected data about their perceived TSPW before and during the COVID-19 pandemic. Our study identified six predictors of the increase in TSPW, as perceived by nurses: their work in COVID-19 infectious zones (OR = 1.446), exhaustion due to work under protective equipment (OR = 1.413), uncertainty and fear of infection (OR = 1.481), a high degree of superiors' appreciation and respect (OR = 1.147), a high degree of patients' attitudes (OR = 1.111), and a low degree of work autonomy (OR = 0.889). The study's findings suggest that a solution to this issue is necessary to ensure that nurses are safe and able to alleviate the physical and mental strain that comes with prolonged use of protective equipment. Nurses on the frontline of the pandemic require better health protection, better conditions, and respect for their role. Strategies to promote mental health would help reduce nurses' stress and increase job satisfaction.

The Influence of Treatment Modalities on the Quality of Life of Psoriasis Patients.

Background: We aimed to identify the quality of life (QoL) of patients with psoriasis, to determine the possible differences depending on the therapeutic modalities (biologic, conventional treatment and phototherapy), and to examine other variables that could affect the success of the treatment. Methods: This research was a non-experimental, quantitative, observational study that included 183 psoriasis patients. The study was conducted from November 2021 to December 2022 at the University Clinical Center Kragujevac, Serbia. The following instruments were used: Dermatology Life Quality Index (DLQI), Psoriasis Area and Severity Index (PASI), as well as a general questionnaire that contained a set of questions which referred to sociodemographic data. Results: There was a statistically significant difference in the average values of the DLQI score concerning the application of different therapeutic modalities (P<0.001). Biologic treatment was the modality with the lowest impairments in the QoL domain (average value of DLQI score 10.6±7.3), followed by patients on conventional treatment (average value of DLQI score 12.9±7.9), and the highest levels of impaired QoL were in patients who received phototherapy (average value of the DLQI score 13.7±9.3). Conclusion: Patients on biological therapy at all four time points individually (baseline, 4, 12 and 16 weeks) had the lowest average values of the DLQI score, i.e. the best QoL compared to subjects who received other therapy.

The Burden of Disease due to COVID-19 (BoCO-19): A study protocol for a secondary analysis of surveillance data in Southern and Eastern Europe, and Central Asia.

INTRODUCTION: The COVID-19 pandemic has had an extensive impact on public health worldwide. However, in many countries burden of disease indicators for COVID-19 have not yet been calculated or used for monitoring. The present study protocol describes an approach developed in the project "The Burden of Disease due to COVID-19. Towards a harmonization of population health metrics for the surveillance of dynamic outbreaks" (BoCO-19). The process of data collection and aggregation across 14 different countries and sub-national regions in Southern and Eastern Europe and Central Asia is described, as well as the methodological approaches used. MATERIALS AND METHODS: The study implemented in BoCO-19 is a secondary data analysis, using information from national surveillance systems as part of mandatory reporting on notifiable diseases. A customized data collection template is used to gather aggregated data on population size as well as COVID-19 cases and deaths. Years of life lost (YLL), as one component of the number of Disability Adjusted Life Years (DALY), are calculated as described in a recently proposed COVID-19 disease model (the 'Burden-EU' model) for the calculation of DALY. All-cause mortality data are collected for excess mortality sensitivity analyses. For the calculation of Years lived with disability (YLD), the Burden-EU model is adapted based on recent evidence. Because Covid-19 cases vary in terms of disease severity, the possibility and suitability of applying a uniform severity distribution of cases across all countries and sub-national regions will be explored. An approach recently developed for the Global Burden of Disease Study, that considers post-acute consequences of COVID-19, is likely to be adopted. Findings will be compared to explore the quality and usability of the existing data, to identify trends across age-groups and sexes and to formulate recommendations concerning potential improvements in data availability and quality. DISCUSSION: BoCO-19 serves as a collaborative platform in order to build international capacity for the calculation of burden of disease indicators, and to support national experts in the analysis and interpretation of country-specific data, including their strengths and weaknesses. Challenges include inherent differences in data collection and reporting systems between countries, as well as assumptions that have to be made during the calculation process.

The burden of breast, cervical, and colon and rectum cancer in the Balkan countries, 1990-2019 and forecast to 2030.

BACKGROUND: Despite effective prevention and control strategies, in countries of the Balkan region, cancers are the second leading cause of mortality, closely following circulatory system diseases. OBJECTIVE: To describe trends in the burden of breast, cervical, and colon and rectum cancer in the Balkan region and per country between 1990 and 2019, including a forecast to 2030. METHODS: We described the 2019 Global Burden of Disease (GBD) estimates for breast, cervical, and colon and rectum cancers in eleven Balkan countries over the period 1990-2019, including incidence, years lived with disability (YLD), years of life lost (YLL), and disability-adjusted life years (DALYs) rates per 100,000 population and accompanied 95% uncertainty interval. With the Autoregressive Integrated Moving Average, we forecasted these rates per country up to 2030. RESULTS: In the Balkan region, the highest incidence and DALYs rates in the study period were for colon and rectum, and breast cancers. Over the study period, the DALYs rates for breast cancer per 100,000 population were the highest in Serbia (reaching 670.84 in 2019) but the lowest in Albania (reaching 271.24 in 2019). In 2019, the highest incidence of breast cancer (85 /100,000) and highest YLD rate (64 /100,000) were observed in Greece. Romania had the highest incidence rates, YLD rates, DALY rates, and YLL rates of cervical cancer, with respective 20.59%, 23.39% 4.00%, and 3.47% increases for the 1990/2019 period, and the highest forecasted burden for cervical cancer in 2030. The highest incidence rates, YLD rates and DALY rates of colon and rectum cancers were continuously recorded in Croatia (an increase of 130.75%, 48.23%, and 63.28%, respectively), while the highest YLL rates were in Bulgaria (an increase of 63.85%). The YLL rates due to colon and rectum cancers are forecasted to progress by 2030 in all Balkan countries. CONCLUSION: As most of the DALYs burden for breast, cervical, and colon and rectum cancer is due to premature mortality, the numerous country-specific barriers to cancer early detection and quality and care continuum should be a public priority of multi-stakeholder collaboration in the Balkan region.

Measuring the Burden of Infodemics: Summary of the Methods and Results of the Fifth WHO Infodemic Management Conference.

Background: An infodemic is excess information, including false or misleading information, that spreads in digital and physical environments during a public health emergency. The COVID-19 pandemic has been accompanied by an unprecedented global infodemic that has led to confusion about the benefits of medical and public health interventions, with substantial impact on risk-taking and health-seeking behaviors, eroding trust in health authorities and compromising the effectiveness of public health responses and policies. Standardized measures are needed to quantify the harmful impacts of the infodemic in a systematic and methodologically robust manner, as well as harmonizing highly divergent approaches currently explored for this purpose. This can serve as a foundation for a systematic, evidence-based approach to monitoring, identifying, and mitigating future infodemic harms in emergency preparedness and prevention. Objective: In this paper, we summarize the Fifth World Health Organization (WHO) Infodemic Management Conference structure, proceedings, outcomes, and proposed actions seeking to identify the interdisciplinary approaches and frameworks needed to enable the measurement of the burden of infodemics. Methods: An iterative human-centered design (HCD) approach and concept mapping were used to facilitate focused discussions and allow for the generation of actionable outcomes and recommendations. The discussions included 86 participants representing diverse scientific disciplines and health authorities from 28 countries across all WHO regions, along with observers from civil society and global public health-implementing partners. A thematic map capturing the concepts matching the key contributing factors to the public health burden of infodemics was used throughout the conference to frame and contextualize discussions. Five key areas for immediate action were identified. Results: The 5 key areas for the development of metrics to assess the burden of infodemics and associated interventions included (1) developing standardized definitions and ensuring the adoption thereof; (2) improving the map of concepts influencing the burden of infodemics; (3) conducting a review of evidence, tools, and data sources; (4) setting up a technical working group; and (5) addressing immediate priorities for postpandemic recovery and resilience building. The summary report consolidated group input toward a common vocabulary with standardized terms, concepts, study designs, measures, and tools to estimate the burden of infodemics and the effectiveness of infodemic management interventions. Conclusions: Standardizing measurement is the basis for documenting the burden of infodemics on health systems and population health during emergencies. Investment is needed into the development of practical, affordable, evidence-based, and systematic methods that are legally and ethically balanced for monitoring infodemics; generating diagnostics, infodemic insights, and recommendations; and developing interventions, action-oriented guidance, policies, support options, mechanisms, and tools for infodemic managers and emergency program managers.

Attitudes towards COVID-19 vaccination and intention to get vaccinated in Western Balkans: cross-sectional survey.

BACKGROUND: Introduction of vaccines against COVID-19 has not encountered expected acceptance. The uptake of COVID-19 vaccines in Western Balkans countries is lagging behind the European Union average. The aim of our study was to assess the intention to get vaccinated against COVID-19 in the population of unvaccinated adult citizens of five Western Balkans countries, and to explore factors that influence the vaccination intention. METHODS: Cross-sectional study was conducted in the period from July to October 2021. The questionnaire was shared through online social media. Intention to get vaccinated against COVID-19 was measured by a single item assessing the likelihood of getting vaccinated on a 5-points Likert scale. Linear regressions were conducted with socio-demographic characteristics, presence of chronic diseases and attitudes towards COVID-19 vaccination as independent factors. RESULTS: The largest proportion of unvaccinated respondents willing to get vaccinated in the future was observed in Montenegro and Albania (40.4% in each country), while in the Serbian sample, the willingness to get vaccinated was the lowest (22.6%). Socio-demographic characteristics were not significantly associated with the intention to get vaccinated against COVID-19 in most of the countries. In Albania, Bosnia and Herzegovina, North Macedonia and Serbia the strongest determinant of COVID-19 vaccination intention was the higher sense of social responsibility. CONCLUSIONS: Vaccination interventions and campaigns aiming to improve the COVID-19 vaccine uptake should be focussed on specific set of factors in each country, appealing to social responsibility as most prevalent determinant of vaccination intention in Western Balkans.

Participation in Bullying and Associated Health Characteristics, Risk Factors and Leisure Activities: A Profile of School-Age Children in Serbia.

The aim of this study was to examine the prevalence and association of school-age children's participation in bullying, focusing on their health characteristics, risk factors, and leisure activities. We performed a secondary analysis of the original data of the 2017 HBSC study to examine participation in bullying once and multiple times among school-age children in Serbia. For this purpose, a nationally representative sample of 3267 children from 64 primary and high schools in the Republic of Serbia was evaluated. The outcome variable of interest in our study was participation in bullying. Further groups of individual variables such as health characteristics, risk factors, and leisure activities were assessed. Multivariate regression analysis indicated that children who felt everyday stomach pain, irritability or bad mood, and nervousness were more likely to participate in bullying at least once compared with those who rarely or never had such symptoms by 1.46, 1.58, and 1.58 times, respectively. School-age children who reported being drunk two to three times, and four or more times in life were more likely to participate in bullying than those who reported never being drunk by 1.53 and 1.74 times, respectively. Children who reported to watch TV or other media for five or more hours per day were 2.34 times more likely to be involved in bullying at least once. Multiple regression analysis showed that students with daily stomach pain, back pain, nervousness, and dizziness were more likely to be involved in multiple bullying by 1.16, 1.62, 1.82, and 1.70 times, respectively. Students who had nightly meetings or reported being drunk four or more times in the last 30 days were more likely to be involved in multiple bullying by 2.54 and 3.47, respectively. Students who reported playing games five or more times per day were 2.70 times more likely to be involved in this multiple bullying. This study highlights the importance of professional and family education programmes for early identification of specific health symptoms in the pediatric population, as well as integration with interventions aimed at reducing alcohol abuse among school-age children.

Impact of Accurate Detection of Freeway Traffic Conditions on the Dynamic Pricing: A Case Study of I-95 Express Lanes.

Express lanes (ELs) implementation is a proven strategy to deal with freeway traffic congestion. Dynamic toll pricing schemes effectively achieve reliable travel time on ELs. The primary inputs for the typical dynamic pricing algorithms are vehicular volumes and speeds derived from the data collected by sensors installed along the ELs. Thus, the operation of dynamic pricing critically depends on the accuracy of data collected by such traffic sensors. However, no previous research has been conducted to explicitly investigate the impact of sensor failures and erroneous sensors' data on toll computations. This research fills this gap by examining the effects of sensor failure and faulty detection scenarios on ELs tolls calculated by a dynamic pricing algorithm. The paper's methodology relies on applying the dynamic toll pricing algorithm implemented in the field and utilizing the fundamental speed-volume relationship to 'simulate' the sensors' reported data. We implemented the methodology in a case study of ELs on Interstate-95 in Southeast Florida. The results have shown that the tolls increase when sensors erroneously report higher than actual traffic demand. Moreover, it has been found that the accuracy of individual sensors and the number of sensors utilized to estimate traffic conditions are critical for accurate toll calculations.

Simulation and prediction of spread of COVID-19 in The Republic of Serbia by SEAIHRDS model of disease transmission.

As a response to the pandemic caused by SARS-Cov-2 virus, on 15 March 2020, the Republic of Serbia introduced comprehensive anti-epidemic measures to curb COVID-19. After a slowdown in the epidemic, on 6 May 2020, the regulatory authorities decided to relax the implemented measures. However, the epidemiological situation soon worsened again. As of 7 February 2021, a total of 406,352 cases of SARSCov-2 infection have been reported in Serbia, 4,112 deaths caused by COVID-19. In order to better understand the epidemic dynamics and predict possible outcomes, we have developed an adaptive mathematical model SEAIHRDS (S-susceptible, E-exposed, A-asymptomatic, I-infected, H-hospitalized, R-recovered, d-dead due to COVID-19 infection, S-susceptible). The model can be used to simulate various scenarios of the implemented intervention measures and calculate possible epidemic outcomes, including the necessary hospital capacities. Considering promising results regarding the development of a vaccine against COVID-19, the model is extended to simulate vaccination among different population strata. The findings from various simulation scenarios have shown that, with implementation of strict measures of contact reduction, it is possible to control COVID-19 and reduce number of deaths. The findings also show that limiting effective contacts within the most susceptible population strata merits a special attention. However, the findings also show that the disease has a potential to remain in the population for a long time, likely with a seasonal pattern. If a vaccine, with efficacy equal or higher than 65%, becomes available it could help to significantly slow down or completely stop circulation of the virus in human population. The effects of vaccination depend primarily on: 1. Efficacy of available vaccine(s), 2. Prioritization of the population categories for vaccination, and 3. Overall vaccination coverage of the population, assuming that the vaccine(s) develop solid immunity in vaccinated individuals. With expected basic reproduction number of Ro=2.46 and vaccine efficacy of 68%, an 87% coverage would be sufficient to stop the virus circulation.

An Automatic Car Counting System Using OverFeat Framework.

Automatic car counting is an important component in the automated traffic system. Car counting is very important to understand the traffic load and optimize the traffic signals. In this paper, we implemented the Gaussian Background Subtraction Method and OverFeat Framework to count cars. OverFeat Framework is a combination of Convolution Neural Network (CNN) and one machine learning classifier (like Support Vector Machines (SVM) or Logistic Regression). With this study, we showed another possible application area for the OverFeat Framework. The advantages and shortcomings of the Background Subtraction Method and OverFeat Framework were analyzed using six individual traffic videos with different perspectives, such as camera angles, weather conditions and time of the day. In addition, we compared the two algorithms above with manual counting and a commercial software called Placemeter. The OverFeat Framework showed significant potential in the field of car counting with the average accuracy of 96.55% in our experiment.

GraphCrunch 2: Software tool for network modeling, alignment and clustering.

BACKGROUND: Recent advancements in experimental biotechnology have produced large amounts of protein-protein interaction (PPI) data. The topology of PPI networks is believed to have a strong link to their function. Hence, the abundance of PPI data for many organisms stimulates the development of computational techniques for the modeling, comparison, alignment, and clustering of networks. In addition, finding representative models for PPI networks will improve our understanding of the cell just as a model of gravity has helped us understand planetary motion. To decide if a model is representative, we need quantitative comparisons of model networks to real ones. However, exact network comparison is computationally intractable and therefore several heuristics have been used instead. Some of these heuristics are easily computable "network properties," such as the degree distribution, or the clustering coefficient. An important special case of network comparison is the network alignment problem. Analogous to sequence alignment, this problem asks to find the "best" mapping between regions in two networks. It is expected that network alignment might have as strong an impact on our understanding of biology as sequence alignment has had. Topology-based clustering of nodes in PPI networks is another example of an important network analysis problem that can uncover relationships between interaction patterns and phenotype. RESULTS: We introduce the GraphCrunch 2 software tool, which addresses these problems. It is a significant extension of GraphCrunch which implements the most popular random network models and compares them with the data networks with respect to many network properties. Also, GraphCrunch 2 implements the GRAph ALigner algorithm ("GRAAL") for purely topological network alignment. GRAAL can align any pair of networks and exposes large, dense, contiguous regions of topological and functional similarities far larger than any other existing tool. Finally, GraphCruch 2 implements an algorithm for clustering nodes within a network based solely on their topological similarities. Using GraphCrunch 2, we demonstrate that eukaryotic and viral PPI networks may belong to different graph model families and show that topology-based clustering can reveal important functional similarities between proteins within yeast and human PPI networks. CONCLUSIONS: GraphCrunch 2 is a software tool that implements the latest research on biological network analysis. It parallelizes computationally intensive tasks to fully utilize the potential of modern multi-core CPUs. It is open-source and freely available for research use. It runs under the Windows and Linux platforms.

Geometric evolutionary dynamics of protein interaction networks.

Understanding the evolution and structure of protein-protein interaction (PPI) networks is a central problem of systems biology. Since most processes in the cell are carried out by groups of proteins acting together, a theoretical model of how PPI networks develop based on duplications and mutations is an essential ingredient for understanding the complex wiring of the cell. Many different network models have been proposed, from those that follow power-law degree distributions and those that model complementarity of protein binding domains, to those that have geometric properties. Here, we introduce a new model for PPI network (and thus gene) evolution that produces well-fitting network models for currently available PPI networks. The model integrates geometric network properties with evolutionary dynamics of PPI network evolution.