prospective analysis
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The regulation of the cryptocurrency market is becoming an increasingly debated topic in the context of the transition to the digital economy and the health security procedures adopted by the authorities in this period dominated by the pandemic and economic crisis. In this context, we propose a prospective analysis of the effects of legislative regulation and the shift to the cryptocurrency market as a unit supporting digitization. The methods and procedures used in the analysis aim to obtain the staging of the interaction between the accounting system and the cryptocurrency trading processes. Thus, we will address the issue of the digital economy and the effects produced by it, and the result of this approach will be to identify viable solutions that will prevent certain effects on financial reporting and limit tax evasion or other money laundering techniques as a result of the widespread transition to this trading system. The results obtained will be useful to economic decision-makers and tax authorities concerned with these aspects of economic development.

Abstract Background Psychosocial working conditions were previously analyzed using the first recruitment wave of the Gutenberg Health Study (GHS) cohort (n = 5000). We aimed to confirm the initial analysis using the entire GHS population at baseline (N = 15,010) and at the five-year follow-up. We also aimed to determine the effects of psychosocial working conditions at baseline on self-rated outcomes measured at follow-up. Methods At baseline, working GHS participants were assessed with either the Effort-Reward-Imbalance questionnaire (ERI) (n = 4358) or with the Copenhagen Psychosocial Questionnaire (COPSOQ) (n = 4322); participants still working after five years received the same questionnaire again (ERI n = 3142; COPSOQ n = 3091). We analyzed the association between working conditions and the outcomes job satisfaction, general health, burnout, and satisfaction with life at baseline, at follow-up and also prospectively from baseline to follow-up using linear regression models. We examined the outcome variance explained by the models (R2) to estimate the predictive performance of the questionnaires. Results The models’ R2 was comparable to the original baseline analyses at both t0 and t1 (R2 range: ERI 0.10–0.43; COPSOQ 0.10–0.56). However, selected scales of the regression models sometimes changed between assessment times. The prospective analysis showed weaker associations between baseline working conditions and outcomes after five years (R2 range: ERI 0.07–0.19; COPSOQ 0.07–0.24). This was particularly true for job satisfaction. After adjusting for the baseline levels of the outcomes, fewer scales still explained some of the variance in the distribution of the outcome variables at follow-up. The models using only data from t0 or t1 confirmed the previous baseline analysis. We observed a loss of explained variance in the prospective analysis models. This loss was greatest for job satisfaction, suggesting that this outcome is most influenced by short-term working conditions. Conclusions Both the COPSOQ and ERI instruments show good criterion validity and adequately predict contemporaneously measured self-reported measurements of health and (occupational) well-being. However, the COPSOQ provides a more detailed picture of working conditions and might be preferable for improvment strategies in workplaces. Additional prospective research with shorter follow-up times would be beneficial for estimating dose-response relationships.

On the basis of retrospective and prospective analysis of case reports of 540 patients with recurrent endometrial hyperplasia and combined intrauterine pathology (recurrent endometrial hyperplasia + hysteromyoma and/or adenomyosis) some aspects of surgical treatment of the pathology are considered, the effectiveness of different kinds of endoscopic operations is analyzed. Potentialities of spiral computerized tomography (SCT) for diagnosis of uterine disease are examined.

Abstract Syndromic surveillance for respiratory disease is limited by an inability to monitor its protean manifestation, cough. Advances in artificial intelligence provide the ability to passively monitor cough at individual and community levels. We hypothesized that changes in the aggregate number of coughs recorded among a sample could serve as a lead indicator for population incidence of respiratory diseases, particularly that of COVID-19. We enrolled over 900 people from the city of Pamplona (Spain) between 2020 and 2021 and used artificial intelligence cough detection software to monitor their cough. We collected nine person-years of cough aggregated data. Coughs per hour surged around the time cohort subjects sought medical care. There was a weak temporal correlation between aggregated coughs and the incidence of COVID-19 in the local population. We propose that a clearer correlation with COVID-19 incidence could be achieved with better penetration and compliance with cough monitoring.