ABSTRACT
Background: Coronavirus 2 is the cause of COVID-19, a hazardous respiratory disease (SARS-CoV-2). More than 80% of hospitalized patients and 30% of COVID-19 survivors may have long-term effects. The most prevalent and incapacitating symptoms of the post-COVID-19 syndrome are thought to be fatigue and cognitive impairment. Objective: The major objective of the current study is to trace fatigue affected post-COVID survivors’ cognitive function. Subjects and Methods: In this study, 84 cases were enlisted, and they were subdivided into two groups. The study group consisted of 42 post-COVID survivors, and the control group consisted of 42 healthy individuals who were age-and sex-matched. Addenbrooke's cognitive examination revised scale, the fatigue rating scale, and the computer-based Cognitive Assessment therapy (Rehacom system) were adopted in the current study to evaluate each case. Results: A clear negative correlation was found between the FSS scores and the ACE-R (r =-0.98, p = 0.001), as well as between the FSS scores and the degree of logical thinking difficulty (r =-0.74, p = 0.001) and the FSS scores and the level of figural memory difficulty (r =-0.93, p = 0.001). Clear positive correlation were detected between the FSS scores and the first quartile response time (r = 0.94, p = 0.001), the third quartile reaction time (r = 0.96, p = 0.001), the acquisition time (r = 0.97, p = 0.001), and the solution time (r = 0.98, p = 0.001). Conclusion: In post-COVID survivors, fatigue has a major impact on cognitive abilities.
ABSTRACT
To date, despite the great debate regarding the best seating arrangement for learning in classrooms, no empirical studies have examined the direct effects of different seating arrangements on children's cognitive processes. This is particularly important nowadays that the COVID-19 measures include maintaining distance in the classroom. Aim of this study was experimentally investigating the effect of changing the seating arrangement (clusters vs. single desks), on logical reasoning, creativity and theory of mind, in children attending primary school. Furthermore, some individual characteristics (e.g., gender, loneliness, popularity) were analysed as potential moderators. Results on 77 participants showed that, when children were seated in single desks, their score in logical reasoning was globally higher. Furthermore, when seated in single desks, girls showed a better performance in the theory of mind, and lonelier children performed better in theory of mind and creativity. This on field experimental study suggests the importance of considering both the nature of the task and children's individual characteristics when deciding on a seating arrangement in the classroom.
ABSTRACT
Background-Aim: The inflammatory cascade in patients (pts) with COVID-19 may lead to pulmonary embolism (PE), worsening prognosis. Lung perfusion SPECT/CT (Q-scan) in symptomatic pts discharged after COVID-19 can confirm or rule out pulmonary vascular involvement, helping the differential diagnosis with other respiratory diseases. We aim to investigate an innovative methodology, based on radiomic features and formal methods, as a virtual second look able to detect perfusion abnormalities to better define appropriate patient-centered diagnostic and therapeutic strategies. Methods: A total of 23 pts with a recent history of COVID-19, without any previous pulmonary disease (e.g. lung cancer, emphysema, or pathological findings at CT such as lung bullae) were enrolled for Q-scan for persistent dyspnea 1 month after discharge. They were classified as negative (14 pts) and positive (9 pts) for lung perfusion abnormalities by visual and semiquantitative analysis. Q-Lung® software by GE Healthcare was used to obtain percent evaluation of pulmonary lobar perfusion (cts/volume % for each lobe), assuming as a normal value any defect lower than 10% for each lobe. We analysed these data using an innovative methodology based on formal methods techniques centered on mathematical logical reasoning, to build a formal and rigorous representation of a system merging patients clinical conditions and disease-specific characteristics, to confirm or exclude the disease. Results: In a comparative analysis with Q-Scan results, the model showed concordant features in 13/23 pts, identifying perfusion defects in 8/9 pts with a positive Q-Scan, and excluding perfusion defects in 5/14 pts with a negative Q-Scan. Discordant results were observed in the remaining 10/23 pts, in particular in negative pts: however, in this sub-group, the Q-Lung semiquantitative analysis revealed perfusion defects lower than 10% per lobe, which we considered unsignificant but may deserve further evaluation. Conclusions: Although our data are still preliminary and based on a limited population, this methodology based on formal methods showed promising concordance with Q-scan results and needs to be implemented with further analyses including co-registered CT data. When compared to artificial intelligence techniques, this mathematical reasoning may enable (i) to use a reduced dataset of patients and/ or images, without having any impact on the robustness of the model;(ii) to produce an intuitive model easy to understand;(iii) to represent a rigorous and formal tool that may be used by medical specialists in a clinical setting.