ABSTRACT
Introduction and Aim: Adipokines, both pro-inflammatory and anti-inflammatory ones, play an important role in regulation of inflammatory responses toward infections including COVID-19. The aim of the study was to investigate the role of chemerin, adiponectin and leptin concentrations in prognosis and clinical features of hospitalized COVID19 patients. Method(s): Serum levels of 3 adipokines were measured upon admission of 77 PCR-confirmed COVID-19 patients who were followed up for 6 months and grouped into 2 according to prognosis. Result(s): A total of 77 patients were included in the study. 58.4% of patients were male and the average age was 63.2+/- 18.3 years (R: 21-96). 51 patients (66.2%) had a good prognosis based on 6-month follow-up. Leucocyte number, neutrophil to lymphocyte ratio, GGT, ALP, D-Dimer, ferritin, CRP, prokalsitonin, CK, troponin, oxygen saturation at admission, presence of comorbidities or another infection were all signifactly related with prognosis of disease (p<0.05). Among adipokines only Chemerin was significantly higher in the bad prognosis group (p=0.044) and the serum levels showed a negative correlation with age (p=0.037). Leptin levels were correlated negatively with GGT levels which were significantly higher in bad prognostic group (p=0.036). The ratio of adipokines had no relation with the prognosis and the other clinical features. Conclusion(s): Higher Chemerin levels, an anti-inflammatory adipokine, were related with a worse prognosis, whereas GGT levels especially higher in bad prognostic group were shown to be inversely correlated with leptin levels (a pro-inflammatory adipokine). Anti-inflammatory response predominance at admission might be a bad prognostic clue.
ABSTRACT
Introduction and Aim: Pulmonary function tests are used in the evaluation of the respiratory system. Maneuvers during spirometry can create aerosols and cause the spread of microbiological agents such as SARS-CoV-2 virus. The measures taken due to the pandemic can negatively affect both the number and the quality of the spirometry. There are no comparative studies on this subject. Method(s): The tests conducted in the spirometry laboratory of Baskent University Hospital during November 2019 and November 2021 were evaluated and compared by 2 chest specialists. Result(s): A total of 440 patients were included in the study. 50.5% of patients were male and the average age was 61.8+/-16.5 years. The age, gender, height and weight of the patients were similar. 75.2% (331) of the tests were evaluated as successful. The most common errors in tests evaluated as erroneous were early termination (84.1%), uncooperative patients (29%) and poor effort (22.4%). The median number of tests performed for each patient was 6. Test numbers were 262 and 178 for 2019 and 2021 (p=0.011), but test success remained unchanged over the years (p=0.513). The type of errors were not different between the two periods. There was no significant difference between the 3 operators who conducted the tests and the test success (p=0.909), which was similar for both periods. However, the number of tests performed between the two periods until the successful maneuver varied significantly (p=0.009) and fewer maneuvers were required in 2021. Conclusion(s): As a result, the spirometry quality did not change with the measures of pandemic,but the decrease in number of patients significantly reduced the number of tests performed until the successful maneuver.
ABSTRACT
Recently, as a consequence of COVID-19 pandemic, the delivery of education at most of the educational institutions depended mainly on e-learning. So, the researchers give more attention for both synchronous and asynchronous e-learning. Although from an economical perspective, asynchronous e-learning seems to be the best e-learning option for institutions, still one of the biggest challenges is how to keep learners motivated for the entire learning process. One of important motivational factors that drives the success of the learning process is the learner attention. Therefore, to retain the learners' attention during the asynchronous e-learning process, we need first to detect their loss of attention. Accordingly, more studies started to focus on detecting learners’ attention. However, those studies can't be widely used for attention detection within asynchronous e-learning environments, as the used approaches tend to be inaccurate, and complex for the design and maintain. In contrast, in this study, we explore the possibility to find a simple way that can be widely used to detect learners' attention within the asynchronous e-learning environments. Therefore, we used webcams which are available in almost every laptop, and computer vision tools to detect learners' attention by tracking their faces. Thereafter, we evaluated the accuracy of our suggested method, the result of this evaluation showed that our method is efficient.