Ustundag, Hilal; Akbaba, Özge; Huyut, Mehmet

Mask use during the coronavirus disease 2019 (COVID-19) pandemic has been widely recommended and mandated worldwide. However, there is a lack of comprehensive research on the potential adverse health effects of mask usage. This study aimed to investigate and evaluate the negative effects of surgical mask use on scientifically proven cardiopulmonary functions in undergraduate and associate degree students, as well as its impact on coronaphobia. A total of 145 volunteer university students (49 males, 96 females, with a mean age of 20 years) were enrolled in the study, which consisted of two 120-minute sessions. Blood oxygen saturation, heart rate, and blood pressure were assessed before and immediately after each session. The Coronavirus-19 Phobia Scale was utilized to measure levels of COVID-19 phobia. While a time-dependent decrease in oxygen saturation level, blood pressure, and heart rate was measured when vital signs were evaluated at 1 and 120 minutes, none of the values fell outside the reference range. The study also investigated the effects of mask use on various symptoms including headaches, visual impairment, facial discomfort, earaches, shortness of breath, and anxiety. Significantly increased occurrences of all these symptoms were observed at the 60 th and 120 th minute compared with the baseline. The participants enrolled in the study demonstrated a moderate level of COVID-19 phobia based on the mean total score. Furthermore, high scores were recorded in the psychological and social sub-dimensions, while lower scores were recorded in the economic and psychosomatic sub-dimensions. In the post-COVID-19 normalization phase, the use of a surgical mask during a 120-minute course was found to have no significant impact on cardiopulmonary functions, but moderately affected coronaphobia scores.

Mertoglu, Cuma; Huyut, Mehmet; Olmez, Hasan; Tosun, Mustafa; Kantarci, Mecit; Coban, Taha

Coronavirus disease 2019 (COVID-19) triggers important changes in routine blood tests. In this retrospective case-control study, biochemical, hematological and inflammatory biomarkers between March 10, 2020, and November 30, 2020 from 3969 COVID-19 patients (3746 in the non-intensive care unit (non-ICU) group and 223 in the ICU group) were analyzed by dividing into three groups as spring, summer and autumn. In the non-ICU group, lymphocyte to monocyte ratio was lower in autumn than the other two seasons and neutrophil to lymphocyte ratio was higher in autumn than the other two seasons. Also, monocyte and platelet were higher in spring than autumn; and eosinophil, hematocrit, hemoglobin, lymphocyte, and red blood cells decreased from spring to autumn. In the non-ICU group, alanine aminotransferase and gamma-glutamyltransferase gradually increased from spring to autumn, while albumin, alkaline phosphatase, calcium, total bilirubin and total protein gradually decreased. Additionally, C-reactive protein was higher in autumn than the other seasons, erythrocyte sedimentation rate was higher in autumn than summer. The changes in routine blood biomarkers in COVID-19 varied from the emergence of the disease until now. Also, the timely changes of blood biomarkers were mostly more negative, indicating that the disease progresses severely. The study was approved by the Erzincan Binali Yildirim University Non-interventional Clinical Trials Ethic Committee (approval No. 86041) on June 21, 2021.

Huyut, Mehmet; Üstündaǧ, Hilal

The coronavirus disease 2019 (COVID-19) epidemic went down in history as a pandemic caused by corona-viruses that emerged in 2019 and spread rapidly around the world. The different symptoms of COVID-19 made it difficult to understand which variables were more influential on the diagnosis, course and mortality of the disease. Machine learning models can accurately assess hidden patterns among risk factors by analyzing large-datasets to quickly predict diagnosis, prognosis and mortality of diseases. Because of this advantage, the use of machine learning models as decision support systems in health services is increasing. The aim of this study is to determine the diagnosis and prognosis of COVID-19 disease with blood-gas data using the Chi-squared Automatic Interaction Detector (CHAID) decision-tree-model, one of the machine learning methods, which is a subfield of artificial intelligence. This study was carried out on a total of 686 patients with COVID-19 (n = 343) and non-COVID-19 (n = 343) treated at Erzincan-Mengücek-Gazi-Training and Research-Hospital between April 1, 2020 and March 1, 2021. Arterial blood gas values of all patients were obtained from the hospital registry system. While the total-accuracyratio of the decision-tree-model was 65.0% in predicting the prognosis of the disease, it was 68.2% in the diagnosis of the disease. According to the results obtained, the low ionized-calcium value (< 1.10 mM) significantly predicted the need for intensive care of COVID-19 patients. At admission, low-carboxyhemoglobin (< 1.00%), high-pH (> 7.43), low-sodium (< 135.0 mM), hematocrit (< 40.0%), and methemoglobin (< 1.30%) values are important biomarkers in the diagnosis of COVID-19 and the results were promising. The findings in the study may aid in the early-diagnosis of the disease and the intensive-care treatment of patients who are severe. The study was approved by the Ministry of Health and Erzincan University Faculty of Medicine Clinical Research Ethics Committee.