ABSTRACT
The COVID-19 pandemic is associated with negative mental outcomes in the early postpartum period. Objective: To assess the long-term postpartum mental health of women infected with COVID-19 during pregnancy. Methods: Cross-sectional study in 101 pregnant women who gave birth in a tertiary center during the COVID-19 pandemic, between March 31, 2020, and November 30, 2021. The pregnant women were classified into 2 groups as COVID-19 positive (study group, n=52) and COVID-19 negative (control group, n=49). Sociodemographic and obstetric data were collected by questionnaire in the early (≤6 months) and late (6-18 months) postpartum periods. Beck Depression Inventory (BDI) and Beck Anxiety Inventory (BAI) scores were calculated by analysis of the participants' data. Results: The mean BDI score and the rate of depression (BDI score >13) in COVID-19 positive patients were higher in the early postpartum period than in the late postpartum period. According to multivariate linear regression analysis, there was a significant correlation between the BDI score of COVID-19 patients and educational level and employment status. According to the same analysis, there was a significant correlation between the BAI score of COVID-19 patients and spousal support, marital relationship, and birth-related diseases. We found that COVID-19 positive and COVID-19 negative patients had similar BDI and BAI scores in the early (≤6 months) and late (6-18 months) postpartum periods. In addition, rates of anxiety and depression were similar in both groups at the same postpartum periods. Conclusion: In our study, COVID-19 infection in pregnancy had no significant additional impact on long-term postpartum maternal mental health.
ABSTRACT
Type 1 Diabetes (T1DM) is a chronic metabolic disease characterized by hyperglycemia due to absolute insulin deficiency as a result of autoimmune damage of pancreatic beta cells. In its treatment, insulin, medical nutrition therapy and exercise is recommended. Although it is known that exercise contributes to disease control, the mechanism of these effects has not been fully clarified. It is thought that myokines such as irisin and sestrin, can be effective by secreting with exercise, turning white fat tissue into brown. Aim(s): This study aimed to compare serum irisin and sestrin levels between patients with T1DM and healthy controls, determine the changes in the clinical and laboratory findings of the patients with T1DM before and after exercise program and evaluate relationships of these changes with the levels of irisin and sestrin. Material(s) and Method(s): 33 patients with T1DM diagnosis and 36 control groups were involved. Firstly, exercise capacities (MaxVO2) and Physical Activity Status (PAS) were determined in T1DM and control groups and serum Irisin and sestrin levels measured in both groups. Secondly T1DM patients attended to the online exercise program 3 days a week for 3 months due to COVID19 restictions. At the end of the exercise program, 10 of the T1DM patients (exercised group) participated in at least 50% of the program. MaxVO2, PAS, serum irisin and sestrin levels were reevaluated and compared with the previous results in exercised and non-exercised groups. Their examinations were obtained from file data and computer records. Changes in laboratory and clinical findings were compared. Result(s): Sestrin level was higher in the T1DM group than in the control group (p=0.003). This is the first data on sestrin and type one diabetes. There was no significant difference in irisin levels between T1DM and control groups (p=0.511). Both groups were sedentary due to the Covid19 lockdown. There was positive correlation between Irisin and maxVO2 in the patients with T1DM and control groups (r=0,34, p=0,02). In the second part of the study, irisin levels increased in the exercised group (p=0.012) and sestrin levels decreased in the non-exercised group (p<0.001). Physical activity score improved in the exercised group (p=0,028) and exercise capacity increased in both groups (<0,001). HbA1c levels decreased (p=0,032) and basal insulin requirement decreased (p=0,038) in the exercised group, unlike the non-exercised group. Conclusion(s): Our findings suggest that the irisin and sestrin could contribute to the curative effects of exercise in type one diabetic children.
ABSTRACT
Wastewater-based disease monitoring is an early warning system and a surveillance tool for infectious disease outbreaks regarding pathogens with pandemic potential. This study aimed at investigating the recovery efficiency of centrifugal ultrafiltration (CeUF), which is one of the most-used virus concentration methods, for inactive severe acute respiratory syndrome coronavi-rus 2 (SARS-CoV-2) added to wastewater. Inactivated SARS-CoV-2 was inoculated into untreated wastewater at different concentrations (4 × 103, 8 × 103, 16 × 103, 24 × 103 and 32 × 103 gene copy/ µL) and concentrated through ultrafiltration with a disposable centrifugal filter device. Total nucleic acids in concentrated filtrates were extracted and isolated by an automated system. In isolates, total RNA was measured by a UV/VIS spectrophotometer, and the recovered virus was quanti-fied by RT-qPCR with two gene regions (N1 and N2). The recovery rates were between 11% and 17.8% (mean 15.1%, CV below 15%). While there were positive correlations among the inoculated virus, total RNA and recovered virus, there was no correlation and linearity between the recovery rates. Despite limited recovery rates, CeUF integrated with RT-qPCR quantification can be a valid assay for monitoring SARS-CoV-2 in wastewater, and an early warning system. © 2022 Desalination Publications. All rights reserved.
ABSTRACT
Objectives: This study aimed to evaluate the COVID-19 triage results of the admissions made by patients in a certain region to the Education Family Practice Center (E-FPC) during the pandemic period. Methods: Patients aged 18 years and above, who were applied to the E-FPC between March 12 and April 30, 2020, were included in the study. Every patient had filled in a triage form. Potential cases were referred to a high-level healthcare center. Polymerase chain reaction (PCR) and chest computed tomography (CT) results of the referred patients were followed up and noted. Results: Four hundred sixty-one patients were included in the study. Twenty-seven (5.9%) patients had a fever, 219 (47.5%) patients had a cough, 34 (7.4%) patients had dyspnea, and 305 (66.2%) patients had other symptoms. Eighty-six (18.6%) of the patients were admitted to the hospital for PCR test of which 15 (17.4%) had a positive test result. Seventy-one (15.4%) patients underwent a chest CT and 25 (35.2%) of them had results compatible with COVID-19. Fever was detected in 8 (53.3%) of the patients with a positive PCR result and in 6 (8.5%) patients with a negative PCR result (p<0.001). Dyspnea was detected in 13 (52.0%) patients whose chest CT results were compatible with COVID-19 and in 5 (10.9%) patients whose chest CT results were not compatible with COVID-19 (p<0.001). Conclusion: Symptoms, CT imaging, and PCR results should be evaluated together in the diagnosis of COVID-19. Triage practices should be maintained in primary healthcare centers throughout the pandemic. ©Copyright 2022 by Anatolian Journal of Family Medicine
ABSTRACT
Objective: In this study, it is aimed to provide a dynamic structure to the summary status and analysis results based on the current COVID-19 data of the countries based on changing status of global COVID-19 outbreak in the world and in Turkey;thus, to support fast and proactive decisions. In this scope, to define COVID-19 based on data, an online R-Shiny application is developed (https://elifkartal.shinyapps.io/covid19/). Material and Method: In this study, CRoss-Industry Standard Process for Data Mining - CRISP-DM is used as the study method. The changing situation of COVID-19 in global and national dimensions was evaluated. New variables are calculated such as Linear Change Rate (LCR), Exponential Growth Coefficient (EGC), and required days to double cases. Cluster analysis was performed by applying the k-Means data mining algorithm to the data reinforced with the new variables and similarities of countries were determined. The countries closest to the cluster average are accepted as cluster centers and the countries in the same cluster are ranked according to their distance from the cluster center. Results: One of the most important findings of the study is that the trends of LCR and EGC are the same. As such, it can be said that COVID-19 does not display an exponential behavior or can be controlled. With the developed application, the countries in which the cluster is located, regardless of their geographical location and dynamically according to time, the possible risk situations and similarities of the countries in the same cluster have been determined more precisely. Conclusion: With this study and the application developed;depending on changing status of global COVID-19 outbreak in the world and in Turkey, a dynamic structure has been given to the summary status and analysis results based on the current COVID-19 data of the countries, thus, it has been provided to support fast and proactive decisions.
ABSTRACT
Objective: In this study, it is aimed to provide a dynamic structure to the summary status and analysis results based on the current COVID-19 data of the countries based on changing status of global COVID-19 outbreak in the world and in Turkey;thus, to support fast and proactive decisions. In this scope, to define COVID-19 based on data, an online R-Shiny application is developed (https://elifkartal.shinyapps.io/covid19/). Material and Method: In this study, CRoss-Industry Standard Process for Data Mining - CRISP-DM is used as the study method. The changing situation of COVID-19 in global and national dimensions was evaluated. New variables are calculated such as Linear Change Rate (LCR), Exponential Growth Coefficient (EGC), and required days to double cases. Cluster analysis was performed by applying the k-Means data mining algorithm to the data reinforced with the new variables and similarities of countries were determined. The countries closest to the cluster average are accepted as cluster centers and the countries in the same cluster are ranked according to their distance from the cluster center.