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Objectives: Aim of this study was to evaluate hepcidin levels and its correlation with inflammatory markers, vitamin D levels as well as its effects on intensive care unit (ICU) mortality in critically ill coronavirus disease-2019 (COVID-19) patients. Materials and Methods: Adult patients those were admitted to pandemic ICU between March 1st, 2021 and May 17th 2021 were prospectively included to the study. Hepcidin levels and inflammatory markers on day 1, 2, 3 and 7, admission vitamin D levels, length of ICU stay and ICU mortality were recorded and analysed. Results: Median age of patients was 60.5 (52.50-71.25) and 20 (66.7%) of them was male. It was observed that hepcidin levels and lymphocyte counts were increased significantly from day 1 to day 7 (p=0.01 and p<0.01, respectively). In contrast, C-reactive protein (CRP) and procalsitonin levels were decreased from day 1 to day 7 (p=0.01 and p<0.01, respectively). In the analysis admission hepcidin levels and inflammatory markers [IL-6 (p=0.61), CRP (p=0.82) and ferritin (p=0.27)], vitamin D (p=0.13) and iron level (p=0.90) was not correlated. There was no correlation between hepcidin levels and ICU mortality (p=0.95). Conclusion: In this study, hepcidin levels were above normal limits in critically ill COVID-19 patients. However, our findings do not support the use of hepcidin, IL6, serum ferritin, and vitamin D levels in predicting COVID-19 mortality.
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Considering the increased patient burden and disruptions in the healthcare system, orthopedic residents are affected both psychologically and physically while working both in the management of trauma patients and caring for COVID-19 patients. Our objective was to investigate the virus exposure and health status of orthopedic residents during the pandemic and to review the measures that can be taken. A survey consisting of 15 questions was organized and circulated through e-mail groups and social media platforms in order to evaluate the health status of residents. Demographic data, co-morbidities, whether they had a COVID-19 infection, time they work in orthopedics-related and COVID-related units, whether there was infection among their colleagues or family members and the infected patient care were questioned. A total of 136 residents completed the survey, of which 41 (30.1%) were infected. A significant difference was found between COVID-infected and COVID-free residents in terms of comorbidities (p=0.026). Residents with infection among their colleagues had lower infection rates (p<0.001). No significant difference was found between groups in terms of the working periods in orthopedics-related or COVID-related units (p>0.05 for each). With this study, the infection rate among orthopedic residents were reported for the first time in the literature, considering the fact that a third of all the participants in our study were infected. The infection rate among the orthopaedics residents were reported for the first time in the literature with this study. The fact that residents with infected colleagues have lower infection rates, demonstrates the importance of self-awareness and protective behaviors. During the pandemic, all healthcare professionals should pay maximum attention for simple measures, like practicing protective behaviors and use of personal protective equipment. Copyright © 2022 Ondokuz Mayis Universitesi. All rights reserved.
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Purpose: New viruses have emerged, causing global damage and mass deaths that can spread to international borders, the latest of which is the new coronavirus (COVID-19). After the Second International Congress on Artificial Intelligence in Health, themed "Artificial Intelligence in Health During COVID-19 Pandemic Process" organized online by Izmir Bakircay University and Izmir Provincial Health Directorate with the contributions of the International Association of Artificial Intelligence in Health, a questionnaire was conducted to evaluate the knowledge of the participants about artificial intelligence applications.Material and Methods: This study aimed to evaluate the interest of the congress participants in this field with the questions which form the questionnaire such as the duration of the interest of the participants in the field of artificial intelligence in health, their publication status, the development of studies on artificial intelligence with the COVID-19 pandemic, demographic structures such as age and gender, and educational level. 130 participants answered the questionnaire consisting of 23 questions. Questionnaire responses were analyzed in a statistical setting.Results: We found that 130 people filled out the questionnaire and the majority of the participants were female, with participation from many organizations, but university staff showed more interest. We have seen that the 30-39 age group is more interested in artificial intelligence than the other age groups, but the majority of the participants do not have academic studies in this field. We found that the technical terms related to artificial intelligence were not well known by the participants, and that the number of participants who tended to this field, especially in the recent year, was high. Another important point was that people working in this field stated that they would definitely follow up if scientific activities continued.Conclusion: We know how important congresses, symposiums, courses and other meetings are, especially for scientist candidates, which will be held to raise awareness about the usage areas of artificial intelligence-based health technologies, to develop new communication and work networks by bringing together different disciplines, to create an agenda and to lay the groundwork for new studies, and we think that there is a need for many repetitive activities in this field and that these activities should be continued.
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Machine learning (ML) and artificial intelligence (AI) approaches are prominent and well established in the field of health-care informatics. Because they have a more productive ability to predict, they are successfully applied in several health-care applications. ML approaches are needed thanks to the unsatisfactory experience of the novel virus, considerable ambiguity, complicated social circumstances, and inadequate accessible data. Several approaches have been applied as a tool to combat and protect against the new diseases. The COVID-19 outbreak has rapid growth, so it is not easy to predict the patients and resources within a specified time. ML is a strong approach in the fighting against the pandemic such as COVID-19. It is found significant to predict the susceptible, infected, recovered, or exposed persons and can assist the control strategies to block the spread of infections. This study critically examines the appropriateness and contribution of AI/ML methods on COVID-19 datasets, enhancing the understanding to apply these methods for quick analysis and verification of pandemic databases. © 2022 Elsevier Inc. All rights reserved.
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With the triggering effect of Covid-19 pandemic, the role of digitalization has become a strategic target and expedited the digital transformation process. World’s direction to the digital future has therefore shaped the use of new-age technologies, such as internet of things, artificial intelligence (AI), machine learning and blockchain. In response to this evolvement of new-age technologies, a noticeable shift from data-driven analysis to technology-oriented applications has occurred, particularly addressing the significance of analytics and AI. These rapid advancements of AI applications influence the use of customer analytics whilst enhancing the importance both for the general understanding and individual behavior of customers, within the scope of customer analytics. Considering the embeddedness of these technologies on practical applications, this study acknowledges the high-impact role and power of AI. In this regard, the study concentrates AI applications from the perspectives of customer analytics. Furthermore, the task of AI, the level of intelligence of AI applications and how the information from customer analytics is obtained and exploited by these applications are discussed. Focusing on the practical case applications, the study suggests a taxonomical structure of AI and customer analytics. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
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Redefinition of our social life for COVID-19, with social distance, prohibition of entering and exiting cities, closure of social areas and curfews effects every aspect of our lives, from psychological to physical. The aim of this study was to evaluate the injury mechanisms, fracture frequencies and priority treatment preferences in a tertiary trauma center from Turkey during the COVID-19 pandemic, both in adult and pediatric populations, and to compare them with pre-COVID-19 period. In this single-centered study, 960 patients (with 1039 fractures) who were admitted to a tertiary trauma center in Turkey, between April 2020 and December 2020 were examined. A control group of 964 patients (with 1070 fractures) who were admitted in the same date range of 2019 was formed. Patient demographics, injury mechanisms, fracture type and preferred treatment methods were recorded. There was a significant difference regarding injury mechanism between groups in both adult and pediatric populations (p=0.002 and p<0.001, respectively). In adults, according to the residual values, there was significant difference between groups in terms of proximal humerus, elbow, forearm, hand, femoral shaft and knee fractures (p<0.001). Among pediatric patients, a significant difference was found between groups in terms of proximal humerus, hand and knee fractures (p<0.001). In the pediatric population, there has been a significant increase in the preference of conservative treatment in the pandemic group (p=0.002). With increased indoors time, restriction of outdoors physical activities and lesser time in traffic, fracture distribution and priority treatment preferences have inevitably changed. In Turkey, the frequency of almost all upper extremity fractures except proximal humerus and humerus shaft fractures have increased significantly in the adult population, while the frequency of lower extremity fractures associated with high-energy injuries have decreased. In the pediatric population, treatment preferences are shifting towards conservative methods rather than surgery during the COVID-19 pandemic
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The real-Time polymerase chain reaction (RT-PCR) analysis using nasal swab samples is the gold standard approach for COVID-19 diagnosis. However, due to the high false-negative rate at lower viral loads and complex test procedure, PCR is not suitable for fast mass screening. Therefore, the need for a highly sensitive and rapid detection system based on easily collected fluids such as saliva during the pandemic has emerged. In this study, we present a surface-enhanced Raman spectroscopy (SERS) metasurface optimized with genetic algorithm (GA) to detect SARS-CoV-2 directly using unprocessed saliva samples. During the GA optimization, the electromagnetic field profiles were used to calculate the field enhancement of each structure and the fitness values to determine the performance of the generated substrates. The obtained design was fabricated using electron beam lithography, and the simulation results were compared with the test results using methylene blue fluorescence dye. After the performance of the system was validated, the SERS substrate was tested with inactivated SARS-CoV-2 virus for virus detection, viral load analysis, cross-reactivity, and variant detection using machine learning models. After the inactivated virus tests are completed, with 36 PCR positive and 33 negative clinical samples, we were able to detect the SARS-CoV-2 positive samples from Raman spectra with 95.2% sensitivity and specificity. © COPYRIGHT SPIE. Downloading of the is permitted for personal use only.
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Coronavirus pandemic (COVID-19) is an infectious illness. A newly explored coronavirus caused it. Currently, more than 112 million verified cases of COVID-19, containing 2,4 million deaths, are reported to WHO (February 2021). Scientists are working to develop treatments. Early detection and treatment of COVID-19 are critical to fighting disease. Recently, automated systems, specifically deep learning-based models, address the COVID-19 diagnosis task. There are various ways to test COVID-19. Imaging technologies are widely available, and chest X-ray and computed tomography images are helpful. A publicly available dataset was used in this study, including chest X-ray images of normal, COVID-19, and viral pneumonia. Firstly, images were pre-processed. Three deep learning models, namely DarkNet-53, ResNet-18, and Xception, were used in feature extraction from images. The number of extracted features was decreased by Binary Particle Swarm Optimization. Lastly, features were classified using Logistic Regression, Support Vector Machine, and XGBoost. The maximum accuracy score is 99.7% in a multi-classification task. This study reveals that pre-trained deep learning models with a metaheuristic-based feature selection give robust results. The proposed model aims to help healthcare professionals in COVID-19 diagnosis. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
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Objective: To compare the effectiveness of different mask types in limiting the dispersal of coughed air. Method: The Schlieren method with a single curved mirror was used in this study. Coughed air has a slightly higher temperature than ambient air, which generates a refractive index gradient. A curved mirror with a radius of curvature of 10 m and a diameter of 60 cm was used. The spread of the cough wavefront was investigated among five subjects wearing: (1) no mask;(2) a single surgical mask;(3) a double surgical mask;(4) a cloth mask;(5) a valveless N95 mask;and (6) a valved N95 mask.
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Background: Iron Deficiency Anemia (IDA) is a common health problem in daily clinical practice. A better understanding of iron metabolism in recent years;has created the need for revising treatment regimens in the treatment of IDA. Aims: Based on these data, we aimed to evaluate the effectiveness of oral ferrous sulfate treatment at different doses, and posologies in premenopausal women diagnosed with IDA, and its relationship with hepcidin, treatment compliance, and gastrointestinal side effects in this study. Methods: This is a prospective observational study including premenopausal female patients median age 36 (18-50) diagnosed with IDA. This study was approved by local ethic committe and sponsored by Ankara University School of Medicine Scientific Research Projects(BAP proje number 20L0230012) All patients recieved po ferrous sulfate (FS) 80 mg elemental iron) for three months. Patients were classified into three group according to FS dose: Group 1: 2x1/day;Group 2: 1x1/ day;Group 3: 1x1every other day. At the time of enrollment, thereafter at the second week of FS and at the third month: Hb level, transferrin saturation(%TS), total iron binding capacity were recorded. Blood was drawn and stored at -20 temperature for hepcidin analysis from patiens who signed informed consent. Results: We intent to analyse 50 patients for each group, however as a consequence of COVID-19 pandemic, neither patients could come to hospitals nor we could visit them. The patients in each group, ages and results are shown Table At the end of the second week a significant increase in Hb level was observed (p <0.01) and the mean Hb level increased by 1.38 ± 1.04 and 1.03 ± 0.48 in the first and second groups respectively and ≥ 1 g / dl while in the group given every other day it remained <1 g / dl with an increase of 0.69 ± 0.36. (p = 0.020, p = 0.019, respectively). Comparing the ferritin levels within the three group, it was seen that the ferritin increased significantly in the first group compared to the second and third group (p <0.05). There was no difference between group 2 and group 3 in terms of increase in ferritin level (p> 0.05). The increase in TS% and decrease in total iron binding capacity were similar in all three group at the end of the treatment (p> 0.05). The change between the second week and the initial hepcidin was mostly observed in the second group (p= 0.024) and the change between the three group were similar (p = 0.708). Gastrointestinal side effects were observed more in the first group who received 2∗1 daily than in the second and third group (p <0.05). While a similar increase in appetite and weight was observed in the patients in group 1 and 2 at the end of the treatment (p> 0.05) and there was no increase in appetite and weight in group 3. Summary/Conclusion: Hb level increase at the end of second week was ≥1 g / dl in the first and second group and <1 g / dl in the third treatment group. However, at the end of the treatment third month anemia was significantly improved in all three group and the increase in Hb level was similar. We consider, 1∗1 daily or 1∗1 every other day posology would be more appropriate instead of 2∗1 daily due to the significant gastrointestinal side effects in the first group. In addition, we believe in that, the evaluation of Hb response in the second week is too early to decide treatment outcome due to the slower Hb increase in the other day group and serial Hepcidin measurements will give a better idea to better understand the kinetics.