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Article Citation - WoS: 21Citation - Scopus: 36Deep Learning-Based Computer-Aided Diagnosis (cad): Applications for Medical Image Datasets(Mdpi, 2022) Kadhim, Yezi Ali; Khan, Muhammad Umer; Mishra, AlokComputer-aided diagnosis (CAD) has proved to be an effective and accurate method for diagnostic prediction over the years. This article focuses on the development of an automated CAD system with the intent to perform diagnosis as accurately as possible. Deep learning methods have been able to produce impressive results on medical image datasets. This study employs deep learning methods in conjunction with meta-heuristic algorithms and supervised machine-learning algorithms to perform an accurate diagnosis. Pre-trained convolutional neural networks (CNNs) or auto-encoder are used for feature extraction, whereas feature selection is performed using an ant colony optimization (ACO) algorithm. Ant colony optimization helps to search for the best optimal features while reducing the amount of data. Lastly, diagnosis prediction (classification) is achieved using learnable classifiers. The novel framework for the extraction and selection of features is based on deep learning, auto-encoder, and ACO. The performance of the proposed approach is evaluated using two medical image datasets: chest X-ray (CXR) and magnetic resonance imaging (MRI) for the prediction of the existence of COVID-19 and brain tumors. Accuracy is used as the main measure to compare the performance of the proposed approach with existing state-of-the-art methods. The proposed system achieves an average accuracy of 99.61% and 99.18%, outperforming all other methods in diagnosing the presence of COVID-19 and brain tumors, respectively. Based on the achieved results, it can be claimed that physicians or radiologists can confidently utilize the proposed approach for diagnosing COVID-19 patients and patients with specific brain tumors.Review Citation - WoS: 7Citation - Scopus: 9A Survey of Covid-19 Diagnosis Using Routine Blood Tests With the Aid of Artificial Intelligence Techniques(Mdpi, 2023) Habashi, Soheila Abbasi; Koyuncu, Murat; Alizadehsani, RoohallahSevere Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), causing a disease called COVID-19, is a class of acute respiratory syndrome that has considerably affected the global economy and healthcare system. This virus is diagnosed using a traditional technique known as the Reverse Transcription Polymerase Chain Reaction (RT-PCR) test. However, RT-PCR customarily outputs a lot of false-negative and incorrect results. Current works indicate that COVID-19 can also be diagnosed using imaging resolutions, including CT scans, X-rays, and blood tests. Nevertheless, X-rays and CT scans cannot always be used for patient screening because of high costs, radiation doses, and an insufficient number of devices. Therefore, there is a requirement for a less expensive and faster diagnostic model to recognize the positive and negative cases of COVID-19. Blood tests are easily performed and cost less than RT-PCR and imaging tests. Since biochemical parameters in routine blood tests vary during the COVID-19 infection, they may supply physicians with exact information about the diagnosis of COVID-19. This study reviewed some newly emerging artificial intelligence (AI)-based methods to diagnose COVID-19 using routine blood tests. We gathered information about research resources and inspected 92 articles that were carefully chosen from a variety of publishers, such as IEEE, Springer, Elsevier, and MDPI. Then, these 92 studies are classified into two tables which contain articles that use machine Learning and deep Learning models to diagnose COVID-19 while using routine blood test datasets. In these studies, for diagnosing COVID-19, Random Forest and logistic regression are the most widely used machine learning methods and the most widely used performance metrics are accuracy, sensitivity, specificity, and AUC. Finally, we conclude by discussing and analyzing these studies which use machine learning and deep learning models and routine blood test datasets for COVID-19 detection. This survey can be the starting point for a novice-/beginner-level researcher to perform on COVID-19 classification.Article Citation - WoS: 3Citation - Scopus: 3The Role of Honor Concerns in Disclosing (vs. Hiding) COVID-19 Diagnosis: Insights from Turkiye(Springer/plenum Publishers, 2023) Ceylan-Batur, Suzan; Dogulu, Canay; Akbas, Gulcin; Yet, Barbaros; Uskul, Ayse K. K.Members of honor cultures value engaging in moral behaviors and managing their social image to maintain their honor. These two goals reflect reputation and integrity concerns, which also have bearing on gender roles. In the current study, we examined a) evaluations of women and men described as diagnosed with COVID-19 and as either hiding or disclosing their diagnosis, b) the moderating role of honor concerns (reputation and integrity) and the gender of the infected person in these evaluations, and c) the relationship between honor concerns and individuals' own disclosure preferences among participants living in Turkiye, a country that exemplifies an honor culture. Findings revealed that participants with stronger reputation concerns evaluated a woman's hiding behavior more favorably than that of a man's. Moreover, higher integrity concerns were associated with lower levels of participants' own preference to hide a diagnosis for both men and women, whereas reputation concerns were positively associated with a preference for hiding a diagnosis among men only. Furthermore, a content analysis of participants' open-ended explanations of their views on women's and men's motivation to hide a diagnosis revealed further evidence for the gendered nature of reputation concerns. Our findings point to the importance of prioritizing integrity concerns (and downplaying reputation concerns) in public health campaigns in honor cultures.Article Citation - WoS: 5Citation - Scopus: 8A Novel Hybrid Machine Learning-Based System Using Deep Learning Techniques and Meta-Heuristic Algorithms for Various Medical Datatypes Classification(Mdpi, 2024) Kadhim, Yezi Ali; Guzel, Mehmet Serdar; Mishra, AlokMedicine is one of the fields where the advancement of computer science is making significant progress. Some diseases require an immediate diagnosis in order to improve patient outcomes. The usage of computers in medicine improves precision and accelerates data processing and diagnosis. In order to categorize biological images, hybrid machine learning, a combination of various deep learning approaches, was utilized, and a meta-heuristic algorithm was provided in this research. In addition, two different medical datasets were introduced, one covering the magnetic resonance imaging (MRI) of brain tumors and the other dealing with chest X-rays (CXRs) of COVID-19. These datasets were introduced to the combination network that contained deep learning techniques, which were based on a convolutional neural network (CNN) or autoencoder, to extract features and combine them with the next step of the meta-heuristic algorithm in order to select optimal features using the particle swarm optimization (PSO) algorithm. This combination sought to reduce the dimensionality of the datasets while maintaining the original performance of the data. This is considered an innovative method and ensures highly accurate classification results across various medical datasets. Several classifiers were employed to predict the diseases. The COVID-19 dataset found that the highest accuracy was 99.76% using the combination of CNN-PSO-SVM. In comparison, the brain tumor dataset obtained 99.51% accuracy, the highest accuracy derived using the combination method of autoencoder-PSO-KNN.Article Investigation of Sars-Cov Antibody Levels After Covid-19 Vaccine in Chronic Hepatitis B Patients(Aepress Sro, 2024) Kinikli, Sami; Afsar, Fatma Elcin; Dursun, Ali Dogan; Aksoy, Altan; Karahan, Gizem; Cesur, Salih; Urtimur, UfukAIM: The aim was to compare SARS-CoV-2 IgG antibody levels in chronic hepatitis B patients and healthcare personnel selected as the control group and to determine factors such as age, gender, vaccine type, and number of vaccines that may affect the antibody levels. MATERIALS AND METHODS: 87 chronic hepatitis B (CHB) patients followed in Ankara Training and Research Hospital Infectious Diseases Clinic and Mamak State Hospital Infectious Diseases outpatient clinic and 89 healthcare personnel selected as the control group were included in the study. SARS-CoV-2 IgG antibody levels in the serum samples of patients and healthcare personnel who received the COVID-19 vaccine were studied with the ELISA method in the Microbiology Laboratory of Ankara Training and Research Hospital, using a commercial ELISA kit (Abbott, USA) in line with the recommendations of the manufacturer. In the study, SARS-CoV-2 IgG levels were compared in CHB patients and healthcare personnel. In addition, the relationship between SARS-CoV-2 antibody level, gender, average age, natural history of the disease, number of vaccinations, vaccine type (Coronavac TM vaccine alone, BNT162b2 vaccine alone or Coronavac TM and BNT162b2 vaccine (heterologous vaccination)), treatment duration of CHB was investigated. Statistical analyses were made in the SPSS program. A value of p <= 0.05 was considered statistically significant. FINDINGS: A total of 167 people, including 87 CKD patients and 80 healthcare personnel as the control group, were included in the study. SARS-CoV-2 IgG antibody levels were detected above the cut-off level in the entire study group, regardless of the vaccine type. No difference was detected in SARS-CoV-2 IgG titers after COVID-19 vaccination between CHB patients and healthcare personnel. There was a statistically significant difference in SARS-CoV-2 IgG antibody levels among individuals participating in the study according to vaccine types. Compared to those who received Coronavac TM vaccine alone, the average SARS-CoV-2 IgG level was found to be statistically significantly higher in those who received BNT162b2 vaccine alone or heterologous vaccination with Coronavac TM + BNT162b2 vaccine. There was no difference between the groups in terms of age, gender, number of vaccinations, natural transmission of the disease, and duration of antiviral therapy in the CHD patient group. CONCLUSION: As a result, SARS-CoV-2 IgG antibody levels above the cut-off value were achieved with Coronavac TM and BNT162b2 vaccines in both CHD patients and healthy control groups. however, both CHD patients and healthcare personnel had higher antibody levels than those who received BNT162b2 alone or those who received heterologous vaccination had higher antibody levels than those with Coronavac TM alone. Therefore, if there are no contraindications, BNT162b2 vaccine may be preferred in CHB and health personnel (Tab. 2, Ref. 14) .Article Health Capital and a Sustainable Economic-Growth Nexus: a High-Frequency Analysis During Covid-19(Mdpi, 2024) Sungur, Nazli Ceylan; Akdogan, Ece C.; Gokten, SonerThe recent COVID-19 pandemic effectively concretized the vitality of health expenditure and the economic-growth nexus, and the threat of new pandemics make re-examining this relationship a necessity. Consequently, this paper focuses on this nexus for developed OECD countries, paying particular attention to the effects of the COVID-19 pandemic. The use of stock indices as proxy variables for health expenditure and economic growth enabled the examination of this nexus by using high-frequency data and financial econometric techniques, specifically via rolling correlation and bivariate GARCH analyses. The data span 1170 observations between 15 May 2018 and 11 November 2022. Since the research period overlaps with the outbreak of Ukraine-Russia war, additional insights are obtained regarding the effects of the war as well. It was found that an increase in health expenditure leads to a delayed increase in economic growth even in the short term, and this relationship mainly develops during crises such as epidemics, wars, supply chain breakdowns, etc., for developed OECD countries. Given the aging population of developed countries, which will probably deteriorate the health status of those countries in the near future, the increasing political tensions around the globe and the considerations of a global recession highlight the importance and the inevitability of investments in health capital for developed countries as well.Article Breast Cancer Management During the Covid Pandemic(Coll Physicians & Surgeons Pakistan, 2024) Sariyildiz, Gulcin Turkmen; Ayhan, Fikriye FigenObjective: To explore the impact of COVID-19 among both the newly diagnosed patients and patients under follow-up for breast cancer by focusing on patients' accessibility to management and comparing the distribution of them before and during pandemic. Study Design: Single -centric retrospective study. Place and Duration of the Study: Department of General Surgery and Department of Physical Medicine and Rehabilitation, Atilim University, Medicana International Ankara Hospital, Ankara, Turkiye, from March 2018 to 2022. Methodology: The data were collected to analyse numbers and distributions of physician visits regarding breast cancer. Results: The mean age of patients was 55.98 +/- 12.60 years. The percentages of newly diagnosed cases showed similarity (7.37% vs. 9.79%) before and during the pandemic (p = 0.18). The number of imaging studies decreased by 53.33% in patients under follow-up (p = 0.006), despite screening tests showed a similar trend (p = 0.145). General surgery visits marked up (+44.6%), in contrast to plastic surgery visits (-42.04%, p <0.001). Patients' admissions decreased in many COVID-19 related clinics (pulmonology, emergency, internal medicine, and intensive care), but cardiology (+96.59%) and rehabilitation (+75%) admissions increased during the pandemic (p <0.001). The number of medical oncology and radiation oncology visits did not change (p >0.05). Conclusion: Total number of physician visits was similar before and during the pandemic despite the changing distribution. While COVID-19 led to markedly rising trends of surgical, cardiological, and rehabilitative management in patients with breast cancer, falling trends were seen in other specialities except oncology which showed a plateau during two years. The falling trends of visits to pulmonology, emergency, internal medicine, and intensive care clinics may be explained by crowded COVID-19 cases.Article Citation - WoS: 11Citation - Scopus: 13What pandemic inflation tells: Old habits die hard(Elsevier Science Sa, 2021) Kantur, Zeynep; Ozcan, GulserimCOVID-19 has led to changes in individuals' consumption habits, which will cause the calculation of inflation based on the average consumption basket to give distorted information. Using debit and credit card spending data of Turkey, we build CPI weights and compute an alternative pandemic consumption basket price index for Jan 2020-Feb 2021. Our findings show that the pandemic inflation is higher than the official inflation rate during the first lockdown, suggesting a behavioral change in consumption. However, in the reopening period, old habits come back. During the second lockdown, the difference between the pandemic and the official inflation rates is trivial in comparison with the first lockdown. (C) 2021 Elsevier B.V. All rights reserved.Article Citation - WoS: 31Citation - Scopus: 36Predicting the Outcome of Covid-19 Infection in Kidney Transplant Recipients(Bmc, 2021) Oto, Ozgur Akin; Ozturk, Savas; Turgutalp, Kenan; Arici, Mustafa; Alpay, Nadir; Merhametsiz, Ozgur; Yildiz, AlaattinBackgroundWe aimed to present the demographic characteristics, clinical presentation, and outcomes of our multicenter cohort of adult KTx recipients with COVID-19.MethodsWe conducted a multicenter, retrospective study using data of patients hospitalized for COVID-19 collected from 34 centers in Turkey. Demographic characteristics, clinical findings, laboratory parameters (hemogram, CRP, AST, ALT, LDH, and ferritin) at admission and follow-up, and treatment strategies were reviewed. Predictors of poor clinical outcomes were analyzed. The primary outcomes were in-hospital mortality and the need for ICU admission. The secondary outcome was composite in-hospital mortality and/or ICU admission.ResultsOne hundred nine patients (male/female: 63/46, mean age: 48.412.4years) were included in the study. Acute kidney injury (AKI) developed in 46 (42.2%) patients, and 4 (3.7%) of the patients required renal replacement therapy (RRT). A total of 22 (20.2%) patients were admitted in the ICU, and 19 (17.4%) patients required invasive mechanical ventilation. 14 (12.8%) of the patients died. Patients who were admitted in the ICU were significantly older (age over 60years) (38.1% vs 14.9%, p=0.016). 23 (21.1%) patients reached to composite outcome and these patients were significantly older (age over 60years) (39.1% vs. 13.9%; p=0.004), and had lower serum albumin (3.4g/dl [2.9-3.8] vs. 3.8g/dl [3.5-4.1], p=0.002), higher serum ferritin (679 mu g/L [184-2260] vs. 331 mu g/L [128-839], p=0.048), and lower lymphocyte counts (700/mu l [460-950] vs. 860 /mu l [545-1385], p=0.018). Multivariable analysis identified presence of ischemic heart disease and initial serum creatinine levels as independent risk factors for mortality, whereas age over 60years and initial serum creatinine levels were independently associated with ICU admission. On analysis for predicting secondary outcome, age above 60 and initial lymphocyte count were found to be independent variables in multivariable analysis.Conclusion Over the age of 60, ischemic heart disease, lymphopenia, poor graft function were independent risk factors for severe COVID-19 in this patient group. Whereas presence of ischemic heart disease and poor graft function were independently associated with mortality.Article Citation - WoS: 26Citation - Scopus: 32In Covid-19 Health Messaging, Loss Framing Increases Anxiety With Little-To Concomitant Benefits: Experimental Evidence From 84 Countries(Springernature, 2022) Dorison, Charles A.; Lerner, Jennifer S.; Heller, Blake H.; Rothman, Alexander J.; Kawachi, Ichiro I.; Wang, Ke; Coles, Nicholas A.The COVID-19 pandemic (and its aftermath) highlights a critical need to communicate health information effectively to the global public. Given that subtle differences in information framing can have meaningful effects on behavior, behavioral science research highlights a pressing question: Is it more effective to frame COVID-19 health messages in terms of potential losses (e.g., "If you do not practice these steps, you can endanger yourself and others") or potential gains (e.g., "If you practice these steps, you can protect yourself and others")? Collecting data in 48 languages from 15,929 participants in 84 countries, we experimentally tested the effects of message framing on COVID-19-related judgments, intentions, and feelings. Loss- (vs. gain-) framed messages increased self-reported anxiety among participants cross-nationally with little-to-no impact on policy attitudes, behavioral intentions, or information seeking relevant to pandemic risks. These results were consistent across 84 countries, three variations of the message framing wording, and 560 data processing and analytic choices. Thus, results provide an empirical answer to a global communication question and highlight the emotional toll of loss-framed messages. Critically, this work demonstrates the importance of considering unintended affective consequences when evaluating nudge-style interventions.
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