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Article Citation - WoS: 3Citation - Scopus: 3Online Learning Perceptions Amid Covid-19 Pandemic: the Engineering Undergraduates' Perspective(Tempus Publications, 2022) Eryilmaz, Meltem; Kalem, Guler; Kilic, Hurevren; Tirkes, Guzin; Topalli, Damla; Turhan, Cigdem; Yazici, Ali; Information Systems Engineering; Computer Engineering; Software EngineeringThe COVID-19 pandemic caused face-to-face education in just about all universities worldwide to shift to online education. For most students, this educational model was a compulsory first experience. In this study, the survey results are analyzed and discussed related to a group of students in the Engineering Faculty of a university in Turkey regarding their online education perceptions. Briefly summarized, the findings of the study indicate that: (a) most of the students still prefer face-to-face learning, which is also favored if accompanied by distance learning; (b) the concentration level of the students has dropped due to the concerns about the COVID-19 pandemic which affects their learning negatively; and (c) around half of the students participating in the study feel that the online exams conducted without a secure exam software, is considered unsafe. Additionally, the study's results were further extended to evaluate the questionnaire results and reported along with the suggestions of necessary actions in emergency online learning (EOL).Article Citation - WoS: 5Citation - Scopus: 9A Novel Hybrid Machine Learning-Based System Using Deep Learning Techniques and Meta-Heuristic Algorithms for Various Medical Datatypes Classification(Mdpi, 2024-07-09) 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 Breast Cancer Management During the Covid Pandemic(Coll Physicians & Surgeons Pakistan, 2024-06-01) 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: 173Citation - Scopus: 179Mortality Analysis of Covid-19 Infection in Chronic Kidney Disease, Haemodialysis and Renal Transplant Patients Compared With Patients Without Kidney Disease: a Nationwide Analysis From Turkey(Oxford Univ Press, 2020-12) Ozturk, Savas; Turgutalp, Kenan; Arici, Mustafa; Odabas, Ali Riza; Altiparmak, Mehmet Riza; Aydin, Zeki; Ates, KenanBackground. Chronic kidney disease (CKD) and immunosuppression, such as in renal transplantation (RT), stand as one of the established potential risk factors for severe coronavirus disease 2019 (COVID-19). Case morbidity and mortality rates for any type of infection have always been much higher in CKD, haemodialysis (HD) and RT patients than in the general population. A large study comparing COVID-19 outcome in moderate to advanced CKD (Stages 3-5), HD and RT patients with a control group of patients is still lacking. Methods. We conducted a multicentre, retrospective, observational study, involving hospitalized adult patients with COVID-19 from 47 centres in Turkey. Patients with CKD Stages 3-5, chronic HD and RT were compared with patients who had COVID-19 but no kidney disease. Demographics, comorbidities, medications, laboratory tests, COVID-19 treatments and outcome [in-hospital mortality and combined in-hospital outcome mortality or admission to the intensive care unit (ICU)] were compared. Results. A total of 1210 patients were included [median age, 61 (quartile 1-quartile 3 48-71) years, female 551 (45.5%)] composed of four groups: control (n = 450), HD (n = 390), RT (n = 81) and CKD (n = 289). The ICU admission rate was 266/1210 (22.0%). A total of 172/1210 (14.2%) patients died. The ICU admission and in-hospital mortality rates in the CKD group [114/289 (39.4%); 95% confidence interval (CI) 33.9-45.2; and 82/289 (28.4%); 95% CI 23.9-34.5)] were significantly higher than the other groups: HD = 99/390 (25.4%; 95% CI 21.3-29.9; P < 0.001) and 63/390 (16.2%; 95% CI 13.0-20.4; P < 0.001); RT = 17/81 (21.0%; 95% CI 13.2-30.8; P = 0.002) and 9/81 (11.1%; 95% CI 5.7-19.5; P = 0.001); and control = 36/450 (8.0%; 95% CI 5.8-10.8; P < 0.001) and 18/450 (4%; 95% CI 2.5-6.2; P < 0.001). Adjusted mortality and adjusted combined outcomes in CKD group and HD groups were significantly higher than the control group [hazard ratio (HR) (95% CI) CKD: 2.88 (1.52-5.44); P = 0.001; 2.44 (1.35-4.40); P = 0.003; HD: 2.32 (1.21-4.46); P = 0.011; 2.25 (1.23-4.12); P = 0.008), respectively], but these were not significantly different in the RT from in the control group [HR (95% CI) 1.89 (0.76-4.72); P = 0.169; 1.87 (0.81-4.28); P = 0.138, respectively]. Conclusions. Hospitalized COVID-19 patients with CKDs, including Stages 3-5 CKD, HD and RT, have significantly higher mortality than patients without kidney disease. Stages 3-5 CKD patients have an in-hospital mortality rate as much as HD patients, which may be in part because of similar age and comorbidity burden. We were unable to assess if RT patients were or were not at increased risk for in-hospital mortality because of the relatively small sample size of the RT patients in this study.Article Avrupa Birliği Ülkeleri ve Türkiye’nin 2010-2021 Dönemi Toplam Antibiyotik Tüketiminin Karşılaştırılması: Akılcı İlaç Kullanımı ve Pandeminin Etkileri(Bilimsel Tip Yayinevi, 2023-09-22) Kavruk, Murat; Uçak, Samet; Sapmaz, Burcu; Demir, Canan Çiçek; Dursun, Ali DoğanGiriş: Antibiyotik tüketimini düşürmek adına dünya genelinde pek çok uygulama yapılmaktadır fakat bu uygulamaların karşılaştırmalı analizi ve pandemi gibi geniş çaplı değişkenler karşısındaki durumu yeterince analiz edilmemektedir. Bu kapsamda; Türkiye ve Avrupa ülkelerinin ATC grubu J01 toplam antibiyotik tüketim eğilimleri ve ülkeler arasındaki farklılıklar incelenmiş olup son dönemde yaşanan pandeminin antibiyotik tüketim verilerindeki değişime etkisi sorgulanmıştır. Materyal ve Metod: Türkiye ve 19 Avrupa ülkesinin 2010-2021 yılları arasındaki ATC grubu J01 toplam antibiyotik tüketimi (hastane + toplum) verileri birleştirilerek karşılaştırıldı. Çalışma için Avrupa Hastalık Önleme ve Kontrol Merkezi (ECDC) ve Türkiye İlaç ve Tıbbi Cihaz Kurumu (TICKK) verileri kullanılmıştır. Antibiyotik tüketim verileri, günlük 1000 hasta başına tanımlanmış günlük doz (DDD) cinsinden temsil edildi. Bulgular: Türkiye, odaklanılan dönemde en yüksek antibiyotik tüketimine sahip olmasına rağmen 2010-2015 tarihleri arasında 41.43 günlük 1000 hasta başına tanımlanmış günlük doz (DDD) ve 2016-2021 tarihleri arasında 32.24 günlük 1000 hasta başına tanım- lanmış günlük doz (DDD) antibiyotik tüketim verisi ile istatistiksel olarak (p= 0.05) anlamlı bir düşüş gösterdi. COVID-19 pandemisinin etkili olduğu 2021 yılında Avrupa’da, çalışmaya konu olan 2010-2021 yılları arasındaki en düşük düzeyi olan 14.91 günlük 1000 hasta başına tanımlanmış günlük doz (DDD)’a gerilerken Türkiye’de 2020 yılındaki kaydedilen 24.39 günlük 1000 hasta başına tanımlanmış günlük doz (DDD) seviyesine düşen antibiyotik tüketimi, 2021 yılında 26.97 günlük 1000 hasta başına tanımlanmış günlük doz (DDD) seviyesine yükseldi. Sonuç: Akılcı ilaç kullanımı uygulamaları, Türkiye için antibiyotik tüketimini azaltmada etkili olmakla birlikte, 2021 tüketim verileri ile trendin bozulduğu gözlemlenmiştir. Avrupa ülkeleri antibiyotik tüketim miktarlarında farklılık gösterse de toplamda COVID-19 pandemisi ile azalan bir tüketim durumuna girdiği tespit edilmiştir.Article Citation - WoS: 19Citation - Scopus: 20Subacute Thyroiditis Related To Sars-Cov Vaccine and Covid-19 (thyrovac Study): a Multicenter Nationwide Study(Endocrine Soc, 2023-04-25) Batman, Adnan; Yazici, Dilek; Dikbas, Oguz; Agbaht, Kemal; Saygili, Emre Sedar; Demirci, Ibrahim; Sahin, MustafaContext The aims of the study are to compare characteristics of subacute thyroiditis (SAT) related to different etiologies, and to identify predictors of recurrence of SAT and incident hypothyroidism. Methods This nationwide, multicenter, retrospective cohort study included 53 endocrinology centers in Turkey. The study participants were divided into either COVID-19-related SAT (Cov-SAT), SARS-CoV-2 vaccine-related SAT (Vac-SAT), or control SAT (Cont-SAT) groups. Results Of the 811 patients, 258 (31.8%) were included in the Vac-SAT group, 98 (12.1%) in the Cov-SAT group, and 455 (56.1%) in the Cont-SAT group. No difference was found between the groups with regard to laboratory and imaging findings. SAT etiology was not an independent predictor of recurrence or hypothyroidism. In the entire cohort, steroid therapy requirement and younger age were statistically significant predictors for SAT recurrence. C-reactive protein measured during SAT onset, female sex, absence of antithyroid peroxidase (TPO) positivity, and absence of steroid therapy were statistically significant predictors of incident (early) hypothyroidism, irrespective of SAT etiology. On the other hand, probable predictors of established hypothyroidism differed from that of incident hypothyroidism. Conclusion Since there is no difference in terms of follow-up parameters and outcomes, COVID-19- and SARS-CoV-2 vaccine-related SAT can be treated and followed up like classic SATs. Recurrence was determined by younger age and steroid therapy requirement. Steroid therapy independently predicts incident hypothyroidism that may sometimes be transient in overall SAT and is also associated with a lower risk of established hypothyroidism.Review Citation - WoS: 7Citation - Scopus: 9A Survey of Covid-19 Diagnosis Using Routine Blood Tests With the Aid of Artificial Intelligence Techniques(Mdpi, 2023-05-16) Habashi, Soheila Abbasi; Koyuncu, Murat; Alizadehsani, Roohallah; Abbasi Habashi, SoheilaSevere 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: 30Citation - Scopus: 37In Covid-19 Health Messaging, Loss Framing Increases Anxiety With Little-To Concomitant Benefits: Experimental Evidence From 84 Countries(Springernature, 2022-09) 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.Article Citation - WoS: 3Citation - Scopus: 3Shared 6mer Peptides of Human and Omicron (21k and 21l) at Sars-Cov Mutation Sites(Mdpi, 2022-10-25) Adiguzel, Yekbun; Shoenfeld, YehudaWe investigated the short sequences involving Omicron 21K and Omicron 21L variants to reveal any possible molecular mimicry-associated autoimmunity risks and changes in those. We first identified common 6mers of the viral and human protein sequences present for both the mutant (Omicron) and nonmutant (SARS-CoV-2) versions of the same viral sequence and then predicted the binding affinities of those sequences to the HLA supertype representatives. We evaluated change in the potential autoimmunity risk, through comparative assessment of the nonmutant and mutant viral sequences and their similar human peptides with common 6mers and affinities to the same HLA allele. This change is the lost and the new, or de novo, autoimmunity risk, associated with the mutations in the Omicron 21K and Omicron 21L variants. Accordingly, e.g., the affinity of virus-similar sequences of the Ig heavy chain junction regions shifted from the HLA-B*15:01 to the HLA-A*01:01 allele at the mutant sequences. Additionally, peptides of different human proteins sharing 6mers with SARS-CoV-2 proteins at the mutation sites of interest and with affinities to the HLA-B*07:02 allele, such as the respective SARS-CoV-2 sequences, were lost. Among all, any possible molecular mimicry-associated novel risk appeared to be prominent in HLA-A*24:02 and HLA-B*27:05 serotypes upon infection with Omicron 21L. Associated disease, pathway, and tissue expression data supported possible new risks for the HLA-B*27:05 and HLA-A*01:01 serotypes, while the risks for the HLA-B*07:02 serotypes could have been lost or diminished, and those for the HLA-A*03:01 serotypes could have been retained, for the individuals infected with Omicron variants under study. These are likely to affect the complications related to cross-reactions influencing the relevant HLA serotypes upon infection with Omicron 21K and Omicron 21L.Article Citation - WoS: 22Citation - Scopus: 38Deep Learning-Based Computer-Aided Diagnosis (cad): Applications for Medical Image Datasets(Mdpi, 2022-11-21) 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.