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  • Article
    Citation - WoS: 5
    Citation - Scopus: 9
    A 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, Alok
    Medicine 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 Figen
    Objective: 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: 173
    Citation - Scopus: 179
    Mortality 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, Kenan
    Background. 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 Doğan
    Giriş: 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.
  • Review
    Citation - WoS: 7
    Citation - Scopus: 9
    A 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, Soheila
    Severe 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: 30
    Citation - Scopus: 37
    In 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: 3
    Citation - Scopus: 3
    Shared 6mer Peptides of Human and Omicron (21k and 21l) at Sars-Cov Mutation Sites
    (Mdpi, 2022-10-25) Adiguzel, Yekbun; Shoenfeld, Yehuda
    We 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: 22
    Citation - Scopus: 38
    Deep Learning-Based Computer-Aided Diagnosis (cad): Applications for Medical Image Datasets
    (Mdpi, 2022-11-21) Kadhim, Yezi Ali; Khan, Muhammad Umer; Mishra, Alok
    Computer-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.
  • 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, Ufuk
    AIM: 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
    Citation - WoS: 7
    Citation - Scopus: 8
    Food Safety Awareness, Changes in Food Purchasing Behaviour and Attitudes Towards Food Waste During Covid-19 in Türkiye
    (Mdpi, 2023-12-06) Erol, Irfan; Mutus, Begum; Ayaz, Naim Deniz; Stowell, Julian D.; Siriken, Belgin
    (1) Background: The COVID-19 pandemic brought the key issues of food security, food safety, and food waste into sharp focus. Turkiye is in the enviable position of being among the top ten agricultural economies worldwide, with a wide diversity of food production. This survey was undertaken in order to gain insights into consumer behaviour and attitudes in Turkiye with respect to these issues. The objective was to highlight strengths and weaknesses, identify areas for improvement, and present strategies for the future. (2) Methods: This survey was carried out between April and May 2022 in 12 provinces throughout Turkiye. Face-to-face interviews were performed with 2400 participants representing a cross-section of ages, educational attainment, and socio-economic categories. The findings were evaluated statistically. (3) Results: The results provide an insight into attitudes and behaviours, both pre-COVID-19 and during the pandemic. In several ways, the pandemic enhanced knowledge and improved behaviour, leading to improvements in diet and reductions in food waste. However, worrying concerns about food safety persist. Specific attention has been given to understanding patterns of bread consumption, particularly in consideration of waste. (4) Conclusions: It is hoped that the results of this survey will increase dialogue between the components of the food sector, encourage education initiatives, and contribute to improving food safety and security and reducing food waste in Turkiye and beyond.