Recep Tayyip Erdoğan Üniversitesi Kurumsal Akademik Arşivi
DSpace@RTEÜ, Recep Tayyip Erdoğan Üniversitesi tarafından doğrudan ve dolaylı olarak yayınlanan; kitap, makale, tez, bildiri, rapor, araştırma verisi gibi tüm akademik kaynakları uluslararası standartlarda dijital ortamda depolar, Üniversitenin akademik performansını izlemeye aracılık eder, kaynakları uzun süreli saklar ve yayınların etkisini artırmak için telif haklarına uygun olarak Açık Erişime sunar.
Güncel Gönderiler
Analysis of inhaler treatment in patients with asthma and chronic obstructive pulmonary disease (COPD): The INTEDA-2 study
(Kare Publishing, 2025) Suerdem, Mecit; Ceylan, Emel; Coşkun, Funda; Dalar, Levent; Demir, Melike; Özyurt, Songül; Yormaz, Burcu
BACKGROUND AND AIM: Noncompliance with inhaler treatment among patients with asthma or chronic obstructive pulmonary disease (COPD) remains a significant issue. This study aims to identify issues related to inhaler use and to propose potential solutions. METHODS: Patients aged 18 and older who had been receiving inhaler therapy for at least one year for asthma or COPD were included. The study was conducted across nine centers located in different geographical regions of Türkiye. Data were collected through face-to-face interviews, during which patients were asked about their demographic characteristics and history of inhalation therapy. RESULTS: A total of 256 patients, 179 (69.92%) male and 77 female (30.08%), from 44 different cities were included in the study. Among the participants, 54.40% were former smokers, and 17.92% were current smokers. Annual physician visit rates were three or more times in 55.73% of patients, twice in 24.77%, and once in 13.62%; additionally, 5.88% reported not attending any check-up visits. The hospitalization rate in the past year was 28.53%. Among those hospitalized, 46.07% were admitted once, 30.34% twice, and 23.59% three times or more. The mean number of hospitalizations during the previous year was 2.27. Participants reported that education on inhaler device use had been provided by physicians (83.50%), pharmacists (10.36%), and nurses (2.27%). Regarding the method of education, 77.52% received only verbal instructions, 20.13% received verbal instructions along with their device usage, and 2.35% were trained with a brochure. Among the patients, 45.43% stated that device usage was not assessed during outpatient visits, while 40.20% reported at least one change in their inhaler device. CONCLUSIONS: Although most patients stated that their initial education on device use was adequate, significant issues with noncompliance remain. These issues include providing only verbal inhaler device training and failing to assess patients’ inhaler technique during outpatient visits. Training respiratory nurses specifically for inhaler device education, as well as certifying family medicine specialists and nurses working in family health centers in this area, may help minimize these problems.
Primary vs. Rescue medium vessel occlusions: comparative clinical outcomes in patients with acute ischemic stroke
(Multidisciplinary Digital Publishing Institute (MDPI), 2025) Özdemir, Gökhan; Eren, Alper; Kızıldağ, Nazım; Gündoğdu, Ömer Lütfi; Ersoy, Ayşe Nur; Körez, Muslu Kazım; Utku, Uygar
Background: Medium vessel occlusions (MeVOs) are an increasingly recognized but heterogeneous target for endovascular therapy (EVT). This study aims to compare primary MeVO, rescue MeVO, and large vessel occlusion (LVO) thrombectomy cases to identify which MeVO subtypes derive a meaningful benefit from EVT under appropriate safety conditions. Methods: We retrospectively analyzed a multicenter registry of patients undergoing EVT for acute ischemic stroke. MeVO was defined as the occlusion of the A1-A3, M2-M3, P1-P3, fetal PCA, or PICA segments and classified as primary or rescue. Clinical outcomes were assessed by NIHSS score at baseline, discharge, and 90 days; functional outcome by the modified Rankin scale (mRS); and reperfusion by modified thrombolysis in cerebral infarction (mTICI). Safety endpoints included intracranial hemorrhage and mortality. Results: Among 603 EVT patients, 202 (33.5%) had MeVO. Compared to LVO, MeVO patients were older and had more prior strokes but achieved similar reperfusion and safety outcomes. At 90 days, mRS distribution differed, with MeVO showing more mRS 2 and LVO more mRS 1, while higher-disability strata were comparable. Within MeVO, 119 (58.9%) were primary and 83 (41.1%) rescue occlusions. Rescue MeVO patients presented with higher baseline severity (NIHSS score of 19 vs. 18) and, despite similar reperfusion, experienced worse 90-day outcomes and higher mortality (21.7% vs. 0.8%). Conclusions: EVT for primary MeVO is feasible, effective, and safe, whereas rescue MeVO is associated with poor functional outcomes and markedly higher mortality. These findings highlight rescue MeVO as a distinct phenotype and support a selective approach prioritizing disabling syndromes, proximal/dominant branch occlusions, and IVT non-response.
Mobile phone-based plasmodium parasites stage detection from Giemsa stained blood smear by convolutional neural networks
(Springer, 2025) Bedir, Hilal; Arslan, Mükremin Özkan; Akıner, Muhammet Mustafa; Öztürk, Murat; Uygun, Zihni Onur
Plasmodium vivax is a malaria parasite with a broad geographic distribution worldwide. The unique biological characteristics of P. vivax, such as early gametocytogenesis and its latent hypnozoite stage, make it more difficult to control compared to P. falciparum. Malaria remains a significant global health concern, particularly in regions with limited diagnostic infrastructure. This study aims to develop a computer-assisted method for characterizing and classifying malaria parasites using a machine learning approach based on light microscopic images of peripheral blood smears. One of the major challenges in malaria diagnostics is the inadequacy of current detection methods. To address this, the study introduces a convolutional neural network (CNN)-based pipeline for the automated detection and staging of malaria infections from Giemsa-stained blood smear images. The dataset used in this study was annotated into four classes: Ring Form, Trophozoite, Schizont, and Uninfected Red Blood Cells (RBCs), encompassing diverse staining qualities and morphological variations. The dataset was divided into training (70%), validation (15%), and testing (15%) subsets. The CNN achieved an overall classification accuracy of 92.4%, with precision, recall, and F1-scores exceeding 0.90 across all classes. Statistical metrics, including mean accuracy (92.4% ± 2.1%), precision (93.1% ± 1.8%), and recall (92.8% ± 1.9%), demonstrated the robustness of the model. Class-specific analysis revealed that the Schizont stage achieved the highest classification accuracy (94.7%), while the Ring Form stage showed slightly lower performance (91.2%), likely due to inherent morphological overlaps with early Trophozoite forms. Visualizations, including confusion matrices and class probability distribution overlays, provided detailed insights into the model’s decision-making processes. The pipeline was further evaluated using cross-validation techniques, showing high reliability across various dataset splits. This approach offers scalability and adaptability, with the potential for deployment in real-world diagnostic workflows, particularly in resource-constrained settings.
Can natural disasters and financial technology (FINTECH) facilitate trade in low-carbon energy source technologies? An empirical evaluation from OECD economies
(Wiley, 2025) Beşer, Nazife Özge; Çabaş, Meral; Brika, Said Khalfa; Barut, Abdulkadir; Sharabati, Abdel-Aziz Ahmad
Countries around the world are taking various measures to combat climate change. Low-carbon energy technology development can significantly assist in addressing this issue. However, due to having fewer resources, not all countries can produce low-carbon energy and have to rely on the trade of low-carbon energy resources. The purpose of this study is to investigate the determinants of low-carbon energy technology trade (LOWT) in 36 Organisation for Economic Cooperation and Development (OECD) countries between 2010 and 2020, and to examine the role of financial technology (FinTech) in this trade in particular. To this end, control variables such as natural disasters, per capita income, population, and institutional quality were also included in the model. Three different methods were used in the empirical analysis: System Generalised Method of Moments (System GMM), machine learning, and moment-based quantile regression panel method (MMQREG). These methods allow for the examination of both dynamic dependence and heterogeneous effects at the quantile level. The findings of the study show that the adoption of financial technology increases trade in low-carbon energy sources in OECD countries. The effects of other variables, such as natural disasters, were also found to be positive for participation in low-carbon energy trade, while institutional quality, income, and population improve this trade. However, according to the MMQREG estimate, population and institutional quality are not significant in higher percentiles. In light of these results, policymakers are advised to support FinTech-based green finance instruments and reduce trade barriers to low-carbon technologies. This approach is consistent with the Paris Climate Agreement and the United Nations Sustainable Development Goals (SDG) (particularly SDG 7: Affordable and Clean Energy, SDG 16: Peace, Justice, and Strong Institutions).
Nanotechnology: a smart approach for sustainable agriculture under global climate change
(Springer, 2025) Altaf, Muhammad Tanveer; Shaheryar, Muhammad; Hayat, Hafiz Saqib; Liaqat, Waqas; Jamil, Amna; Hayat, Momna; Baloch, Faheem Shehzad
Agriculture is the cornerstone of economic development for many nations and is indispensable for global food security. However, the sector faces exceptional challenges exacerbated by climate change, which disrupts ecosystems and threatens food production. It has been proposed that climate change may decrease global crop yields by an average of 8% by the middle of the century. As the world’s population continues to grow, the demand for food is projected to increase significantly by 50% by 2050. To address these challenges and ensure global food security, there is an urgent need to adopt innovative agricultural technologies. This chapter investigates the potential of nanotechnology to revolutionize agricultural practices and mitigate the adverse impacts of climate change. Nanotechnology offers a range of solutions tailored to enhance crop productivity, optimize resource utilization, and promote environmental sustainability. Nano-enabled fertilizers and pesticides hold promise in delivering nutrients effectively to plants while minimizing environmental damage. Furthermore, nanosensors provide real-time monitoring of agro-climatic conditions, enabling precise resource management. Despite its potential benefits, the integration of nanotechnology into agriculture requires careful consideration of environmental and health implications. Risks associated with nanomaterials must be thoroughly assessed to ensure responsible implementation. However, nanotechnology offers a unique chance to transform agricultural sustainability and tackle the issues presented by climate change. Through persistent research, innovation, and responsible integration, nanotechnology can lead to a resilient and sustainable agricultural future, ensuring food security for future generations.
